Repository: probabilisticai/probai-2022 Branch: main Commit: 7e64c1316e9f Files: 12 Total size: 28.9 MB Directory structure: gitextract_qz6qchcq/ ├── README.md ├── day_1/ │ └── 1_antonio/ │ └── probAI2022.ipynb ├── day_3/ │ └── 3_didrik/ │ ├── bnn.ipynb │ ├── bnn_solution.ipynb │ ├── realnvp.ipynb │ └── realnvp_solution.ipynb ├── day_4/ │ ├── 4_cagatay/ │ │ └── ODE2VAE.ipynb │ └── 4_henri/ │ └── elfitutorialprobAI2022.ipynb └── day_5/ └── 5_yingzhen/ ├── classification.ipynb ├── classification_solution.ipynb ├── regression.ipynb └── regression_solution.ipynb ================================================ FILE CONTENTS ================================================ ================================================ FILE: README.md ================================================ # ProbAI 2022 Materials of the Nordic Probabilistic AI School ([ProbAI](https://www.probabilistic.ai)) 2022. ## Lectures and Tutorials * Day 1 (June 13): - [[recording](https://youtu.be/qQsaJH-VRbM), [slides](day_1/1_luigi/day1-probai2022-luigi.pdf)] Luigi Acerbi - Opening of ProbAI 2022 - [[recording](https://youtu.be/i59_eqiw0K8), [slides](day_1/1_arto/IntroLectureKlami.pdf)] Arto Klami - Probabilistic AI: What is it? - [[recording](https://youtu.be/2faAkcS8R0c), [slides](day_1/1_antonio/inference-probai.pdf), [materials](day_1/1_antonio)] Antonio Salmerón - Introduction to Probabilistic Models - [[recording](https://youtu.be/X5-5ykQsuWQ), [materials and slides](https://github.com/PGM-Lab/2022-ProbAI)] Andrés R. Masegosa & Thomas D. Nielsen - Introduction to Probabilistic Programming - [[recording](https://youtu.be/-GRdaXTRCko), [slides](day_1/1_elizaveta/elizaveta_probai2022.pdf), [materials](https://github.com/elizavetasemenova/ProbAI-2022)] Elizaveta Semenova - Bayesian Workflow * Day 2 (June 14): - [[recording](https://youtu.be/bvhCkDmWVQw), [materials](https://github.com/PGM-Lab/2022-ProbAI)] Andrés R. Masegosa, Helge Langseth & Thomas D. Nielsen - Variational Inference 1 - [[recording](https://youtu.be/5jyVatgCzr0), [materials](https://github.com/PGM-Lab/2022-ProbAI)] Andrés R. Masegosa, Helge Langseth & Thomas D. Nielsen - Variational Inference 2 - [~~recording~~, [materials](https://github.com/PGM-Lab/2022-ProbAI)] Andrés R. Masegosa, Helge Langseth & Thomas D. Nielsen - Variational Inference 3 * Day 3 (June 15): - [[recording](https://youtu.be/wOxDHVVurIg), [slides](day_3/3_rianne/Deep%20Generative%20Models_helsinki_15_6-2022_cut.pdf)] Rianne van den Berg - Deep Generative Models - [[recording](https://youtu.be/bu9WZ0RFG0U), [slides](day_3/3_didrik/nf_slides.pdf)], [[RealVNP task](day_3/3_didrik/realnvp.ipynb), [solution](day_3/3_didrik/realnvp_solution.ipynb)], [[BNNs task](day_3/3_didrik/bnn.ipynb), [solution](day_3/3_didrik/bnn_solution.ipynb)], [[Colab - RealVNP](https://colab.research.google.com/github/probabilisticai/probai-2022/blob/main/day_3/3_didrik/realnvp.ipynb)] Didrik Nielsen - Normalizing Flows - [[recording](https://youtu.be/_80NmKf7H0k), [slides](day_3/3_arno/gps.pdf)] Arno Solin - Gaussian Processes * Day 4 (June 16): - [[recording](https://youtu.be/oh2X89rmMdQ), [materials](https://onedrive.live.com/?authkey=%21AJSvUQkSLNITlrU&id=9D9AFECB41FCA080%21271190&cid=9D9AFECB41FCA080), [notebook](day_4/4_cagatay/ODE2VAE.ipynb), [colab](https://drive.google.com/file/d/1PjwNLeLCUcam9BjeddwDRDSyTIdX2h6o/view?usp=sharing)] - Cagatay Yildiz - Neural ODEs - [[recording](https://youtu.be/un7t9UIavRU), [slides](https://hpesonen.github.io/assets/presentations/pesonen_probAI2022_slides/index.html#0), [slides in pdf](day_4/4_henri/pesonen_probAI2022.pdf), [colab](https://colab.research.google.com/drive/1Dg9FZe07DJdGw5tZI5PIxNuAuszULsNP?usp=sharing), [notebook](day_4/4_henri/elfitutorialprobAI2022.ipynb)] - Henri Eerikki Pesonen - ELFI-tutorial - [[recording](https://youtu.be/iDv1REzbRzg)] - Fani Deligianni - Human-Centric ML * Day 5 (June 17): - [[recording](https://youtu.be/cRzNWVjnD6I), [slides](day_5/5_yingzhen/ProbAI2022_vi_bnn_tutorial.pdf), [notes](day_5/5_yingzhen/notes.pdf)], [regression task: [colab](https://colab.research.google.com/drive/1jvJE-P0zye95ICSS5bG2qL35lpr63zxm?usp=sharing) / [notebook](day_5/5_yingzhen/regression.ipynb) / [solution](day_5/5_yingzhen/regression_solution.ipynb)], [classification task: [colab](https://colab.research.google.com/drive/1d-7S7bsu8XE7rYikg5wuRkHyHZo8Pn8v?usp=sharing) / [notebook](day_5/5_yingzhen/classification.ipynb) / [solution](day_5/5_yingzhen/classification_solution.ipynb)] Yingzhen Li - Bayesian Neural Networks - Hands-on tutorial - [[recording](https://youtu.be/MoJVCc9QDf8), [slides](day_5/5_jose/slides_helsinki.pdf)] José Miguel Hernández–Lobato - Advanced Bayesian Neural Networks - [[recording](https://youtu.be/L13EYwDec4w)] Luigi Acerbi - Closing of ProbAI 2022 ================================================ FILE: day_1/1_antonio/probAI2022.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "probAI2022.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "code", "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import math\n", "import scipy.stats as stats\n", "from sklearn.linear_model import LinearRegression\n" ], "metadata": { "id": "crndOuUQMEUi" }, "execution_count": 1, "outputs": [] }, { "cell_type": "markdown", "source": [ "# **Can simple linear regression be considered a ML method?**" ], "metadata": { "id": "893lv1PipFcG" } }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "_xJElH11Ko1j" }, "outputs": [], "source": [ "t = [2,4,6,10,100,500,1000]\n", "rmse = [0]*len(t) \n", "\n", "x = np.random.uniform(-3,3,1000)\n", "\n", "y = 2*x + 3 + np.random.normal(0,2, 1000)\n" ] }, { "cell_type": "code", "source": [ "\n", "plt.figure(figsize=(15,10))\n", "plt.scatter(x,y, s=5, color=\"grey\")\n", "\n", "co = [\"black\",\"red\",\"green\",\"blue\",\"yellow\",\"cyan\",\"brown\"]\n", "\n", "for i in range(0, len(t)):\n", "\n", "\n", "\n", " x_partial, y_partial = x[:t[i]].reshape(1, -1) ,y[:t[i]].reshape(1, -1)\n", "\n", "\n", "\n", " m = LinearRegression().fit(x_partial.T, y_partial.T)\n", "\n", " b = m.coef_[0][0] \n", " a = m.intercept_[0]\n", "\n", " y_estimate = a + b * x\n", " rmse[i] = math.sqrt(np.mean((y - y_estimate)**2))\n", "\n", " f = lambda xi : a + b*xi\n", " if ((i==0) or (i==(len(t)-1))):\n", " plt.plot([-3,3],[f(-3), f(3)], c = co[i],linewidth=5)\n", " else:\n", " plt.plot([-3,3],[f(-3), f(3)], c = co[i])\n", "\n", "\n", "plt.show()\n", "\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 592 }, "id": "uSdU_MeELNf2", "outputId": "c99e04e7-748b-4d9d-e17e-deed929bad14" }, "execution_count": 4, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "rmse" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eLR8MmLjOx1G", "outputId": "26ad82db-a0ff-47ae-e340-dd4d81cd2ac3" }, "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[2.2659173638043635,\n", " 1.9897077447865485,\n", " 2.0499444428269564,\n", " 2.16823631919792,\n", " 1.955510871571366,\n", " 1.950981441525572,\n", " 1.950742126415348]" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "source": [ "i=range(0,len(t))\n", "plt.figure(figsize=(15,10))\n", "plt.plot(i,rmse)\n", "plt.show()" ], "metadata": { "id": "B13O6dAAPBOp", "colab": { "base_uri": "https://localhost:8080/", "height": 592 }, "outputId": "65c85ede-a2b7-4a61-d0b0-815389e500af" }, "execution_count": 6, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "# **Conjugate analysis for a Beta-Binomial model**" ], "metadata": { "id": "vlkxfopGo7Bv" } }, { "cell_type": "code", "source": [ "data = np.random.binomial(1,0.4,1000)\n", "x = np.linspace(0,1, 1000)\n", "plt.figure(figsize=(15,10))\n", "plt.plot(x, stats.beta.pdf(x,1,1))\n", "\n", "n=5\n", "su = sum(data[1:n])\n", "plt.plot(x, stats.beta.pdf(x,1+su,1+n-su),c=\"red\")\n", "\n", "n=10\n", "su = sum(data[1:n])\n", "plt.plot(x, stats.beta.pdf(x,1+su,1+n-su),c=\"blue\")\n", "\n", "n=50\n", "su = sum(data[1:n])\n", "plt.plot(x, stats.beta.pdf(x,1+su,1+n-su),c=\"green\")\n", "\n", "n=100\n", "su = sum(data[1:n])\n", "plt.plot(x, stats.beta.pdf(x,1+su,1+n-su),c=\"purple\")\n", "\n", "n=1000\n", "su = sum(data[1:n])\n", "plt.plot(x, stats.beta.pdf(x,1+su,1+n-su),c=\"brown\")\n", "\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 592 }, "id": "WkwnTPz46Pes", "outputId": "1b398074-410f-46b9-e2d1-896dad29c03c" }, "execution_count": 7, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "# **Bayesian linear regression**" ], "metadata": { "id": "m1-iDOkE-jD_" } }, { "cell_type": "code", "source": [ "N = 5000\n", "x = np.random.uniform(0,3,N)\n", "y = 3*x + 1 + np.random.normal(0,1,N)" ], "metadata": { "id": "HiubYUtv-m2x" }, "execution_count": 32, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now we define the prior on w0 to be a N(a0,b0), initially N(0,1)" ], "metadata": { "id": "RBt-0eCX_j8z" } }, { "cell_type": "code", "source": [ "a0 = 0\n", "b0 = 1" ], "metadata": { "id": "01UzwIAt_rx0" }, "execution_count": 33, "outputs": [] }, { "cell_type": "markdown", "source": [ "The same for w1" ], "metadata": { "id": "FrjUa0pX_36N" } }, { "cell_type": "code", "source": [ "a1 = 0\n", "b1 = 1" ], "metadata": { "id": "EC7NoG4u_55C" }, "execution_count": 34, "outputs": [] }, { "cell_type": "markdown", "source": [ "Sample size for each iteration: K" ], "metadata": { "id": "UWX9T-Ig__-3" } }, { "cell_type": "code", "source": [ "K = 1000\n", "for i in range(0,N):\n", " s_w0 = np.random.normal(a0,b0,K) \n", " s_w1 = np.random.normal(a1,b1,K)\n", " s_epsilon = np.random.normal(0,1,K)\n", " var_yi = b0**2+b1**2*x[i]**2+1\n", " \n", " p = stats.norm.pdf(s_w0,a0,b0) * stats.norm.pdf(s_w1,a1,b1) * stats.norm.pdf(y[i],s_w0+s_w1*x[i]+s_epsilon,math.sqrt(var_yi))\n", " p_star = stats.norm.pdf(s_w0,a0,b0) * stats.norm.pdf(s_w1,a1,b1)\n", " weights = p / p_star\n", " sum_weights = sum(weights)\n", " \n", " est_a0 = 1/sum_weights*sum(weights*s_w0)\n", " est_b0 = 1/sum_weights*sum(weights*s_w0**2)\n", " a0 = est_a0\n", " b0 = math.sqrt(est_b0-est_a0**2)\n", " \n", " est_a1 = 1/sum_weights*sum(weights*s_w1)\n", " est_b1 = 1/sum_weights*sum(weights*s_w1**2)\n", " a1 = est_a1\n", " b1 = math.sqrt(est_b1-est_a1**2)\n" ], "metadata": { "id": "ybOYwRuZACYz" }, "execution_count": 35, "outputs": [] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(15,10))\n", "plt.scatter(x,y, s=5, color=\"grey\")\n", "f_exact = lambda xi : 1 + 3*xi\n", "f_estimated = lambda xi : a0 + a1*xi\n", "plt.plot([0,3],[f_exact(0), f_exact(3)], c = \"blue\")\n", "plt.plot([0,3],[f_estimated(0), f_estimated(3)], c = \"red\")\n", "m = LinearRegression().fit(x.reshape(1,-1).T,y.reshape(1,-1).T)\n", "\n", "b = m.coef_[0][0] \n", "a = m.intercept_[0]\n", "f_lr = lambda xi : a + b*xi\n", "plt.plot([0,3],[f_lr(0), f_lr(3)], c = \"green\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 592 }, "id": "woPwIICWlptb", "outputId": "49d15cf9-fb3a-41cc-a5d3-bfccbf3c92e7" }, "execution_count": 31, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] } ] } ================================================ FILE: day_3/3_didrik/bnn.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "_k6vts8hjMih" }, "source": [ "## Tasks\n", "We will implement a Bayesian Neural Network (BNN) and fit it to toy data.\n", "\n", "#### Todo:\n", "* Implement the `VariationalLinear` class (in **Create Layers** section)\n", "* Implement the `VariationalFlow` class (in **Create Flow Layers** section)\n", "* Train mean-field & flow-based BNNs and compare\n", "* **Bonus:** Study the effect of some parameter (e.g. flow architecture, NN architecture, initialization, etc.) and improve performance." ] }, { "cell_type": "markdown", "metadata": { "id": "G28jER-pjMil" }, "source": [ "## Create Dataset" ] }, { "cell_type": "code", "source": [ "!pip install einops\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from torch.distributions import Normal\n", "from torch.utils.data import Dataset, DataLoader\n", "from einops import rearrange, reduce, repeat\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "\n", "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "\n", "def plot_preds(dataset, model=None, title=None):\n", " fig = go.Figure()\n", " fig.update_layout(title=title, title_x=0.5)\n", " fig.add_trace(go.Scatter(x=dataset.x[:,0], y=dataset.y[:,0], mode='markers', name='data'))\n", " if model:\n", " x = torch.linspace(-5,5,steps=200).to(device)\n", " ys = [model(x.unsqueeze(-1))[0].detach()[:,0] for _ in range(10)]\n", " for y in ys: fig.add_trace(go.Scatter(x=x, y=y, mode='lines', name='pred', marker = {'color' : '#EF553B'}))\n", " return fig" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nwL6k5ngjWom", "outputId": "8438fd74-6a79-4179-db53-56e122c08a14" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Requirement already satisfied: einops in /usr/local/lib/python3.7/dist-packages (0.4.1)\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "id": "2_fpR-TFjMil", "outputId": "625ca6ed-d35e-4f05-90a7-f4e21db57221" }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ], "source": [ "class ToyData(Dataset):\n", " def __init__(self, num, sigma=0.5):\n", " self.x = 8 * torch.rand(num, 1) - 4\n", " self.y = 0.1 * self.x**3 - self.x + sigma * torch.randn(num, 1)\n", " self.num = num\n", " self.sigma = sigma\n", "\n", " def __getitem__(self, i):\n", " return self.x[i], self.y[i]\n", "\n", " def __len__(self):\n", " return len(self.x)\n", "\n", "torch.manual_seed(42)\n", "dataset = ToyData(128)\n", "testset = ToyData(1024)\n", "train_loader = DataLoader(dataset, batch_size=32, shuffle=True)\n", "plot_preds(dataset)" ] }, { "cell_type": "markdown", "metadata": { "id": "12vNrsrjjMin" }, "source": [ "## Create Layers" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NJ-u_AKQjMin" }, "outputs": [], "source": [ "class StandardGaussian(nn.Module):\n", "\n", " def __init__(self, dim):\n", " super().__init__()\n", " self.register_buffer('loc', torch.zeros(dim))\n", " self.register_buffer('scale', torch.ones(dim))\n", "\n", " def log_prob(self, x):\n", " return Normal(self.loc, self.scale).log_prob(x).sum(1)\n", "\n", " def sample(self, num_samples):\n", " return Normal(self.loc, self.scale).sample((num_samples,))\n", "\n", " def sample_with_log_prob(self, num_samples):\n", " x = self.sample(num_samples)\n", " return x, self.log_prob(x)\n", "\n", "\n", "class Gaussian(nn.Module):\n", "\n", " def __init__(self, dim, mean_std_init=0.05, std_init=0.05):\n", " super().__init__()\n", " self.loc = nn.Parameter(mean_std_init*torch.randn(dim))\n", " self.log_scale = nn.Parameter(np.log(std_init)*torch.ones(dim))\n", "\n", " def sample_with_log_prob(self, num_samples):\n", " d = Normal(self.loc, self.log_scale.exp())\n", " x = d.rsample((num_samples,))\n", " return x, d.log_prob(x).sum(1)" ] }, { "cell_type": "code", "source": [ "def split_wb(wb, i, o):\n", " w, b = torch.split_with_sizes(wb, (i*o, o))\n", " return w.reshape(o, i), b\n", "\n", "\n", "class VariationalLinear(nn.Module):\n", "\n", " def __init__(self, q, p, dim_in, dim_out):\n", " super().__init__()\n", " self.q = q # Variational distribution\n", " self.p = p # Prior distribution\n", " self.dim_in = dim_in\n", " self.dim_out = dim_out\n", "\n", " def forward(self, x):\n", " '''TODO: Implement w, b & kl'''\n", " '''Hint: Use p, q & the split_wb function above'''\n", " return F.linear(x, w, b), kl\n", "\n", " \n", "# Test VariationalLinear\n", "m = VariationalLinear(Gaussian(10*5+5), StandardGaussian(10*5+5), 10, 5)\n", "m(torch.randn(1, 10))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YXSQF1rk9V6W", "outputId": "6c2075c1-6d91-4944-b8d4-f74ad65ffd81" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(tensor([[ 0.4509, 0.6837, 2.1835, -4.6944, 7.5346]]), tensor([0.]))" ] }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "markdown", "source": [ "## Create Flow Layers" ], "metadata": { "id": "nOzJEru9luGA" } }, { "cell_type": "code", "source": [ "def build_mask(in_dim, out_dim, ar_dim, causal=False):\n", " assert in_dim % ar_dim == 0; assert out_dim % ar_dim == 0\n", " base = torch.ones(ar_dim, ar_dim)\n", " base = base.tril(-1) if causal else base.tril(0)\n", " return repeat(base, 'h w -> (o h) (i w)', i=in_dim // ar_dim, o=out_dim // ar_dim)\n", "\n", "\n", "class MaskedLinear(nn.Module):\n", "\n", " def __init__(self, in_dim, out_dim, ar_dim, causal=False):\n", " super().__init__()\n", " self.register_buffer('mask', build_mask(in_dim, out_dim, ar_dim, causal))\n", " self.weight = nn.Parameter(torch.empty(out_dim, in_dim))\n", " self.bias = nn.Parameter(torch.empty(out_dim))\n", " self.reset_parameters()\n", "\n", " def reset_parameters(self):\n", " nn.init.kaiming_normal_(self.weight)\n", " nn.init.zeros_(self.bias)\n", "\n", " def forward(self, x):\n", " return F.linear(x, self.weight * self.mask, self.bias)\n", "\n", "\n", "def make_net(dim): \n", " return nn.Sequential(\n", " MaskedLinear(dim, dim*4, ar_dim=dim), nn.GELU(),\n", " MaskedLinear(dim*4, dim*4, ar_dim=dim), nn.GELU(),\n", " MaskedLinear(dim*4, dim*2, ar_dim=dim, causal=True),\n", " )" ], "metadata": { "id": "O8Em_3mDltEt" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "class Reverse(nn.Module):\n", "\n", " def forward(self, x):\n", " return x.flip(-1), x.new_zeros(x.shape[0])\n", "\n", " def inverse(self, z):\n", " return z.flip(-1)\n", "\n", "\n", "class IAF(nn.Module):\n", "\n", " def __init__(self, ar_net):\n", " super().__init__()\n", " self.ar_net = ar_net\n", "\n", " def forward(self, x):\n", " p = self.ar_net(x)\n", " m, u = torch.chunk(p, 2, dim=1)\n", " u = u + 1.0 # Improve initialization\n", " s, log_s = torch.sigmoid(u), F.logsigmoid(u)\n", " z = s * x + (1-s) * m\n", " ldj = log_s.sum(1)\n", " return z, ldj\n", "\n", "\n", "class VariationalFlow(nn.Module):\n", "\n", " def __init__(self, base_dist, bijections):\n", " super().__init__()\n", " self.base_dist = base_dist\n", " self.bijections = nn.ModuleList(bijections)\n", "\n", " def sample_with_log_prob(self, num_samples):\n", " '''TODO: Implement z, log_prob'''\n", " '''Hint: Sample from base_dist, loop through bijections'''\n", " return z, log_prob\n", "\n", "\n", "def make_flow(dim):\n", " return VariationalFlow(\n", " base_dist=StandardGaussian(dim=dim),\n", " bijections=[\n", " IAF(make_net(dim)), Reverse(),\n", " IAF(make_net(dim)), Reverse(),\n", " IAF(make_net(dim)), Reverse(),\n", " IAF(make_net(dim)),\n", " ],\n", " )\n", "\n", "# Test VariationalFlow\n", "flow = make_flow(5)\n", "flow.sample_with_log_prob(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "H_0qK_Z6l0r2", "outputId": "1dcecb9d-fc2f-4f1f-93b4-c8580f48b23f" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(tensor([[ 0.4556, 0.0111, 0.0035, 0.2923, -0.1062],\n", " [-0.5595, 0.1934, -0.0331, -0.0753, 0.4863]], grad_fn=),\n", " tensor([-0.1170, -0.5190], grad_fn=))" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "markdown", "source": [ "## Create BNN" ], "metadata": { "id": "vRQn8lVb5Y20" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UxAovOxfjMiq" }, "outputs": [], "source": [ "class BNN(nn.Module):\n", "\n", " def __init__(self, I, O, H, scale, num, mean_field=True):\n", " super().__init__()\n", " D = lambda i,o: i*o+o\n", " q = lambda i,o: Gaussian(D(i,o), mean_std_init=1/np.sqrt(i)) if mean_field else make_flow(D(i,o))\n", " self.layer0 = VariationalLinear(q(I,H), StandardGaussian(D(I,H)), I, H)\n", " self.layer1 = VariationalLinear(q(H,O), StandardGaussian(D(H,O)), H, O)\n", " self.scale = scale\n", " self.num = num\n", "\n", " def forward(self, x):\n", " h, kl0 = self.layer0(x)\n", " h = F.gelu(h)\n", " h, kl1 = self.layer1(h)\n", " return h, kl0+kl1\n", "\n", " def elbo(self, x, y):\n", " yhat, kl = self(x)\n", " return Normal(yhat, self.scale).log_prob(y).mean() - kl/self.num\n", "\n", "get_model = lambda mean_field: BNN(1, 1, 32, scale=dataset.sigma, num=dataset.num, mean_field=mean_field).to(device)" ] }, { "cell_type": "code", "source": [ "def train(model, epochs=1000):\n", " optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n", "\n", " print('Training...')\n", " model = model.train()\n", " elbos = []\n", " for epoch in range(epochs):\n", " loss_sum = 0.0\n", " for i, (x,y) in enumerate(train_loader):\n", " x, y = x.to(device), y.to(device)\n", " optimizer.zero_grad()\n", " loss = -model.elbo(x, y)\n", " loss.backward()\n", " optimizer.step()\n", " loss_sum += loss.detach().cpu().item()\n", " elbos.append(loss_sum/len(train_loader))\n", " if (epoch+1) % 100 == 0:\n", " print('Epoch: {}/{}, Loss: {:.3f}'.format(epoch+1, epochs, elbos[-1]))\n", " model = model.eval()\n", " return model, elbos" ], "metadata": { "id": "YcrtU857x6xK" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Train Mean-Field BNN" ], "metadata": { "id": "X533DKq5pBC9" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 737 }, "id": "vIadGCejjMir", "outputId": "da0e2729-e08a-43e7-9386-a3d7d90b91d8" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Training...\n", "Epoch: 100/1000, Loss: 4.521\n", "Epoch: 200/1000, Loss: 3.637\n", "Epoch: 300/1000, Loss: 3.533\n", "Epoch: 400/1000, Loss: 3.192\n", "Epoch: 500/1000, Loss: 2.846\n", "Epoch: 600/1000, Loss: 2.807\n", "Epoch: 700/1000, Loss: 2.817\n", "Epoch: 800/1000, Loss: 2.699\n", "Epoch: 900/1000, Loss: 2.565\n", "Epoch: 1000/1000, Loss: 2.480\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ], "source": [ "torch.manual_seed(42)\n", "model_mf, elbos_mf = train(get_model(True))\n", "plot_preds(dataset, model_mf, title='Mean-Field BNN')" ] }, { "cell_type": "markdown", "source": [ "## Train Flow-Based BNN" ], "metadata": { "id": "buOaaNOOpdut" } }, { "cell_type": "code", "source": [ "torch.manual_seed(42)\n", "model_flow, elbos_flow = train(get_model(False))\n", "plot_preds(dataset, model_flow, title='Flow-Based BNN')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 737 }, "id": "AqyKbTRGpjta", "outputId": "6f4877a4-4714-4832-e6dd-681b67dca797" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Training...\n", "Epoch: 100/1000, Loss: 4.943\n", "Epoch: 200/1000, Loss: 3.913\n", "Epoch: 300/1000, Loss: 3.706\n", "Epoch: 400/1000, Loss: 2.041\n", "Epoch: 500/1000, Loss: 2.156\n", "Epoch: 600/1000, Loss: 1.848\n", "Epoch: 700/1000, Loss: 2.123\n", "Epoch: 800/1000, Loss: 1.870\n", "Epoch: 900/1000, Loss: 1.732\n", "Epoch: 1000/1000, Loss: 1.984\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "## Compare Models" ], "metadata": { "id": "taCLBdHtrI58" } }, { "cell_type": "code", "source": [ "df = pd.DataFrame({'mean_field':elbos_mf, 'flow':elbos_flow})\n", "px.line(df, y=['mean_field','flow'])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "id": "HwcVa3PMxF2C", "outputId": "7776be4a-9257-4b31-b4c2-6a1d14421039" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "print(f'Mean-Field: Avg. loss over last 100 epochs: {np.mean(elbos_mf[-100:]):.4f}')\n", "print(f'Flow: Avg. loss over last 100 epochs: {np.mean(elbos_flow[-100:]):.4f}')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ibg78b0Dq51z", "outputId": "cb4af894-c7fd-4a3e-fedf-45cdfdf2c8ff" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mean-Field: Avg. loss over last 100 epochs: 2.5634\n", "Flow: Avg. loss over last 100 epochs: 1.8884\n" ] } ] }, { "cell_type": "code", "source": [ "test_mse_mf = [F.mse_loss(model_mf(testset.x)[0], testset.y).detach().item() for _ in range(100)]\n", "test_mse_flow = [F.mse_loss(model_flow(testset.x)[0], testset.y).detach().item() for _ in range(100)]\n", "\n", "print(f'Mean-Field: Test MSE: {np.mean(test_mse_mf):.4f}')\n", "print(f'Flow: Test MSE: {np.mean(test_mse_flow):.4f}')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Og4bxjLQssnw", "outputId": "7c2fb189-5324-4362-fba8-73881f3df0c6" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mean-Field: Test MSE: 0.4711\n", "Flow: Test MSE: 0.4392\n" ] } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "lvcji7EdCCcd" }, "execution_count": null, "outputs": [] } ], "metadata": { "interpreter": { "hash": "c06e93ed1e8956668d79d6c20a61b551c29e6e73b8ed125dfe0605ed63fa74bc" }, "kernelspec": { "display_name": "Python 3.9.5 ('base')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.5" }, "orig_nbformat": 4, "colab": { "name": "bnn.ipynb", "provenance": [], "collapsed_sections": [ "G28jER-pjMil", "12vNrsrjjMin", "nOzJEru9luGA", "vRQn8lVb5Y20", "X533DKq5pBC9", "buOaaNOOpdut", "taCLBdHtrI58" ] } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: day_3/3_didrik/bnn_solution.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "_k6vts8hjMih" }, "source": [ "## Tasks\n", "We will implement a Bayesian Neural Network (BNN) and fit it to toy data.\n", "\n", "#### Todo:\n", "* Implement the `VariationalLinear` class (in **Create Layers** section)\n", "* Implement the `VariationalFlow` class (in **Create Flow Layers** section)\n", "* Train mean-field & flow-based BNNs and compare\n", "* **Bonus:** Study the effect of some parameter (e.g. flow architecture, NN architecture, initialization, etc.) and improve performance." ] }, { "cell_type": "markdown", "metadata": { "id": "G28jER-pjMil" }, "source": [ "## Create Dataset" ] }, { "cell_type": "code", "source": [ "!pip install einops\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "from torch.distributions import Normal\n", "from torch.utils.data import Dataset, DataLoader\n", "from einops import rearrange, reduce, repeat\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "\n", "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "\n", "def plot_preds(dataset, model=None, title=None):\n", " fig = go.Figure()\n", " fig.update_layout(title=title, title_x=0.5)\n", " fig.add_trace(go.Scatter(x=dataset.x[:,0], y=dataset.y[:,0], mode='markers', name='data'))\n", " if model:\n", " x = torch.linspace(-5,5,steps=200).to(device)\n", " ys = [model(x.unsqueeze(-1))[0].detach()[:,0] for _ in range(10)]\n", " for y in ys: fig.add_trace(go.Scatter(x=x, y=y, mode='lines', name='pred', marker = {'color' : '#EF553B'}))\n", " return fig" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nwL6k5ngjWom", "outputId": "07afa018-42db-44f5-9d9b-553e9d2a0e4d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Requirement already satisfied: einops in /usr/local/lib/python3.7/dist-packages (0.4.1)\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2_fpR-TFjMil", "outputId": "ef759266-07e1-4276-ea6f-87756763a3b1" }, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ], "source": [ "class ToyData(Dataset):\n", " def __init__(self, num, sigma=0.5):\n", " self.x = 8 * torch.rand(num, 1) - 4\n", " self.y = 0.1 * self.x**3 - self.x + sigma * torch.randn(num, 1)\n", " self.num = num\n", " self.sigma = sigma\n", "\n", " def __getitem__(self, i):\n", " return self.x[i], self.y[i]\n", "\n", " def __len__(self):\n", " return len(self.x)\n", "\n", "torch.manual_seed(42)\n", "dataset = ToyData(128)\n", "testset = ToyData(1024)\n", "train_loader = DataLoader(dataset, batch_size=32, shuffle=True)\n", "plot_preds(dataset)" ] }, { "cell_type": "markdown", "metadata": { "id": "12vNrsrjjMin" }, "source": [ "## Create Layers" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NJ-u_AKQjMin" }, "outputs": [], "source": [ "class StandardGaussian(nn.Module):\n", "\n", " def __init__(self, dim):\n", " super().__init__()\n", " self.register_buffer('loc', torch.zeros(dim))\n", " self.register_buffer('scale', torch.ones(dim))\n", "\n", " def log_prob(self, x):\n", " return Normal(self.loc, self.scale).log_prob(x).sum(1)\n", "\n", " def sample(self, num_samples):\n", " return Normal(self.loc, self.scale).sample((num_samples,))\n", "\n", " def sample_with_log_prob(self, num_samples):\n", " x = self.sample(num_samples)\n", " return x, self.log_prob(x)\n", "\n", "\n", "class Gaussian(nn.Module):\n", "\n", " def __init__(self, dim, mean_std_init=0.05, std_init=0.05):\n", " super().__init__()\n", " self.loc = nn.Parameter(mean_std_init*torch.randn(dim))\n", " self.log_scale = nn.Parameter(np.log(std_init)*torch.ones(dim))\n", "\n", " def sample_with_log_prob(self, num_samples):\n", " d = Normal(self.loc, self.log_scale.exp())\n", " x = d.rsample((num_samples,))\n", " return x, d.log_prob(x).sum(1)" ] }, { "cell_type": "code", "source": [ "def split_wb(wb, i, o):\n", " w, b = torch.split_with_sizes(wb, (i*o, o))\n", " return w.reshape(o, i), b\n", "\n", "\n", "class VariationalLinear(nn.Module):\n", "\n", " def __init__(self, q, p, dim_in, dim_out):\n", " super().__init__()\n", " self.q = q # Variational distribution\n", " self.p = p # Prior distribution\n", " self.dim_in = dim_in\n", " self.dim_out = dim_out\n", "\n", " def forward(self, x):\n", " wb, log_qwb = self.q.sample_with_log_prob(1)\n", " kl = log_qwb - self.p.log_prob(wb)\n", " w, b = split_wb(wb.squeeze(0), self.dim_in, self.dim_out)\n", " return F.linear(x, w, b), kl\n", "\n", " \n", "# Test VariationalLinear\n", "m = VariationalLinear(Gaussian(10*5+5), StandardGaussian(10*5+5), 10, 5)\n", "m(torch.randn(1, 10))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YXSQF1rk9V6W", "outputId": "8fce37a4-b0e9-4b58-bda2-9448d080a7af" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(tensor([[-0.2881, 0.1345, 0.3139, -0.2588, 0.6795]],\n", " grad_fn=), tensor([137.3688], grad_fn=))" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "markdown", "source": [ "## Create Flow Layers" ], "metadata": { "id": "nOzJEru9luGA" } }, { "cell_type": "code", "source": [ "def build_mask(in_dim, out_dim, ar_dim, causal=False):\n", " assert in_dim % ar_dim == 0; assert out_dim % ar_dim == 0\n", " base = torch.ones(ar_dim, ar_dim)\n", " base = base.tril(-1) if causal else base.tril(0)\n", " return repeat(base, 'h w -> (o h) (i w)', i=in_dim // ar_dim, o=out_dim // ar_dim)\n", "\n", "\n", "class MaskedLinear(nn.Module):\n", "\n", " def __init__(self, in_dim, out_dim, ar_dim, causal=False):\n", " super().__init__()\n", " self.register_buffer('mask', build_mask(in_dim, out_dim, ar_dim, causal))\n", " self.weight = nn.Parameter(torch.empty(out_dim, in_dim))\n", " self.bias = nn.Parameter(torch.empty(out_dim))\n", " self.reset_parameters()\n", "\n", " def reset_parameters(self):\n", " nn.init.kaiming_normal_(self.weight)\n", " nn.init.zeros_(self.bias)\n", "\n", " def forward(self, x):\n", " return F.linear(x, self.weight * self.mask, self.bias)\n", "\n", "\n", "def make_net(dim): \n", " return nn.Sequential(\n", " MaskedLinear(dim, dim*4, ar_dim=dim), nn.GELU(),\n", " MaskedLinear(dim*4, dim*4, ar_dim=dim), nn.GELU(),\n", " MaskedLinear(dim*4, dim*2, ar_dim=dim, causal=True),\n", " )" ], "metadata": { "id": "O8Em_3mDltEt" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "class Reverse(nn.Module):\n", "\n", " def forward(self, x):\n", " return x.flip(-1), x.new_zeros(x.shape[0])\n", "\n", " def inverse(self, z):\n", " return z.flip(-1)\n", "\n", "\n", "class IAF(nn.Module):\n", "\n", " def __init__(self, ar_net):\n", " super().__init__()\n", " self.ar_net = ar_net\n", "\n", " def forward(self, x):\n", " p = self.ar_net(x)\n", " m, u = torch.chunk(p, 2, dim=1)\n", " u = u + 1.0 # Improve initialization\n", " s, log_s = torch.sigmoid(u), F.logsigmoid(u)\n", " z = s * x + (1-s) * m\n", " ldj = log_s.sum(1)\n", " return z, ldj\n", "\n", "\n", "class VariationalFlow(nn.Module):\n", "\n", " def __init__(self, base_dist, bijections):\n", " super().__init__()\n", " self.base_dist = base_dist\n", " self.bijections = nn.ModuleList(bijections)\n", "\n", " def sample_with_log_prob(self, num_samples):\n", " z, log_prob = self.base_dist.sample_with_log_prob(num_samples)\n", " for bijection in self.bijections:\n", " z, ldj = bijection(z)\n", " log_prob -= ldj\n", " return z, log_prob\n", "\n", "\n", "def make_flow(dim):\n", " return VariationalFlow(\n", " base_dist=StandardGaussian(dim=dim),\n", " bijections=[\n", " IAF(make_net(dim)), Reverse(),\n", " IAF(make_net(dim)), Reverse(),\n", " IAF(make_net(dim)), Reverse(),\n", " IAF(make_net(dim)),\n", " ],\n", " )\n", "\n", "# Test VariationalFlow\n", "flow = make_flow(5)\n", "flow.sample_with_log_prob(2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "H_0qK_Z6l0r2", "outputId": "cb066ea3-cb6e-4c35-9a30-b09827f615be" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(tensor([[ 0.0887, -0.1286, 0.3598, -0.1486, -0.0394],\n", " [ 0.0592, 0.1806, -0.2329, -0.0417, -0.1132]], grad_fn=),\n", " tensor([0.4505, 1.0394], grad_fn=))" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "markdown", "source": [ "## Create BNN" ], "metadata": { "id": "vRQn8lVb5Y20" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UxAovOxfjMiq" }, "outputs": [], "source": [ "class BNN(nn.Module):\n", "\n", " def __init__(self, I, O, H, scale, num, mean_field=True):\n", " super().__init__()\n", " D = lambda i,o: i*o+o\n", " q = lambda i,o: Gaussian(D(i,o), mean_std_init=1/np.sqrt(i)) if mean_field else make_flow(D(i,o))\n", " self.layer0 = VariationalLinear(q(I,H), StandardGaussian(D(I,H)), I, H)\n", " self.layer1 = VariationalLinear(q(H,O), StandardGaussian(D(H,O)), H, O)\n", " self.scale = scale\n", " self.num = num\n", "\n", " def forward(self, x):\n", " h, kl0 = self.layer0(x)\n", " h = F.gelu(h)\n", " h, kl1 = self.layer1(h)\n", " return h, kl0+kl1\n", "\n", " def elbo(self, x, y):\n", " yhat, kl = self(x)\n", " return Normal(yhat, self.scale).log_prob(y).mean() - kl/self.num\n", "\n", "get_model = lambda mean_field: BNN(1, 1, 32, scale=dataset.sigma, num=dataset.num, mean_field=mean_field).to(device)" ] }, { "cell_type": "code", "source": [ "def train(model, epochs=1000):\n", " optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n", "\n", " print('Training...')\n", " model = model.train()\n", " elbos = []\n", " for epoch in range(epochs):\n", " loss_sum = 0.0\n", " for i, (x,y) in enumerate(train_loader):\n", " x, y = x.to(device), y.to(device)\n", " optimizer.zero_grad()\n", " loss = -model.elbo(x, y)\n", " loss.backward()\n", " optimizer.step()\n", " loss_sum += loss.detach().cpu().item()\n", " elbos.append(loss_sum/len(train_loader))\n", " if (epoch+1) % 100 == 0:\n", " print('Epoch: {}/{}, Loss: {:.3f}'.format(epoch+1, epochs, elbos[-1]))\n", " model = model.eval()\n", " return model, elbos" ], "metadata": { "id": "YcrtU857x6xK" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Train Mean-Field BNN" ], "metadata": { "id": "X533DKq5pBC9" } }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vIadGCejjMir", "outputId": "c1c34221-1003-4ef0-db4a-d957ef1d642f" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Training...\n", "Epoch: 100/1000, Loss: 3.846\n", "Epoch: 200/1000, Loss: 3.224\n", "Epoch: 300/1000, Loss: 3.468\n", "Epoch: 400/1000, Loss: 3.192\n", "Epoch: 500/1000, Loss: 2.953\n", "Epoch: 600/1000, Loss: 2.868\n", "Epoch: 700/1000, Loss: 2.715\n", "Epoch: 800/1000, Loss: 2.686\n", "Epoch: 900/1000, Loss: 2.816\n", "Epoch: 1000/1000, Loss: 2.597\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ], "source": [ "torch.manual_seed(42)\n", "model_mf, elbos_mf = train(get_model(True))\n", "plot_preds(dataset, model_mf, title='Mean-Field BNN')" ] }, { "cell_type": "markdown", "source": [ "## Train Flow-Based BNN" ], "metadata": { "id": "buOaaNOOpdut" } }, { "cell_type": "code", "source": [ "torch.manual_seed(42)\n", "model_flow, elbos_flow = train(get_model(False))\n", "plot_preds(dataset, model_flow, title='Flow-Based BNN')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AqyKbTRGpjta", "outputId": "1b0c7ad2-fc09-46b8-8201-e003a21158e0" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Training...\n", "Epoch: 100/1000, Loss: 4.131\n", "Epoch: 200/1000, Loss: 3.409\n", "Epoch: 300/1000, Loss: 3.519\n", "Epoch: 400/1000, Loss: 3.165\n", "Epoch: 500/1000, Loss: 2.081\n", "Epoch: 600/1000, Loss: 2.000\n", "Epoch: 700/1000, Loss: 2.063\n", "Epoch: 800/1000, Loss: 2.551\n", "Epoch: 900/1000, Loss: 2.136\n", "Epoch: 1000/1000, Loss: 1.939\n" ] }, { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "## Compare Models" ], "metadata": { "id": "taCLBdHtrI58" } }, { "cell_type": "code", "source": [ "df = pd.DataFrame({'mean_field':elbos_mf, 'flow':elbos_flow})\n", "px.line(df, y=['mean_field','flow'])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 542 }, "id": "HwcVa3PMxF2C", "outputId": "38bae67d-de5e-4c06-d43d-755ce87baa27" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", "\n", "\n", "
\n", "
\n", "\n", "" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "print(f'Mean-Field: Avg. loss over last 100 epochs: {np.mean(elbos_mf[-100:]):.4f}')\n", "print(f'Flow: Avg. loss over last 100 epochs: {np.mean(elbos_flow[-100:]):.4f}')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ibg78b0Dq51z", "outputId": "178f7e0f-90c9-4a87-daa5-d668d543e592" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mean-Field: Avg. loss over last 100 epochs: 2.6132\n", "Flow: Avg. loss over last 100 epochs: 1.9368\n" ] } ] }, { "cell_type": "code", "source": [ "test_mse_mf = [F.mse_loss(model_mf(testset.x)[0], testset.y).detach().item() for _ in range(100)]\n", "test_mse_flow = [F.mse_loss(model_flow(testset.x)[0], testset.y).detach().item() for _ in range(100)]\n", "\n", "print(f'Mean-Field: Test MSE: {np.mean(test_mse_mf):.4f}')\n", "print(f'Flow: Test MSE: {np.mean(test_mse_flow):.4f}')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Og4bxjLQssnw", "outputId": "ac2417a8-0ccf-46bc-df88-615561d76c6d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mean-Field: Test MSE: 0.4393\n", "Flow: Test MSE: 0.3836\n" ] } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "lvcji7EdCCcd" }, "execution_count": null, "outputs": [] } ], "metadata": { "interpreter": { "hash": "c06e93ed1e8956668d79d6c20a61b551c29e6e73b8ed125dfe0605ed63fa74bc" }, "kernelspec": { "display_name": "Python 3.9.5 ('base')", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.5" }, "orig_nbformat": 4, "colab": { "name": "bnn_solution.ipynb", "provenance": [], "collapsed_sections": [ "G28jER-pjMil", "12vNrsrjjMin", "nOzJEru9luGA", "vRQn8lVb5Y20", "X533DKq5pBC9", "buOaaNOOpdut" ] } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: day_3/3_didrik/realnvp.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "realnvp.ipynb", "provenance": [], "collapsed_sections": [ "2vlLPnUXCOme", "2Ze4Y7gFGE-4" ] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "ByIKgJcoLZEE" }, "source": [ "# Tasks\n", "\n", "We will implement a coupling flow like RealNVP/Glow and fit it to 2D toy data.\n", "\n", "### *Todo*:\n", "1. Implement the `Coupling` bijection class\n", "1. Implement the `make_net` function\n", "1. Train flow and sample\n", "1. **Bonus:** Study the effect of some parameter (e.g. flow layers, NN layers, NN dim, NN activation, etc.) and improve performance." ] }, { "cell_type": "markdown", "metadata": { "id": "WX2sR4XXCE4_" }, "source": [ "# Create Dataset" ] }, { "cell_type": "code", "metadata": { "id": "TSYzJZf1-DsI" }, "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.utils.data import Dataset, DataLoader\n", "import numpy as np\n", "\n", "\n", "class TwoSpirals(Dataset):\n", " def __init__(self, num_points):\n", " self.num_points = num_points\n", " self._create_data()\n", "\n", " def __getitem__(self, item):\n", " return self.data[item]\n", "\n", " def __len__(self):\n", " return self.num_points\n", "\n", " def _create_data(self):\n", " n = torch.sqrt(torch.rand(self.num_points // 2)) * 540 * (2 * np.pi) / 360\n", " d1x = -torch.cos(n) * n + torch.rand(self.num_points // 2) * 0.5\n", " d1y = torch.sin(n) * n + torch.rand(self.num_points // 2) * 0.5\n", " x = torch.cat([torch.stack([d1x, d1y]).t(), torch.stack([-d1x, -d1y]).t()])\n", " self.data = x / 3 + torch.randn_like(x) * 0.1\n", "\n", "train_loader = DataLoader(TwoSpirals(128000), batch_size=128, shuffle=True)\n", "test_loader = DataLoader(TwoSpirals(128000), batch_size=128, shuffle=False)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 708 }, "id": "oLybF53hAfu3", "outputId": "3b155309-eb7c-4090-f79b-487758ea43f3" }, "source": [ "import matplotlib.pyplot as plt\n", "\n", "def plot_samples(s=None):\n", " d = test_loader.dataset.data.numpy()\n", " fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(26, 12))\n", " ax[0].axis('off'); ax[1].axis('off')\n", " ax[0].set_title('Data', fontsize=24); ax[1].set_title('Samples', fontsize=24)\n", " ax[0].hist2d(d[...,0], d[...,1], bins=256, range=[[-4, 4], [-4, 4]])\n", " if s is not None:\n", " s = s.detach().cpu().numpy()\n", " ax[1].hist2d(s[...,0], s[...,1], bins=256, range=[[-4, 4], [-4, 4]])\n", " plt.show()\n", "\n", "plot_samples()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "2vlLPnUXCOme" }, "source": [ "# Create Model" ] }, { "cell_type": "code", "metadata": { "id": "XmKf9lqzAxk2" }, "source": [ "import torch\n", "from torch import nn\n", "from torch.distributions import Normal\n", "\n", "\n", "class Flow(nn.Module):\n", "\n", " def __init__(self, bijections):\n", " super().__init__()\n", " self.bijections = nn.ModuleList(bijections)\n", "\n", " @property\n", " def base_dist(self):\n", " return Normal(\n", " loc=torch.zeros(2, device=device),\n", " scale=torch.ones(2, device=device),\n", " )\n", "\n", " def log_prob(self, x):\n", " log_prob = torch.zeros(x.shape[0], device=device)\n", " for bijection in self.bijections:\n", " x, ldj = bijection(x)\n", " log_prob += ldj\n", " log_prob += self.base_dist.log_prob(x).sum(1)\n", " return log_prob\n", "\n", " def sample(self, num_samples):\n", " with torch.no_grad():\n", " z = self.base_dist.sample((num_samples,))\n", " for bijection in reversed(self.bijections):\n", " z = bijection.inverse(z)\n", " return z\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "hK_rD5wXCh18" }, "source": [ "# Implement Bijections\n", "\n", "class Reverse(nn.Module):\n", "\n", " def forward(self, x):\n", " return x.flip(-1), x.new_zeros(x.shape[0])\n", "\n", " def inverse(self, z):\n", " return z.flip(-1)\n", "\n", "\n", "class Coupling(nn.Module):\n", " \n", " def __init__(self, net):\n", " super().__init__()\n", " self.net = net\n", "\n", " def forward(self, x):\n", " '''TODO: Implement z<-x transformation and ldj (log determinant Jacobian)'''\n", " z = x\n", " ldj = x.new_zeros(x.shape[0])\n", " return z, ldj\n", "\n", " def inverse(self, z):\n", " '''TODO: Implement x<-z transformation'''\n", " x = z\n", " return x" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "61HH5RrzCSLF", "outputId": "9708dd75-13a8-479d-f568-952485c3f0b8" }, "source": [ "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "\n", "def make_net():\n", " '''TODO: Implement a suitable net'''\n", " return None\n", "\n", "flow = Flow([\n", " Coupling(make_net()), Reverse(),\n", " Coupling(make_net()), Reverse(),\n", " Coupling(make_net()), Reverse(),\n", " Coupling(make_net()),\n", "]).to(device)\n", "\n", "print(flow)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Flow(\n", " (bijections): ModuleList(\n", " (0): Coupling()\n", " (1): Reverse()\n", " (2): Coupling()\n", " (3): Reverse()\n", " (4): Coupling()\n", " (5): Reverse()\n", " (6): Coupling()\n", " )\n", ")\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 708 }, "id": "_2e8k1rjNt2p", "outputId": "c8fa545e-a1d2-4548-82fc-c8c3c39cca91" }, "source": [ "# Plot model samples at initialization\n", "plot_samples(flow.sample(128000))" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "2Ze4Y7gFGE-4" }, "source": [ "# Train model" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 374 }, "id": "cUDClrGYGHqc", "outputId": "f99b01d0-9d99-4cb4-a865-493b91631cd2" }, "source": [ "optimizer = torch.optim.Adam(flow.parameters(), lr=1e-3)\n", "epochs = 10\n", "\n", "print('Training...')\n", "flow = flow.train()\n", "for epoch in range(epochs):\n", " loss_sum = 0.0\n", " for i, x in enumerate(train_loader):\n", " x = x.to(device)\n", " optimizer.zero_grad()\n", " loss = -flow.log_prob(x).mean()\n", " loss.backward()\n", " optimizer.step()\n", " loss_sum += loss.detach().cpu().item()\n", " print('Epoch: {}/{}, Loss: {:.3f}'.format(epoch+1, epochs, loss_sum/len(train_loader)))\n", "flow = flow.eval()" ], "execution_count": null, "outputs": [ { "output_type": "error", "ename": "ValueError", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Training...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mflow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/optim/adam.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, params, lr, betas, eps, weight_decay, amsgrad)\u001b[0m\n\u001b[1;32m 46\u001b[0m defaults = dict(lr=lr, betas=betas, eps=eps,\n\u001b[1;32m 47\u001b[0m weight_decay=weight_decay, amsgrad=amsgrad)\n\u001b[0;32m---> 48\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, params, defaults)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0mparam_groups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam_groups\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 50\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"optimizer got an empty parameter list\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 51\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam_groups\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0mparam_groups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'params'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mparam_groups\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: optimizer got an empty parameter list" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 180 }, "id": "fzV7Ie6yHIVM", "outputId": "253ca3a5-2777-4c9b-cd08-b396ba22aee6" }, "source": [ "# Plot model samples after training\n", "plot_samples(flow.sample(128000))" ], "execution_count": null, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Plot model samples after training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplot_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m128000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'plot_samples' is not defined" ] } ] }, { "cell_type": "code", "metadata": { "id": "2W7KdLVQHKvH" }, "source": [ "" ], "execution_count": null, "outputs": [] } ] } ================================================ FILE: day_3/3_didrik/realnvp_solution.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "realnvp_solution.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "ByIKgJcoLZEE" }, "source": [ "# Tasks\n", "\n", "We will implement a coupling flow like RealNVP/Glow and fit it to 2D toy data.\n", "\n", "### *Todo*:\n", "1. Implement the `Coupling` bijection class\n", "1. Implement the `make_net` function\n", "1. Train flow and sample\n", "1. **Bonus:** Study the effect of some parameter (e.g. flow layers, NN layers, NN dim, NN activation, etc.) and improve performance." ] }, { "cell_type": "markdown", "metadata": { "id": "WX2sR4XXCE4_" }, "source": [ "# Create Dataset" ] }, { "cell_type": "code", "metadata": { "id": "TSYzJZf1-DsI" }, "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.utils.data import Dataset, DataLoader\n", "import numpy as np\n", "\n", "\n", "class TwoSpirals(Dataset):\n", " def __init__(self, num_points):\n", " self.num_points = num_points\n", " self._create_data()\n", "\n", " def __getitem__(self, item):\n", " return self.data[item]\n", "\n", " def __len__(self):\n", " return self.num_points\n", "\n", " def _create_data(self):\n", " n = torch.sqrt(torch.rand(self.num_points // 2)) * 540 * (2 * np.pi) / 360\n", " d1x = -torch.cos(n) * n + torch.rand(self.num_points // 2) * 0.5\n", " d1y = torch.sin(n) * n + torch.rand(self.num_points // 2) * 0.5\n", " x = torch.cat([torch.stack([d1x, d1y]).t(), torch.stack([-d1x, -d1y]).t()])\n", " self.data = x / 3 + torch.randn_like(x) * 0.1\n", "\n", "train_loader = DataLoader(TwoSpirals(128000), batch_size=128, shuffle=True)\n", "test_loader = DataLoader(TwoSpirals(128000), batch_size=128, shuffle=False)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oLybF53hAfu3", "outputId": "fc6b97fe-39b8-426f-f00c-118b7224a3ad" }, "source": [ "import matplotlib.pyplot as plt\n", "\n", "def plot_samples(s=None):\n", " d = test_loader.dataset.data.numpy()\n", " fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(26, 12))\n", " ax[0].axis('off'); ax[1].axis('off')\n", " ax[0].set_title('Data', fontsize=24); ax[1].set_title('Samples', fontsize=24)\n", " ax[0].hist2d(d[...,0], d[...,1], bins=256, range=[[-4, 4], [-4, 4]])\n", " if s is not None:\n", " s = s.detach().cpu().numpy()\n", " ax[1].hist2d(s[...,0], s[...,1], bins=256, range=[[-4, 4], [-4, 4]])\n", " plt.show()\n", "\n", "plot_samples()" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "2vlLPnUXCOme" }, "source": [ "# Create Model" ] }, { "cell_type": "code", "metadata": { "id": "XmKf9lqzAxk2" }, "source": [ "import torch\n", "from torch import nn\n", "from torch.distributions import Normal\n", "\n", "\n", "class Flow(nn.Module):\n", "\n", " def __init__(self, bijections):\n", " super().__init__()\n", " self.bijections = nn.ModuleList(bijections)\n", "\n", " @property\n", " def base_dist(self):\n", " return Normal(\n", " loc=torch.zeros(2, device=device),\n", " scale=torch.ones(2, device=device),\n", " )\n", "\n", " def log_prob(self, x):\n", " log_prob = torch.zeros(x.shape[0], device=device)\n", " for bijection in self.bijections:\n", " x, ldj = bijection(x)\n", " log_prob += ldj\n", " log_prob += self.base_dist.log_prob(x).sum(1)\n", " return log_prob\n", "\n", " def sample(self, num_samples):\n", " z = self.base_dist.sample((num_samples,))\n", " for bijection in reversed(self.bijections):\n", " z = bijection.inverse(z)\n", " return z\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "hK_rD5wXCh18" }, "source": [ "class ReverseBijection(nn.Module):\n", "\n", " def forward(self, x):\n", " return x.flip(-1), x.new_zeros(x.shape[0])\n", "\n", " def inverse(self, z):\n", " return z.flip(-1)\n", "\n", "\n", "class CouplingBijection(nn.Module):\n", " \n", " def __init__(self, net):\n", " super().__init__()\n", " self.net = net\n", "\n", " def forward(self, x):\n", " id, x2 = torch.chunk(x, 2, dim=-1)\n", " p = self.net(id)\n", " log_s, b = torch.chunk(p, 2, dim=-1)\n", " z2 = x2 * log_s.exp() + b\n", " z = torch.cat([id, z2], dim=-1)\n", " ldj = log_s.sum(-1)\n", " return z, ldj\n", "\n", " def inverse(self, z):\n", " with torch.no_grad():\n", " id, z2 = torch.chunk(z, 2, dim=-1)\n", " p = self.net(id)\n", " log_s, b = torch.chunk(p, 2, dim=-1)\n", " x2 = (z2 - b) * (-log_s).exp()\n", " x = torch.cat([id, x2], dim=-1)\n", " return x" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "61HH5RrzCSLF", "outputId": "606da163-5735-4a94-9043-5770cb5c3864" }, "source": [ "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "\n", "def make_net():\n", " return nn.Sequential(nn.Linear(1,256), nn.GELU(),\n", " nn.Linear(256,128), nn.GELU(),\n", " nn.Linear(128,2))\n", "\n", "flow = Flow([\n", " CouplingBijection(make_net()), ReverseBijection(),\n", " CouplingBijection(make_net()), ReverseBijection(),\n", " CouplingBijection(make_net()), ReverseBijection(),\n", " CouplingBijection(make_net()),\n", "]).to(device)\n", "\n", "print(flow)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Flow(\n", " (bijections): ModuleList(\n", " (0): CouplingBijection(\n", " (net): Sequential(\n", " (0): Linear(in_features=1, out_features=256, bias=True)\n", " (1): GELU()\n", " (2): Linear(in_features=256, out_features=128, bias=True)\n", " (3): GELU()\n", " (4): Linear(in_features=128, out_features=2, bias=True)\n", " )\n", " )\n", " (1): ReverseBijection()\n", " (2): CouplingBijection(\n", " (net): Sequential(\n", " (0): Linear(in_features=1, out_features=256, bias=True)\n", " (1): GELU()\n", " (2): Linear(in_features=256, out_features=128, bias=True)\n", " (3): GELU()\n", " (4): Linear(in_features=128, out_features=2, bias=True)\n", " )\n", " )\n", " (3): ReverseBijection()\n", " (4): CouplingBijection(\n", " (net): Sequential(\n", " (0): Linear(in_features=1, out_features=256, bias=True)\n", " (1): GELU()\n", " (2): Linear(in_features=256, out_features=128, bias=True)\n", " (3): GELU()\n", " (4): Linear(in_features=128, out_features=2, bias=True)\n", " )\n", " )\n", " (5): ReverseBijection()\n", " (6): CouplingBijection(\n", " (net): Sequential(\n", " (0): Linear(in_features=1, out_features=256, bias=True)\n", " (1): GELU()\n", " (2): Linear(in_features=256, out_features=128, bias=True)\n", " (3): GELU()\n", " (4): Linear(in_features=128, out_features=2, bias=True)\n", " )\n", " )\n", " )\n", ")\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 708 }, "id": "_2e8k1rjNt2p", "outputId": "e1fa832b-c567-4cb2-8fed-a9cc65fc554e" }, "source": [ "# Plot model samples at initialization\n", "plot_samples(flow.sample(128000))" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "markdown", "metadata": { "id": "2Ze4Y7gFGE-4" }, "source": [ "# Train model" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cUDClrGYGHqc", "outputId": "dc8bc3b5-b1ce-4263-9b42-d961abdc97a7" }, "source": [ "optimizer = torch.optim.Adam(flow.parameters(), lr=1e-3)\n", "epochs = 10\n", "\n", "print('Training...')\n", "flow = flow.train()\n", "for epoch in range(epochs):\n", " loss_sum = 0.0\n", " for i, x in enumerate(train_loader):\n", " x = x.to(device)\n", " optimizer.zero_grad()\n", " loss = -flow.log_prob(x).mean()\n", " loss.backward()\n", " optimizer.step()\n", " loss_sum += loss.detach().cpu().item()\n", " print('Epoch: {}/{}, Loss: {:.3f}'.format(epoch+1, epochs, loss_sum/len(train_loader)))\n", "flow = flow.eval()" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Training...\n", "Epoch: 1/10, Loss: 3.149\n", "Epoch: 2/10, Loss: 3.114\n", "Epoch: 3/10, Loss: 3.061\n", "Epoch: 4/10, Loss: 2.994\n", "Epoch: 5/10, Loss: 2.978\n", "Epoch: 6/10, Loss: 2.968\n", "Epoch: 7/10, Loss: 2.954\n", "Epoch: 8/10, Loss: 2.945\n", "Epoch: 9/10, Loss: 2.942\n", "Epoch: 10/10, Loss: 2.937\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 708 }, "id": "fzV7Ie6yHIVM", "outputId": "4035e46e-0741-4900-f425-aa8b8905be24" }, "source": [ "# Plot model samples after training\n", "plot_samples(flow.sample(128000))" ], "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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Among them are e.g.\n", "- `sbi`\n", "- `abcpy`\n", "- `pyabc`\n", "- `DIYABC`\n", "\n", "There's a [gitter-chatroom](https://gitter.im/elfi-dev/ELFI-workshops) named ELFI-workshops where you can copy-paste code and ask questions about the tutorial and the you'll be doing in the second part. \n", "\n", "First we need to install the package from PyPI" ], "metadata": { "id": "4PPE2Jy71z8E" } }, { "cell_type": "code", "source": [ "!pip install -q --upgrade elfi" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SXBFFdLc1w6E", "outputId": "bc786dd9-35c1-4387-9e2d-66d7c4a7d5c4" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[K |████████████████████████████████| 166 kB 5.5 MB/s \n", "\u001b[K |████████████████████████████████| 959 kB 40.8 MB/s \n", "\u001b[K |████████████████████████████████| 296 kB 40.1 MB/s \n", "\u001b[K |████████████████████████████████| 1.1 MB 35.3 MB/s \n", "\u001b[K |████████████████████████████████| 99 kB 7.7 MB/s \n", "\u001b[K |████████████████████████████████| 596 kB 51.5 MB/s \n", "\u001b[K |████████████████████████████████| 140 kB 51.5 MB/s \n", "\u001b[K |████████████████████████████████| 837 kB 50.2 MB/s \n", "\u001b[K |████████████████████████████████| 71 kB 7.7 MB/s \n", "\u001b[K |████████████████████████████████| 189 kB 57.8 MB/s \n", "\u001b[?25h Building wheel for GPy (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for algopy (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for paramz (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "gym 0.17.3 requires cloudpickle<1.7.0,>=1.2.0, but you have cloudpickle 2.1.0 which is incompatible.\u001b[0m\n" ] } ] }, { "cell_type": "markdown", "source": [ "Next, load all the necessary packages for the tutorial and the assigments, and activate some `logging`.\n", "\n" ], "metadata": { "id": "7-uXhq5RL0I6" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.stats as ss\n", "\n", "import elfi\n", "from elfi.methods.post_processing import adjust_posterior\n", "from elfi.model.extensions import ModelPrior\n", "from elfi.methods.bo.acquisition import MaxVar\n", "from elfi.methods.bo.acquisition import RandMaxVar\n", "from elfi.methods.bo.acquisition import ExpIntVar\n", "\n", "%matplotlib inline\n", "%precision 2\n", "\n", "import logging\n", "logging.basicConfig(level=logging.INFO)\n", "\n", "# Set an arbitrary global seed to keep the randomly generated quantities the same\n", "seed = 16062023\n", "np.random.seed(seed)\n", "\n" ], "metadata": { "id": "geLZnFZkZVGq" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Model building with ELFI: case MA(2) model\n", "\n", "We start the tutorial by using the 2nd order moving average model MA(2) as an example. MA(2) is a common model used in univariate time series analysis. Assuming zero mean it can be written as\n", "\n", "$$\n", "x_t = w_t + \\theta_1 w_{t-1} + \\theta_2 w_{t-2},\n", "$$\n", "\n", "where $\\theta_1, \\theta_2 \\in \\mathbb{R}$ and $(w_k)_{k\\in \\mathbb{Z}} \\sim \\mathsf{Normal}(0,1)$ represents an independent and identically distributed sequence of white noise.\n", "\n", "In `elfi` the form of the simulators have few requirements and restrictions that we need to take into account. Let's look at the definition of `MA2`." ], "metadata": { "id": "xhdCBGe70avX" } }, { "cell_type": "code", "source": [ "def MA2(t1, t2, n_obs=100, batch_size=1, random_state=None):\n", " # Make inputs 2d arrays for numpy broadcasting with w\n", " t1 = np.asanyarray(t1).reshape((-1, 1))\n", " t2 = np.asanyarray(t2).reshape((-1, 1))\n", " random_state = random_state or np.random\n", "\n", " w = random_state.randn(batch_size, n_obs+2) # i.i.d. sequence ~ N(0,1)\n", " x = w[:, 2:] + t1 * w[:, 1:-1] + t2 * w[:, :-2]\n", " return x" ], "metadata": { "id": "co2t8hnfZV_g" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Above, `t1`, `t2`, and `n_obs` are the arguments specific to the MA2 process. The latter two, `batch_size` and `random_state` are ELFI specific keyword arguments. The `batch_size` argument tells how many simulations are needed. The `random_state` argument is for generating random quantities in your simulator. It is a `numpy.RandomState` object that has all the same methods as `numpy.random` module has. It is used for ensuring consistent results and handling random number generation in parallel settings.\n", "\n", "We can test the function by evaluating it with some parameter values. " ], "metadata": { "id": "8fRwk7Uk16ZX" } }, { "cell_type": "code", "source": [ "t1 = 0.1\n", "t2 = 0.3\n", "xobs = MA2(t1, t2)\n", "plt.plot(xobs.T);" ], "metadata": { "id": "Vr_EBUiEZjkC", "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "outputId": "787e38d5-84a3-4b94-f4c4-68ee9a690a14" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "Simulator is stochastic and the output will vary unless you fix the state in `numpy.RandomState`. Here's a few more realisations:" ], "metadata": { "id": "-gpKCqod8Wi2" } }, { "cell_type": "code", "source": [ "plt.plot(MA2(t1, t2).T)\n", "plt.plot(MA2(t1, t2).T)\n", "plt.plot(MA2(t1, t2).T);" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "-EL8_Tzh8USe", "outputId": "5c94d41c-ace1-40f8-9aef-a466814ac12f" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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NFPlAlj07vvZ2dXdeblwG4NT4qb6fl+zSnSHyVJH3iFxz0EiRZ9DnkUcbWyuhCJPHDxxQmyjyl643aXlbxKZuMZEvelUArgU7JJygC0hwinRFCYCKbONYxv4tdpYPqa/3OrUiYSJ5z1WvyhwhJalOdj/2d5eo6Fahu7Zn2wk99faRr17f3h+ErqqBwDpr5Xde+B3ef+79fT/zQo+clcM27ZuatQL9yRWtyNdZK2GH8Zyy9W6XR64z5FlrBaBsl+8IkeuopvbK4dau1wl3LZHHQ78ejB9GRkLkg8pA35IO3M5LKfm+X/8cv/WZS5tvgN+6JVP7NBYSAl8Id3gQajK0S7SEOtnLdMhZ5v5U5F4TKgfV13tE5LFUcUNHxthAxcxTdatcJuBUGKdphl2pcr0IxjZa47cLTY5PXFplpbWNi0vgQn4CEOuOv8XW4jpP2Is88mYe27B37ZHbprK/Gt31inydtRJ0mMpPAbeXyCt2JX1djTtvrWSIfGStrMdGxc5+j9wiFOr77qD60h/ugCJ3g5i2H3GtugWp6Fvazq2Z4bKYDOVfiHZIbnq7nCLtyMSVNsVEke/LhiC3oRZ9EMaeeeS6CcVJOvom7YpS5LHLyTCi4lSAXRQ8o6AXl+xW92RboWdXxBI++vw2VHnYVbNjnFKftdL0mzSD5jri8iKPnJnDMZxdNej4YcxUyUm2NaPINyp2hj0i37ZHvnbpptZtnWvMcXLsJKo3sYeSVerLtd8u6GLnyhBrZd/myO8ENvLIg6i/szMwFHl5g7eUWskMHGidJF61utlis6HfiwXeIntlISnSXo/dnamoVJEXaXkhTYoUohaOaezP1IpXVzFJu7hnRK6z0nZCWpO5Ma63r7MYdznleSmR71iRZ3sR9pDIdQHxQCXXZ6+4QcRPv+9pnr46UCcJXEXkdrHPWlloLQDrc9Ne5JG38spa2eXQrOlSLtnW3oVgJRExgx55O2gznZ9W72G7RP6pfwOP/fiOt01jrjHHyfGT635esku09zDWul3UOuoYHKVWtoBW4YNFvH5FbuAbitjdeICYU0U+SOSK+De9xc2eKK29J3IpJYuElOOYGLjRXt8htiH0Bcop0fEjGrJIPmqTs4z9Z61IqayV/Jgipj2yVvTCy3akPsvJ3AQvrL6ABE75LmOWqh3smMiz5L2HPrmux3zf1x/jCxdX0xXrf/Vj5/iDr8zzF18b+PxDF6yciphmrJXF9iKgrI3sfBE3dMmZOWzD3nVDUDlv4ZhGetEJooCqV8UQBs2g2fd6WUW+bWvFa+z6XPIij8X2IicrGxD5bVbkUSzTC15WEI488iHQNkE5Z/V3dvZ55Bae0EQ+cEu5gSLXDQ8rzU2IPHuFvwWKvOE3aAl4g6u2YUcrneiLTEaRO1FLFTv3G5H7LbWIcC4h8j2qN2iycrQiz0+nhc1TQUDFSGyCmyHyzt4RedMNKTkmf/11h4liycdeuM5XrlT5zc9cBGB5UFSELlgFcMp9Fz+tyEMZphcz6FkrlmHtOrXimAZjBSv1yPVY6KPlo4RxmO7fIFLzVVIi3+66naGrLKNdHANXG1eRyHXRQ7gzqRVtP00UbWqdIOUk7ZGPrJUMNClV8vaAIu9ZKzkbfEN5Zp4cIHJ9AgQbKXJ/46lpt5jItbJ6k6u2TZ+g20LWI/dCGrKIHTTJWfvQWtHNQHltrWx8Ej/21DV+8L98YVtPq5MZTqQ+84nCTPq7k0FIRajVo25Oke+lRx5Qydt83ZExTkwV+eBXFvhnH3iWg2N5Tk0XWR4UFUEX7LzaZxlbQx830G+v6Il7uy92ShzLoJK3U5Ja6i4B8MD4A+o9JPtSZ8grTgXbsLdvrejzUA+L2wFudNQdy+Hy4XW/K9klwjjc1Z3IbqELnadn1J1ftd0fgR5ZKxloUirnrHXWimUIhBCYZo+8XTlQ6PM3sFa8ZMBUFG88SN9v8aV8jrP53C0h8oWmIu43uB4GO1Tk+n3ZJdp+RJMCVtDcn4pct+fnKlt65E9dqfKlS2t99RCAulfn9178vb6LriYrJzl5p0oqFXPArlCUkkpyyO+42NnNeNV7aK003ZBy3kIIwXe+9hBfuLjKy0st/tX3vZYT06X1RB66ar76gLWSveBnidwLlSK/mfihbQrG8lZamNX++Onx0+o9JCkrnZgp2SUKVmH7RK4tmF3sV11sLdvldb8r2aW+x9wO6Az5mVm1PbrgObJWhkCTUjlv9XWeqYNOvSVhZAoNcoDENsiRdzKzJDb0yf0275ia4D9MTtwaRZ40N5wIQg5Kc4fWivbItSIvYfiN/Zkj1wmQ3PiWRK6LR4OjVP/s0p/xS0/8EgvtHomlHnnog1VgoqBu80/llTLPxzGWYe0bRd7yQirJGrN/7bVKVf7AG4/xLQ8f4EAlt4EiL6ju3WC9Rw79bfI3Gz/0Q3VOVfJ26pHrxIomcl3w1Iq8YBfIW/nte+Q3ocj1RWszIr+d9opOrJw5oLZHZ8m1tZIzc7fkde9KItceeSVv4WdidUEksZLMq5TqIMrFMR4DJJZ65P0HmrZWYBOf3GtRNUyWLevWKPLGFfJxzGQcc1QaO7RWejnythfSoohwGzjmPlTkfdbK5sXOaludHPUBIl9zlYLLdhOmqZXQg1w59WtPFZQyF2GXMWds10TuO5N76pE33JBK3gbgdccmePePv5lf/J7XADBbybHc8vptvlSR98cPF1oLHCkdAdZbKzkrt3sij+LEWukp8qXOEoYwOF45DmSIPPkMi1Zxd4p8F/tVv3bJKa37nSby2zlvRYsOba2sZhR5wSqsi0juFe5KIvez1spAsdNJFLkRK6KYjGNcBvzuDXLk7T5FvoGv5repmgZLpolsKa9wL1chWWzNcziMEMCReJfFTqdI24twzRIi7FIw5f7LkXtJnC+3DSJPTo6NiDzbCp4WO0MPnDITuQkATpYVyRF0qTiVHRO531qlQYnFsHwLPHIr/f6bH5ql4Kh5ArPlHEEk+9+3VuROMSVyL/JYdVd5xeQrgOHWimPurkVfn1NjeTu9I1rprjCTn2EsmbCZWithj8jzZn77xc5Uke+cyHUqpWgV1/2umHTA3k5rpTGgyPWdfTfs3jJ/HO5SIvfCGCGg6JjrPHJtrcSRIorJSOIOXgQ36OzsZhX5BtaK79boGAaBgEZnhX/xxy/wE+8+e5PvqIeFzg2OhCGYDscipX62XazxO4AAK0/bC/FMlZkeF519rsg3L3bqAlK9G6jGnIa6S6kmowyyyk9bK06gFPmZiTO86dCb+Muzb1APCLpU7J0T+aWr16jGJdbi0p43BI1liDyL2Yq6De+zV7Qiz1griy1lq7xiIiHycKDYuSfWSkaRd5eYKc5QsdXxtU6R27tV5LuwVvw2BauAZazfh9puuZ3Wij5Wj08WsU2RRhDd8NYt8wZ3KZH7YUzOMnAsI02qgLJWbEuxdpQMnBqTJoEQRHFGkW7gkbeTYqchNibymttTDUt+jfM36nzm5ZU9I8qFzjKHwxCKMxyJIiSS6+1tzuHQi0oIQdsPCZIDeWw/Enm22Ols7pH3KfKvvAf+30ch6FJ1EyLPrkupUytBF5wyJbvEb7/9tzk9cUY9INy5Im97IctL16lRZjkqIXdBOBuh6QaUc9skcimTWSuZzk4p0xqBJnJNqFJKvMjDMR1F5LsqdqrUyljBphtEBFHMSmeFA4UDlB11fGnFm1XHu/LId1ns1BbKIO6ItdINKDkmjmUwXcqlTUG6MetW4a4kci/JtjrmEEVuqLfkJ8t6lVH+o5eNDW6UWvFDcpbBVMnZkMg1eQAsmwaGW8WPYl683qDu1bna3OYUuyHoBB2qYYsjYQTFaY4ESgFda13b3hP4bXWCo8gnSOaKVERn28XO5c4yv/fi722xoWvwm38Vqpe3t13D4DVUa75T3rTY2fWjNKXU6AZQnVMWUnu5Z60MmbltBx313BrpoClF5DtJrbz3i3OU4ia5yjRVWSHu7I0iD6IYN4hTj3wQKZHrY1G/T51aQRG7VuQPTj4I9BRoGIfEMr6p+KEf9RQ5qDuI5e4yM8UZSnYJgejFDzOKPG/lt6fIpbwpRd4JOkMLnXDnip3jBfV5TpedPo98ZK0MwAtjcra5LlaXtVbc5EQtogL4bkZJ9xT5+hx5KWcxU86x3PThPd8LT7yz7zE1rxdDWzZNcp46+J65WuMdT72Dn/joT+z6fWnlfSQMoTTNsUAdBNsueAadlLDaXkSc3PpWZGfbOfI/ufgn/NITv8RSZ2njBy29ANe+BPNPbW+7hsFtKDUuRNIQpNTlILQah0SRa1ujs5peVLPKLy12Bi7kskSeNGIEnR0p8q4f8c5PX+SQ02VscpYaJcQexQ9biVVR2a61oi92OrUC4LdZaC9gCjNdIUcTVzYpsZv4oZQy8cgFY8nFptrpsuauMVuYxRAGJbuUKnL9ORStIgWzsL0xtlnbcBdE3gpaqRc+iDsSP+wEjBdV09l0OceKzpFH3VvWDAR3LZFHSpEnsTpdbMxaK53kRHWk+jC97PqQm3R2Fh2TmXJOKfIrX4T5fv+7mlFyy6ZJIVBk8vTVOpfql1hoL+xaAehb5CNhCMVpDgQelrC2T+R+p6fI/TBd7q0s2tsmcq1y694m65zqTPXNrIXqNVT0EBKSlUPHCq8jcle9tmytpBfVvnUptUfutQcUuSbynaVW/vsTc6y2fWbMDmZpipqsYETensyGaaZEPlyRV3IWOcvoEbneP7pFH8Bvs9ha5EDxAHkrj2M4qTJOlxfbpUcexhIpSVMrAFfr6gI/W5wFoOyU1ynyglWgYBe2Z61k9+Nuip1Be0NFrgugt9NaaXQDJhJFPlNyUmtFj0q4VbgridwPY3K2Qc5Sm699ctUQpH6midxCkVlXWyJ68QVhQBxC1EuqdP0oIXKHarOjHjdAVrXk6i4QLJsmxYTIn71WS4n4SuPKrt6XJuzDYQTFGczQ51Dp0PatlaCdUeQhsqCIshwrj3w76ZptEbneJzdD5G4jXcUoVZdDTjhdPIJ+Rd5ozhMljV59xU6dWvHbSvFr6P3SblK2y9uaSf7S9Sa/9vjL/OUzk9h+HacyTY1kW/cggqhz2Rt55EIIFUFMiTwhPauQXrAJOiy0FzhcUhn0bFu63i+7jR9mR17oi818U901zhYSIrfLfTnyglXANEyVWtkOkWfF1G7ih5t45KZhUrAKt7fY2fU3tlZGHnk/tEeu5yRr/zcbP+wGbYSUyITIU0WuF18oTKrvMwdS248oOspa6baHq85aErE6VjrMsmVSCdcwBJxfrqV2xFxzblfva7G9iIVgVhqKhCKfo+WjO1TkSeTKizCSdTuLUp1o2cLwRtB2Rd3fjMj3SpEnRJtRy+u2J1HkliESIlevXc3sk75ip+7s9AcUuekghcG7P/UCl5fV8bKZKl9pefztdz9J0TH5d999GmRMYUx55MCeJFf0wKyNUivQy5IDvaKgne+zVhZbixxJ4pVFu5gWHbdS5LGMN90HQdhbqGWsoLZxMemd0Iq84lRS66ITdFL7YNseuf7Mi9O7Kna2g/aGRA5Kld9ua2WiqIk8RzeI6PhhOirhVmFPiFwI8dtCiCUhxHN78XxbwdceeULa2ifvs1aCDgUp6cSKzNyUyBPVl3T8ZW/nO15irVRy2PoqPkjkkUtFCo5UjrFsmlSiGq85Og5WnTjpIN2tIp9vzXPQyGPmKmA6ICOOlo9sP0sedNITvOOHWAV1ESvG6r1sp+CpFXnD26QYqPdJd4dL0WXhNVLrJ1uIHEQ1UeTHJgv9RJ5J8mSVn/aBbSn7PXIhkFYBO/Zod9WJtlHB0wsj/u57nmK56fFf/9dHOWQnzWWVaVqmJvKbV+RbWSugsuRLjWGKXO2z0Gtwo3NjuCIf9MgHiPxPL/4pb//A29cRbjfs8jvP/w6dZAENxzJSj3w5EStZRZ6dtaLtjLyVJ4iDrWeg69ceO6qO3x0OztqKyMtO+bZaK33FzmSO+2rLpxveHR75u4Dv2KPn2hJeGJEzDZxkQV1N5GHWWgk7FGNJUyqycLXy0ImVopqZnO3u7GQUeVkkPx8g8mrsMS5MZosHWLIdpqjzltPTGN0a40IAACAASURBVHZPoe2WyBdbixwRtlKqljoIjhQOsNJd2Z668VvgFIljScePKORz4FQoxIlnGmzdFKQVebaouw574ZH3WStaka+/Ba4lxaIT0yXq3TC9G1jr9hb1GJojl5Ku0X/ihGaeAh5BoJTRRmr0X/zxC5ydq/Lvf/D1vP74RKq+RWEKQwuAPbBW9BCqjYqdsJkiV4S53L5OJKNUkWdXjvcSkaKtlVjGfTHc+dY8zaC5zkb73Pzn+Ldn/y0vrLwAkDYEAay6KwhE2jFbdsp9irxoF+G3v5PiwrNqG7ZaUk9fvMePqf93eIFsBa0NPXLQirzNz3/wOb567SaO123ADVTCajxR5DNl5YmvtLy7I7Uipfw0sLeLGQ7BxfpFPjv/2dQjd6x+Re5HMk2tdEOXvJR0oqTYqU9afXUuDlHkfkgppzzyMsOJvBYHTAqb2cIsK4ZgRtQ5Mp5ndlKR0KHSIa40d+mRtxc4Ig2lVJPCyJHkFjY7S2ND+Cq10gn0mF8T8mPkI/Xed6LIb7lH3qfIN7ZW1jo+paRu0e500xkt2RhoX7Ez8cgt4OUB9yM08hSEj+upE20jIv/YCzf4G68/wl9/XdINqq2kwiRmKTlu9sBa0Yq8vAWRr7V95Vf3KXJFXgvJcaHb87PLm6XLiyXWCtCnyvV+G7Qe9H5pJFl/2xLpNla9VaYL02kDTsWu9HnkRasAV75APul63tInTxV5sq93cIH0I58wDjdV5CW7RM1t8p4vzvGJlzZJYu0CT1xc5Z//0fPp97qek/XIQSnye8YjF0L8pBDirBDi7PLy7maU/O7Xfpef+fTPbO6Ra2slcilIQTtM4odaifsD1kqGPNppsTNHRStyv6W6CRNUiZgwcswWZ/EF5M26UvETHZCCNx96c7oY7E4QRAHLnWWORFIpclMdDMfyisi3Za8kDUHtxHst5SzIjZGLEmtli+RKJ+ikJ/9mHrnsVvlEsUDo7tJakbJfkTubFzsnig7jBRuZuXDUEltkKj/VHz+MfSwsBFAN+1MCnsiRx8P1Np5JLqWk2vE5NplR85q0C5PkKslY3D2wVrRHvpUih2RmR1aRJ9bKQmJ16DGuJatnrQwqcugncn0nM7gf9PfN5JxxTBPTEJQck0awmtoqkKRWksUlOkGHorAAST5R/lsSuT7/UiLffgRRX4A2tVbsMvXkgpSdpbQX+KNnFnjX5y+nyRQ9SmGi0IsfAiy3Ovixf28QuZTynVLKR6WUj87Ozm79B0NwonKCht/AjRp9qZVh1ko38slLQTtUO8/Vt+36/yGKvJtYK7OVHBUypOL2vNS6iJm0Cr2D2WxRcEwKxTpxOM6B/AnW3LV1S2Btheud60ik6urMlVXEDDicV0XZLRW5lMo2ShaVACg5FuTHcSK1LVsR+Voma7+ZIn/aW+bvH5zlC+EuVWnoQhxsS5FXOz6TJZvxgo2Vubishao1eyI30d8QFAVYSU33muw/zjwcCvi03Y2JvOWFBJFkKskCA31EPj42houzJ4q84QY4lkEusQiHYbacyZKnDUGF1FpZdJXFlPXIh8YPE2GQHfegL9qDilx/r4lcC6axgk0nXGMmM+O94lTSxSU6YYci6r3kkzTYlpZg1iOHHV0g9TJuZWcTa8Uu0koe1/W38Ot3iGtVdbyeX1L7Sw/MSoudiUd+o6l+XzD3v0d+W3Bi7AQAXbmU5sghq8gz1koc4GDQ8BWRe1rt+YPWivowpJS0fbVay1TJ6Xnk0Lu1BqpCMGEV06p9aHcoOiaRsUrsTxK5ynvfaXJFNwMd8txEkauDYExsc0Wb0AUkOMV0rnopZ0H5AMWueu6tsuRZu2KzYufLSddsfbdFpEx7/n/99EU+8Gxy8g4pdFU7AZOJIp8gIZzx41Qjj8ncJDkz159a8RrkY5+PRo/yojzR91xdcuTxabSVAh5W7NS3x/pkVH+oiXyCmYpDVZaRe+KRD5mzsnoB4t7n1OvudDMNQfmeteJVmcpPpWovu7xZX7FzE0W+kbXSTn5uJ+dZJW/RlTUOFA/Q8UO+5z99los3wvRvOkEH3ZpT2C6Rp4o8IfId7Ff9PreyVrpJ0myvFfl8LSHyZbWftCLX1kreNinnLJZa6vf3hCLfC2gi98USOcvEMfuLnX7WWpEheWlS9dWh5YaayJODNi12qgPNC2OkhIJjYZsGs06m4yy5pXe9Fl1DMGlXUkXetGLKwqceLkE4yVpdqcydFjw1cU75nT4iLyAwhbm1ws+MsO0pchMOvIpi+wo5/K2JPBlCNZ2f3rTYeVGqfdaJukO7MQcRRDHf858/ly5f1huYNc6Hnpnnz84lSnuoteKn1sq4SO6mpl9BlZDJ/OS64Uz+/FkcKfnX4Q+vm+Xdjh0KwqfeYcOZ5HrNTL1yPKCKu3YJrBwz5Rw1WSZoDbcAlhouf/789mbjNN2wP0PeWIT/9CZ47rH0R33dnVlFbjlgWCz6jdQfv7rWIQztdN1Oba3oFn3YwCP3hyvydvJ56HRYJW8QyAYzhRk+f36VZ67Veeys2g/NoJkEDNQxVgjV6+zcI9+BteJvba2U7BJeUuzvbKPYv11IKblWVc+bKvIBIgflk6901HG274lcCPE/gC8ADwshrgkh/vZePO8gjpWPYQgD31jCsXqKPMh45FqRd2REXtg0fXVCpif7BvHDnq+sLg4H7PVEXkvsjXGnkt5eLpsmhXCF5c4Sk85BLt9Qt087JXLtSY+7rSS1ok5gEQfbW3uwb4RtxiM/8CqEjDkjFra0Vla76iR6YPyBTT3yC0I9f0fG67pjX66+vC6p8L4nr/LM1RrP6NRAZoRtrROw5idkNsxaaftMFpW1Mq4V+fQZ1gyDSbus8spakS+fw189TyDzXJKH183LacU2eTzq3XDDNv219PZ4wFpJ+g5myjmqskzYGq4c3/X5y/zke57afAFvvT3JMm8pVs+DjODGV9MfzWStFb1/kmMDu8SNsM2h0iEAfu4Pv8pfvFBHIumG3S0VuSbZdUTuayJPPPLkPMvnOyAkB4oH+OS5JVVPKqp474WVFaXIEyWeTyycLdv09XtyypAf35ki32RRCY2SXSImAKK+6aY3i7W2jxuo80kTeV0XOzN3c9Mlh9X2XaLIpZQ/LKU8LKW0pZTHpJS/tRfPOwjHdDhcOkxsLpOzMsXO1COXvfghMY6wAQtTSjx9QA1aK8mBpG+7CrYi8mk7cyK6dbp+xGoyPnUyN6FGdYocy6ZJt3tBLQA7foyXFj0OFA/sOLmiPekxv5OkVhIiCX3VPbdVU0OqyIvpXPVSzoLZVwHwkLi2ZWpFK/JT46c29shDn4vJid0xjL7kSifo8EN/8kN86PyH0p91/Yj/8PGXgZ6HmB1hW+8ErLiJR5xR5H968U/5pS/+Mg03TBX5RFaRmwZTZoG8mWk8efz/wTctqrG6Kxok02ZkU8AniiVlaziR623sV+T9RF6jvKGXezXxTZ+a29pDb7phf6GzngxcW72Q/ihvm4zlrX5FrmsKTpFq7KVRwLW2z0pDnRPtoN3nkTvJ8ZSdt6J/r+eJp9uVzhfXHrn6vHOOev2J3ASffGmZt56Z4Wfe/ggA//IjZ/Fjn6K+Cwh6c7g3RZgp4Band6TINZFvpcgBMLy+FcBuFtofHy/YPSLvBpiGoJK5y5op56h21XaOPPIMjleOI62Vvvihl7FWdENQF0lOKOWSlyqOCGSKndpaUQecJvJS8iFMmB5Rsntkt8b3/vrnePfnn1a/S06cijnGsmVS6yg//PjYUVZaHscrJ3acXKl7dWzDpiClUieayCOPklPa2lpJFXk5HcdbypkwfYbYsHnIuLZljnytu0bOzHGoeIhu2B06B73ZnGfJUvuobYg+Im/4DYI46POe/9vnL7Hc9Dg4lustkJDYSIFdpumFrPkCEH2K/M/Of4gPnHs/EGUUefIep05TMwwmDafXQTj/ZXjpw7THHyCSivgHrZV6aFEU6j0VrPIG1kqAPfl5zjWe7P2wW4WCWqBituJQk2UMdzhRzye327si8lpC5GuX+h6XZslDF4SZJppip0hNBkzk1bZ1/Ii2m3w2QRs3dDGEgWVYqSLPNujoC+Dg3d7gfHEtmHI5te+aHYtr1S7f8vAsD8/qeKxaBLmYEHg+7M0Y2RRBxi4qTO15akU3KAnDH6rILyy30tTJTqD98W96cIbFukvLC6l1fcaS9Vc1pss5aq7aj/tekd9OHK+cQDgrOKbZS60MtOiHcYgnoJBksXMIPE1KevGFpH1dFzu1ii0mq7OMG11uSEXYVxcXefF6k9W2inpN5tVFoGhOsWyarHVVNPAVEyeREmZzR3dlrYzZJQT0WStEPhW7srW1ohX5oLVi2gQTp3lIXN2WIp/KT6Wr6gwrBl5c6eVmO0L0dXfqbdSqr9bx+Y1PXuDbX3mAt56Z6Q3ASoqdLakUSsuLkAOLS8wvPUsoI4Rd7xU7RQvfKtMpjOMaBpPS6BH5wlfUNuemQVq8/vgEbT9KVZgXRjRCm6KhtiFnlIYSebXt40x/kg9f/mDvh4ki/+MLf8xnrn+IGmUcvz60PqBP8LOXt7YI1CzyjLWiFfnaxXUFT2WtuD01DjTsAjGkirzthRArAdAOlSLPmTmEEMOLndHw+GE6zTBSn4c+z6zEbvzavHqOb3l4loqjOl0fPKK2N+8l6tNXz51V5C0vXE+mYVfNPTLt7bfpB1147jHa/sYLL2sYyTEmTHdosfPH3/Ukv/b4y1u/ZoKPz32cX/nSr3B1Tb3Pb35IXcguXVvg8MoX+i054CHjGj8b/howIvI+HCkdR5hdpNFKi51BGBMlk9osw0gPnlxyK5ND4CYdf+niC3qnJspBH2BFRymasuhyQ04gDYuX5xRRu6EirYkkseIYMyybJkvuDUxh8tCs6k4rm4epetUdzbyue3XGrURZZHLkhH7fqNANEfSKnam1kryXYPqVylrZRvxwMj/JeDKVsDYkJ36xek69DAbtAWslJfKELH7jUxdoeSH/5O0PM16wewOwEmulFvdISWaWe5NSspCQiOPcYKJoM5YUO11rjKqp3teUlL0lxaqXwXRoRhIpTR45pt7DSlN97mttny4Oeak+b0sUh34+1Y6PYXpUvQyhJIr898/9Pu9/+b/TEBUMGfYK5wn8MGap6eGYBs/NN3C3uANqehtYK2EXmr246Wwln1gr3d5xC6w56mt94e34ETJWAqATdNKFl4E0fjg0teIPT624kVbkCZGbat996UKbVxwoc2yymBL5iVn1vKKpjplCcg5mFfmP/7cn+ce//3T/TghcpcaFSKyVbRD5V94LH/hxWotfQSA2bX3veIojCk4wlMhXW35a4N4KUkre8eV38N6vvZcvLH2USt7i608qy02efRd/79o/5QGn35L8us6TTJrq7mzfd3beThwuKrJsxzdSG8WP4t6kNkvQTQ7MQnJblcPA1QdwkrVOlY2+vfT6FXlJdmnKApEzxo1lddvoJ+uAjhcPqNdKiHzRXeFg8SDHJpQysGP1+6GqPApg6WvrftzwGozrAzJXSTs7iTzKdnkbiry/2Jm3DUxD7Z945pWcMJaJ3c0vBmvuGlP5qZTIhxU8L9Qv4cSSB/LTdEW/taIvNkEc8IdfucZvf/YSf/ORo7zq8BgTRZumGxJGcWqtrIW9Azu2Cqm1UvfqtJN1Vsecq0wWHfK2yaRo0zHKVJM714kwJG/lVTqjNgcTJ+kGPkiL1x5T5Kbb21dbPl2ZwyLEIsSkuIG14oLh9TL1UqaKfLW7ymJ7EV/n3wdIZ7HeRUqlVP0o5qvzGxeM41jS8gbih7WrvSL82sX0x7Pl4Yq8loxxmMxPpvFZTeTaWskld3ZakWftMp1qWZcjT84fnfTSRC5Mda589arPtyRKVC8ugaXea66T/O+rz1Kv27nUdPnS5TWeuTqwT8Ku8sdB1a22Q+TnHwegM/+kev1NFjRuddT5fGTKWOeR6322Xe/87I2zzDXmqFgFnm6/h8NTESen1JJu4eplAF5rXO77m8Pdl3GT7bsbZq3cNhwsKCJvxdf7hmZp28AxDTrJ7Vkxydo6GHjaGwySCYHag048uk6qyJOGhrhNkwK1uEgxbnF6toQnm4xFEVbSkWjJcXxD8JJf5Uj5CEcm1AEZ+8p6GUrkT/8u/PpbYO7zfT+u+3XGjYS8c2MZayWg5Ay3AbL41HOX1Rd2kbYf9cfaDqiCZ6FxftPnqLrVfiIfUvC82JrngSCg7Iwniryn2rWnenZumX/0vmd49OQU//y7vw6AyeSWU80Ub4BTpu71FFJk9hT5fLvXxVpwFtO/nTbbNEWFtUQpTgZq+Sw/9omql2DyJG6oljY7PK4+C+2Tr7Q8uqjnyeNjxIWh+3Sloy4yOsFD0FGLHxQmWXVXiWRERxdCB2wAXQD77kdUlO7s5Y198rYfImVmYFYcQ/0aPPA29f1ar+A5W8nR9iNCv9OnyKtJrWIyN4kbqPgsGSLvU+TDUisJyWb3gx/56bwaHdvTtSiMpOcizvPNDysi14tL3OgosTMdqouAFfnYhp0q8k+9pLq5F+rdfntFK3JQRB60N5/1Hvpw6TNQmKLVvkHJcDZ+LFDvqG0/MLE+R94NIqTcfr78A+c+QAWT/7q4RISLP/6HWKbBqekSZlONmn4lF/v+Zqb1Em4iqEbWSgZTziGkFDTCxb5ZK2EyotUyRErklWRMqo2JKxMi95MJgUItUqwV+WCx0w7bNGWRRc/hkOPxtgdnCUSHiThOW8qNSBH6ednlSPkIRcdivGDTaSs1OKwpKLz2JEumCX/+830ea92rM5acbP3Wirctj/yLL6nb8i9ec2l7YWoRARgHFZGXaxt7gVJKZa3kJjcn8u4NTgcBxfwEHWO4Iv/y1RW+55EjvOvH35RmanWDTbUTqPhhEj1M94uZT33+hYZ6L04sEc4aE6XkOUSHBqW0cWnK76RJAK92BSZP4UU+BSuX5q91cmWt7eOhs/k+MsoPnUle67bS9+JFXtoM5OZ6d0Vt3fUy0N05nxD5645OcHqmtGnBc92clfYyRB6c+EtKZKz2EzmA77Z76hWoJtbiZH4ytdP6FHnUW8xAz0bpy5Hr6G3m2Ooj9Vi9H63IpeEipUnByvHmB6bSx5WdcjrCeVwGNCwlZAqZdTv1nBMp4dJK5lgOXc7lHD47/9ne3chmqvzqE4rsv+Nf07Ycyv7mqZhqS237g9GzVMIqUdw753S/RXcb+fK6V+fxucf5axT5ulYVp/aXWeWLfG7+czx4sEzZVb0Dp4KMWApc8rXzuCJJ/YwWlughlhYymKCeJfI+a8VIF5GoJAsrOJi4yYhZgnZvdZU+Iu+3VqygSStR5KdKAWN5C1+4TEQ9IicaT7fraFl1ph0ez7PUiNTwrAFF/tn5z/L9a5/lO44fYW3xy/BCr6BW9+qMJ+3N5Mp91krJLuFG7oYLA4RRnKrZ/+Oxl3jpejO9IAHYM6dxpc1Ya2NF3g27eJHHVGGKcUe9r0EPuRN0mA8anA4CSvkJ2obZp8ifvqZU2UMHC7zjBx/paz1X86xj6l1fFTvzY+mIWoBA5FIlNr/6EgBf77kETiONc43RoibLabOSU6/y2FPqNbteAyZOEsQBJSfHVMlBiJ4i19YKwJQTEobDJyBW3d73a921lKxXrV5RslNIRMEA4VyrdRECDo3neePJSb58pbrhYh7r5qzUk8VDJk/C5Kl+ayUh8tDt9NQrqssYFJHrbt680VsVxwu9nrWiPfKkEB3FUaq8s9aK/rriVPB0nSIh8pguMsrz1jMzfZ+tmmeiLuhFKbkQKELOmznc0CWIYj5zboXXJ3WLC8sZKyd0+S95wS9+4Rd7SbLNCp4XPg6GBQ9/J+3xo5Tcem/fDYGOYz649If8gPmpPtLW+2yYIn/y+pN8bO5j6fd/fOGP8WOfH2gpvji49CCT9lF++Ylf5hWzZWZjdcdxrHuu9yRLLyBkNLJWhsEPY2J/mjVvsd9aCXurmXQScpkoqUKEKS08EiL3e+taZolcR/aKjgVxhBG0aVGgSZEZy6WStwlNj8k4Ttuj47C3Ao0eI3pkosBCzeVk5SSX6pc4Vz3Hhy9+mJ96/Kf4qcd/iusEBEKweOAhePwXIPQJooBO2GFcfxx9qZUgnSWx0VzltY5PAReJIDbyvHi9qSYfJnBsm/PyKJObEPmqq6yEydwkJbuEJax1ivxy4zIAZ0IoOhU6hpkq8jiWPP6iunC96kgRw+j3Ld/x/P+OM/MXSoUn63XWM8u4eUa+Z63UL1GJYl7j+bTtrloJSErKcYvVuMiau4YFyLUlvragPj/XEMQTJ4lkQNnJYZsGk8XeItorbY8gsRlm85IgUPs3e7GSUtLIEPuau5amclYz76frJEXZIYr8YCWPYxk8emqStbbPxZXhd1K9EbbJBaKeXPTHj8PUmXUeOUDod/sVuSEpxjE5M5cq8ocPKTJc6zY2tVb0nYgpJc3MPtD++MHiQQKpxj5owRTRhTjPt77yQN970QVPgGIsmYuV7VIwHNzQ5ezlKk0v5Me+8QGEgIvLmX0SdLlhJO32msg3iyCe/zgcezPkx2iVpijFMXzpnRs+/EZNXUjbwmBMdPr8cH0x7XjrPfJfPfur/PQnf5pf+dKvEMYhj738GK+deQ0P11QR+jhrvGX2f+JK8wqnxz3GRIcbcoKKvwTJ5Eeuq8aurhAICSYbD0e7Wdx1RO6FEXEww6o3r2JVpsCPYsJYr2Yi6CQENF6cwhCKyF1N5Dq1Auqk0DnyICRnJQXC5GCO7ArF8Rksv0E5b+GbAROxTGeFR0Ev9nS0cBCAIxN5FupdTpYO8fzq83z/H30/P/OZn+GZpWf4J6/8Ef7zDXXlXv36vwXVS3D2t3pdnfq2zyn3WSs6J7uRT77S9CniEZkF3vmjj/YtBABqybDzHGeqc3Ho30NvzspUfgohBGO5sXVt+hdq6nb/jFlUw5ky1sqHn1tkqa2+juT6E2PZXcDILyTWihphq1uaATyR7yny1jxHw5ATQUgs1Jx2gg42ASthUXn5wsb2ahDbyd8L1pwjSBFSySnlM1N2+hS5k1MX8Jl8hO/n1u3Tth8R0UtZrLqrPUUu1GdjCpO2qYjPbfRmogPM1zocTaYmvjFJMzy1gU/eGFx4WWfIx4/B9Jm+CKJW5DLoT61UiZiMYoh7McvXHJlAxg7Xm/U+a8VJvORBIp+KIoJk6BX0moEOlQ4hiTGMIC2aS8Pl5OQU//Objve9l2z8rxjHiImTANjYdKMun3xpCdsU/JVXH+ToRGGdIl8WMd2g22vS28haaS3B9WfhFd8GQDsOKZcOwVPvAm94IX+hFmBi0TIMSvT78x0/wshfoR2vn8Z6pXmF6fw07/3ae/lbH/5bnK+d5wdOfVcqNo6JZY6OqwvWYVuR+8eiN6o/XlSz2Ln+LDgVWnYJWwourd66JefuOiLXirwTqYH4jmngh3Hf+oLdhMhLxUnGCjYCq3d66tQKqJNCd3Z6UWqr6Jzz97/1lTz68Clw61TyFq4Z9uwPwPUtHKlOxCOGOsEOjxeodQJ+qN7hf6u3+Tdv/UUe++7H+NQPfYofLZxS0w2B1YljcPpb4FO/QqOpOkbHtdo3zD5rpWIrxbORT77S8ijiIe0Cbzw5xe/95Fv4P7/zlX2PuSSOM+YvbbiqT5bIAcZz4+sU+cX6RSzguF2hYBXooJqlwijmV//8HJPlJM8/xAKKZIBhNWg266oVvXKYWidIW9A9nPQkWegucyQMKSWrO11pXkm3+0ZYoOpWmTDzFIMaUioi7wrBVQ4gRMh4XpHpbCWXKvLVlodTVIQznYvouOtnklfbPsLoeear3QyRS/WeHp56mFa8RFMW8NYReZejE+q1T8+UmSjanJ0bTkrp6kDaAqtfVUXuwgRMPaDuFJMI4lTJwTQE0h8gchkyGUcQdNI7ylcfHkfGOZbaDbzQSwtsg/FDXYScSc4brcSzihzAtnufZctvcagygWX200Z2+mBBSt7y9arbs9mO6IZdPvHSEm9+YIpyzuLMbLmPyOOgw5JQNk+YNDZtqMgvflL9f+bbAaXiS7OvUmLi4ifWPdwNIlZaPjnh0DEEZdGfJW97IYXj7yEY+3Df39W9Og2/wY+95sf4+bf8PC+tvUTRKvId469KH3NMrHBqQhF5MVbWzsfiR9UvF5OI5fWvwqHX4Nl5nFgt0H6rcNcRuZcQOcBcYw7HMtZbK8nJWcxPKWUa23gCFf3TqRVQ9kWmszMtECZEfurwIcrj0xB2yYk2oSEZI+OVehFFWcKUkoPJCaGTK4ery/zU2irfSZmHJh9SJ9L1Z5lObq9WvTV42z+FbpX65U8DMB6FvXUstSJPUiuwPiamsdLyKAgvvdP4+hOTPHiw0veYy6ZSSSy/NPQ5dNxuMhmbO+6Mr4sfXqxd5AQ2dl7ZL7EA16vzB1+e5+JKm1cdTYpy8fpcbhAFCKvB4asfVifeIz9MrRtwZCKPENCRisillCz4NY6GIV6ktnmuMZcS6g2/oIqyVokiXYpJEa+bG+dqxwYRMV5Qn8FMOcdKsvjtatsnV1CEM2VHtIcQ+VrbB6OnyJW1khB50jzzyOwjVP1F1gYGZ0WxZLHmpnPMDUPwxhOTnN2g4NkaXOatfk3ZKqCsFUiTK6YheP2xcSK/2xc/rMa+qtn4PcvgVYcrEOWodpt9ijy1VhKPXDcDTUeK2FrpDHL1/8GSInInQ+TNoDm0+UYLjZwwsYTBwWOvUI9vR1xvNDl3o8W3PqzsmNOzJS4ut4mTu89a6KLv37p2Il42UuTnP64KoodfD6gxtqWJk0r0XPniuocvJM1ZRWHTNgxK9BN5tdvCsJpgL/UVQfVi58fKx/jBh3+Qd3/nu/mP3/YfKSbbFWPwgLnMkTHFQ62WssVeio/hVk7B4jPqFgjAGQAAIABJREFUbur6c3Dodfi2TV7CM1dvYmnELXDXEbkfxkhfDay60ryCYxkEGWvFMQ06iaooFqfUggSxrQoOek1A3dJrFdLOTr06EJAZszqWdoCasVJfFZEh8iCkICY4FEZYiYo4PK5OtLieLA6cVQqLz5I/8GpKdkmpvWNvBqtAfeEsAOOB31swWAiVXgi99OTZqE1fK3Ijt3Gr8hUrIfKlF4b+XhP51NwT8Ktfx7hdXjfK9mL9ImciID+ulvQC2m6NX3v8HK8/PsFYUZ0kYdRvrcQyJpQhhtXi1QvvU/NfTn4j9Y7PZNGhnLMSIu+y5q7RlRFHRY614ADFOOZKhsjrlFjtrlFK1s78K6fU/6u5aZUaERFTRbVtaf4aZa0US2o/Ttohrc4QRd7pKXKB6FkrpsOq36TiVDgzcYYg9rlsFonbPSK/0XAJY5laKwBvPDXJxeV2b8ZMBuuWeatdhQlN5KfV/5nkytsemsWI3DR5A1CLPKaiCII2rUSRT5dyWCJPw2vhR/56Ih9U5MkdZuuGsgP0Xd+hohrEZVm9bW/5rT4/XEMr8iKG8rkTQVEQFpfW1Of2LQ8f4GL9IlPjTbpBxPWGev2luJc66coQcuPDi51xDBf+As58KxgmUkpaQYtSbgyOvEGlWQagu2zLpk1LiHXWip7xbzgrtL3eBetqU9lcxyoq6vz62dfzDYe/ARoqFnvVOcNJczW9e603F4gwWGKS4MBrFZGvXVTBikOvxTUtCsCz10ZEnsILY+JgCoHgSuMK9oC1YpmCTtBGSEkuP8VYwSKKbVzDQHrtJLWiibynyNt+RCFV5AmB5SqQ3O7FoUpHlOlFiLp+xEPOt/J3anVoqd8fSYjc1IsDX0iIXMrkVuu1TOenFZFbDpz4BhpLqu19LHB7ihyU0oj81CPfWJH7lISH4WxM5FXrAK5RgOUXh/5+zV0jb+YpPvcH0LjGuOH0WSt+5KvCThBCYaK3TV6DhbrLj/6lk2kxdtBaSb8XMUX/Arz5J0AIal214nglZ9GKcxB0mE/yuEecca4EE5wIQq7ULqTpGJ1asVH76VuPq+24xhjXqi2EiCk56jOaqahVzFteyGrbo1RWcdFxO6A+ZCZ5teOnTS+HSod6ijzJkE/np9OT+7xVRGQSO5o0tLUC8KrD6vVeur6+ttF0QwzRS0lRv9JT5OPH1EU8U/B820Oz5AiYz7hr1SiJw2YUeTFnkk/WqRymyNOMeLLts9MPq+25+gX1f+KRH0ia3vqIfIP1MTW5F6WA0mx613B6ogQEHJ8qcGa2xM9+5mf5TPX/A3rJlSXZO1Y6QSdpChpirSw9D+2l1Fbphl0kUm3PiW+AhafX5c91HLRi2HQMI7FWeiLjRkedo8L0uNbsLQN3LTkG9WedojEPwuBZ8RCH5RITSbqr2r5B0zlAjIE4/HrVnJbcZXP4dbiGSUnGvLDYwAv3dia6xl1H5H4UgbSYLRxMFbkXxQQZa6UbdihKiciVGcvbBGFSEGsvgYx71opdSFMr3WRRCfXA3sIHWpGHoSqIFOmdqB0/4lj5W/n+Vjsl8oPjOUCSd5eVF7/0PDRvQGNBKY1Dr2OmMMNKsrILp76JeisZj+t3+4ncctSsFWdrj3zMDBCbELltWSzaJzdU5FW3ymRuIvUhx81cX7FzrjFHLGNOux2lyJOuWbXykuTIRGFdi75GtpvwqlWC1/0QkCzjVrAp5y1asQMyZiGJbB7OzXAtmuREEPQp8prM0w5bREmG/3RBnRgX/CJXa4qUdWFPpz3mVtu4QUylrPbjuOETRWqQVB+RtwOE4VHAYLa9xurqy+oz00RemOZ4RZHtFdvGzqyopEkju0TcQ4m9dW5p/QVYzVlJBiy5DWU36QWIDRMmH+gj8tcfmyAvfC7Xe0uodeNAFTv9dm9QmmNRtkt4UbcvfmgaJoYwUmul21BkNT31EACtZFZNy29RsAqMOWr/mgmRSylpB+2hq/Foci/KOCFydWwcqxTI50J+9C+dQiK5WLvImq/uVHVyJUvk3bC7cXenbqA7/c3AwOTD42+BOMC78hQ/94df5epakn6qdTEEjBkGbUNQptsXP1zp9ubGX6j2BpVda15jKj+1fhhXYwHKh/iaN0tedlVNC6h117CnTvC/fMMJiieTgudX3qtikrOvxDUMijIiiCQvLm6xQMwucdcRuZfMAD5aPsGVxpW02Olni52hSzGWYKphS36kTmwvIUyS5bCk6aSdnW1vvUdOrpJOvev6SiXkSLo3Y0k3iHDyJWXBJJGjnGXyQCnEij141Xer57n4yTSKxKHXMV2Y7nUOnvom6oaBgaDstQYUudOXWtlMkZeNnkc+DDnb4LLzCpj/iuqOG8Cau8aU4aSJnXFh0Qk76Yn/clU1E51pNyA/kVorHQFFPGYruXT7BqcmZr9/PP8GyFWIYknDDRgvOlTyNq1IXWyvJRHHqfwhbsgpToYh890lwuTkruuFQ3xFNDNtRfxzQZ6nr6l9qke2ziRpD62IC1OHwS5yILgKCMr2WJ99pBS5RzkKmWpXWVt6Dr72J5CfYLWrFPnh0mEsYXE1X2DMW0zv6LQiP5JR5EfG85RzFi/fGKLIvTDjjyeJlYlMGmTqdJ+1YhLjEHK+GiGlTOfgTCbWSscPEQLytsF4vkxEV60TmZnvYRt2Ov3QSwqps+NqsZbW6ksQBanq1secmcxX6YQdYhmnfngWqbUShVA+kEYkCwhyTsRPfNNprrev40Yuy90bVHIiVeTLZFIkYWfjUbar58GppCsJ9U0+PP4NAKy9+Gl+94kr/MP3PU0US+ZrXQ6N5SkjaAuDkuj2eeRrfk+FX6pfTr++2ryaXrD7UL9GVDnCeV/Vkaz6NSpOhZpfpzR7kl/+3tdiHFWFXuafUhailcNFr5gkeeYW2St3HZFrwj6RjIp1LNHX2WmbQi28DCAEYwWbbqBOGLelrsArQvK2972NLwi/p8iD9amVrCLv+IpIHKlXHFLtvcVkOTWtyAG+rpIQ7oN/VR2YF/5CRZEQcPDVTOWn0tw2R95A3XYYExaG1+6tYwmKyKOAvJkfvkrQy4/D47/Aj13/lxyNF3tpnCFwTIMvO28Cvwlzn1v3+zV3jcmgV+jT6Rxd8Hxx7UVsw+a07/Up8o4hGKPNbCW3tbUCfFw8CChFKiVKkecs6pG6iC40rzERxQjnANflJMeDkFDGLLTmkYZKDgEstdV+KlxXttSKKNJ01fZrG0Er8hcTIp8uF+DAq5lpqzx9wSz3KfK1tk/O9inFEdOzr2KtNAmHXgOnvjFV5JZhcaR8hKWchUmUku21aoepktPXUSuE4MGDZc4NI/LsCFvd0DKeWZpu+oyKp+opiMlxuuoavHSjmc6On0yslbYXUXKUwp8qjCFMpViz3YS2Yfc88mTs7MyEKqy24gDmv/z/s/fmwZJlV3nvb58585wc7lzz0NVdPUg9qkGSNaAJ1AJjMRjLPMA2wsgPG2wQGDuEp7D9HI/A4Wfww88mLPMwOLDxIAE2qGV4IBnQQGtEUo/VNd+qurfuvTlnnnG/P/Y+J8/Jm1lDq+RQR2hFdFTXrcy8J8/w7W9/a61v0Y/6BM4UyA1TW9fqXII/hyzk4F5P4goj9+RUiz/bVYw3kxlHNyIF5FnKtXJ9fjJebJy1+6Kq5hFTv3XQuwF/BVbuwbuirIc/dX6P9//Bi1zeG3N4qUaQSc3Iq8nObrxFFreQmcXF0vyAS/1L+2UVgN4mQ29jOgu2c4Elt81eOp7upurL0+t44EH1vYSkJjOOB3K/18wdipcdkOeM/FT7FL2oh2H1idOZ8sM0VIkXlPF7qKepT7QN7aVsQpiGXBRJxTRrf7JzCuTdcA8hJYZUN3LFmyXYmDYBAHfXNOA2D8PJb1CM/MrnFMtyG6zUVuiGXcV2LYdusEYzTXR9dWnrajqQhggh9jsgSgm/9n3wRz/H6ehpLtXuKySLeeFYBp+xHlbla889ue/f98I9lge7oGuA85r2nLE+s/sMdzeOq5qdkkY+FIJVa0zDtYoZivuAvDTMIO/BK8/GDDyLbqKBvHeBQ0lMz15hmzbHtKZ4YXiVzGsXAHWhq86Tu6mSdJEXgFCvnTJy9ecUyB3YeAXN7jOAxDMblTxAZxTjWiOCLGPZW2EvnZD9lY8Svulv0Y/6rGj74qONo3Qc/Z10zuHS3rgiq+Rxer3B89fmSytFrX9Hg8gsI08moEtT853jBIePPrddlIsupSlEw0qyfj1oIszpmLc8HNMpAbmSCldWlLTSNwx48fdVQtNu7APynETcmJFrINe/00PdC0mWFM1kAGtLAyWtJBO2LRNbP6vjZAz+qtLCSza+gFowV04Vf82BPN8ZcuzVBFufRpBxfKXOP/3wczx9pcehdo16luqqlTHjUlJzkG6TxUtk0QqX9c4uTmOujq7uZ+RSQm+TjrlWAfK25dMVTIEc4OBDlT8nMsOTkq8/aHyNkeeRM/JXrj4AQGJd3CetjLKomObd9CxiqW6syVDp0ttalxsIKl4rNbskreT13BrI98IujUwy0p+VZ79rtqkY+XAK5CcczfIaB1SWfXBVlU7pFTofE5ez8l6tSSuaKJ20opG7hQzScGb8VsIexCOyt/593hj9LP/14X8Np79p4XlzLJN+aquF5bnfrvi8SCnZHe+wPNyFV34nAC39792oi5SSZ3af4b5A36ylqpWRYXCsHiORizXyXNICYrpESVY0A7XrNk3PoqN3TZeHmxxOUvaMZVJMjljq/J8PdxC1JYSlfsfesMbEauHqjsjWchP0CLqcka/4LoaAZ6+q67ESuHDgQaywwwZ7OCKoAPnuMMI2x/iZZMVbIZUp3bCrWvWBldoUyEdGl0QKhpfVjqBcQ16OezYCdobRvuEF/Uky9VnpXlSLtl/qmJytXNHVVa1mg48+d33KyNMM4iHDKC1siw8EU+uIMiO3DGsK5CP1LPg1Nfd00FiHsx9R0ooTFNc3XxByEnFDjTyTWlpR56Gub7FJMuFs9yyG9hwJgp4axjAcsGWaHNVJw1E8gtXT6pnslHyK0lgtdvk5KR9Pnnw9+hrsqMNd4gr/8J2vJHAtepOEw+0afpowMgyEkESTaXf0OLuOjNtk0SpXR0reujy4rHYNs0A+6UA85JpYoYdP5rYUkBuu8rxplV5/UMsr+nmfkOJlGY+uGZzZHhQVS3cyXnZAHsYpQsADK/ciEETmBdXZWZZWspiaoYG8ZhNp3+twrG7e65lm4UJAEiKlrJYflgHV8sB06EQ9ltOUfqaAfFT2L59h5Ie1/3DPXoG73qx+mIyLC5szuxzIu5ZDM8sAuV8j1/qyb89MCRooRjWyV0gzWTTWLArXMtQkpdNvV97dpXryUTIiymLVXJIDuW5c6kw6bI222Av3uE93r+K18bV3+sgQHPaiygCBWY08vjYdRmFYPbrjuCjJa9VU+eFuZJEBm2GHw3HCDio3sRQcxEdwIe4iakvYNbUoyNQnqy0jUE0oB5cdhKGOOWfkpiFY9h2u9RQYrfiKkQPcb5zHol6plVflhxOCLGNF11HvjHeK61Rm5DFjvig2uPDsZ1Tt+wIgLxKeM6x8UPYi71xUuzej9DiuVGvJc0Z+14FVPnl2l2u6hj2XVkZhQl3fvy1vCrb7pBW9OwonU4/swA7Yra+TnP8EvfEugR1gCAMhXRC641NLKzeuWtHJTsME08HTrHqSTjjXPccDyw9gCQvTUQvjpWu7bFkmx111XsfJGNZ0I1u536FzQc0yXd7PyIvjOfYaAB43nuPIUo1//G2vBODEqo+vB0GPhCDVXjpplhLKPVxWyaI1dsJNkiyp1JBXQo95vJi28WwDsXRMATmCrmFUGfkrvwMe/C44rBKfkyzBk5IHltXMhBvZG7/UePkBeaKmAPmOz8nWSSbGhf2dnTKlruu9y0A+0czqum6GGCEhmRDGKZmkmuzMAVWoaUJ7cZ92ltHPJ7CUTbaCdcWQtXvfqtxjTwZcGQq1XV5RDRJ5I0PO7PKEZ1cmtNBa4T4gVw/SvrmdemvcNRTg3QzIHctQu5bTT6gfPPeh4t9yxrnsLimgs+u09MiubtTlmV0lH9xv6867MiMXBgfccDqo13D2M3JdKWNgIuwe3XFUjH1r120C16abWFw3TSIyDicJVzP1u6zWYY6l8Hw64u87Y6zlj5AO70OmPmagdjYeJoeXLH76zz5QHEMe+XkJXAvPNgsgf0BcgKy+L9mZiQl+lrEcHFTnZrJbXKcyIwd4sX0Ua+dZnt8aMImzSg15HlMgn5mLWdHIL1ZlFYCmLkHcO6f+rhfK00dWidKML127gilMGpkyTBtGU8fLcrVFWVopa+Rjba3gWi6BE3DOWKYvPYa9izT0ztWQbtEglV/fG9aRZ1IBOYBdw8umFTZne2e5q30XB4ODhKjPP7u1xa5pcqKm3jNKNCOHaplsXr1TYuRF1Uqu2a/czcRZ4uuMZ/Fsk29+8CAf/Guv452PHMLX9/LQMJAayLfH2yAyGtYaWbRKRsrmYLOoId/HyDWQX0pXWPFdZUPQOU87TdkzjSIJq47lFHznvyl2JpMspiYl9zQz/tYT93F0aXEu66XGyxLI89FT96/cz4gLVfdD02AkU2r6YW56NmGmE5T65r2uPZMH2n9lONJ6WznZWQZUr00nGdNOUzqJ+tzxrEYOhbzSTne4JpfY7GqWekp5Q7ChWELByHMgj3rFsIpKstNyCmlln0auf9e2VK9fCW7sy+zq6h5ah9XOoAzk2kt6+cAjauFym7Q0A+yGXZ7efRqB4LR21qPWxjEdLGExNARr9qQ4trbXrsyFBIiuK3a17K4jrB57o3iqkddsGp7FGJdN7ah3KEm4nDRpuBZG6zDHopA/NhM+ILvUR29ndOEvULMtnKYCAM+wmaQT7tlQD07ejg5Tn5JimLLXgtYxXmldRKb1ojJHSsneMCYRIUEmWdbJrp1JiZFrID/WVMms6MhhjnOFf/TrnwOYy8g3mi4Nz6oAuZSS/iSe6epUn9mZdJRjomFA+9gUyPX1uPvQGq5l8MLOVVpuC8N0IRowitKifLYM5BVGbk6BPIz6WKgRcIEdsCfhW8J/Qt8wCM78Pvy39yKkhzSqHizzGHlgB6xbAcfjWJEaAGsK5DvjHbZGW5xsneRIcIROfBVDwJltJYsdran3jJOxqhJrHKoCeS4vzQPy/LsKwXbrYV4lnsXT+PDI0TauZeLnlWmGQOpdbd4MtOxukEXqPjrXO8fF/kU80yvkzyJ0QvqKVFYDtBUjX4pDxoZBWDIzK0ecxSQyxcskvhzyQ286xdHlrwG5YuT6gb9/+X5CdplkXeKytIKkrrfXrZpNqCtNQq2HXs+nhGsgH98EyKXXZFPGbKQpHV3KuE9agUJe8cNttmSbKx1dBfK6H4Vv+1fQVCyvYOSTHdIspR/1abVPqteWNUjdEASK9VQ0cv27rumJ8Wu3wsjzUW+n36E64XR1wJ5uBlk+/gb1716LIBxiCpNu2OXZ3Wc51jyGnzdc6LxB3a4zEgar5qioWFlylyrJTYB4R5UubtQPIcwhO4NxAeQtXUc+xuWSHpRwBJsrY4vlwIHGQb5uOGA1Tfn51uMckt8OGNy15iO0W55ruhWb33mMvLLQHXgl9xsXSLWVbTfqMoxSojQlJMaXGSstlfStMHK9AOeWxbuNAEekbJ5VO44jc5iWEILTG9WEZ5hkxKlUgJBE0L8K7aPsTnZ56396Kx+59BH1wvZx2NNasWbkjlfn8FKNXtRVnYVOXVetJKqCihlGPlN+GKURTHpMsghP2xsEdsAoGbLJEqGA4MAj8NT7cTOQojoObh4jN4TBh458G98xGEJdA6Bdo6bb//Md3cnmSQ43DnNlsMmx5ToXOorlbtTWqFk1ZZwFsHbvfkbuBNNFQh+PZViVa3258RAnjWt4UbV80dfTs0bCKAoZNgf5794oOsXP984XFSv7pg71NkEYbKYtJcG2j0E8otVTJKgzx7sfplU7npQV7/47HXcEyIUQTwghnhVCvCCE+Nt34jMXRVRi5A+sqK10KC5UpRUhqekbuFmziLSuPdGlZtf1dnqoXfomYwVCc6UVoOs26BtwPE7YjXMgV++tOeZ0O6lLEO3RNbZY4krOyFuH4ZHvLj6vZtWKNv1BPEAiaR14WJVt5TIMVDTywJ6Z+j7cBgSboXb0uwUgD3Mgv/cJ/n2jzjd84Jv56Sd/iM889f8AsHRS6/leExH2aDpNelGPp3ef5t6le/WNKFQbNeCZdbqGRdsYF4tM222TyIQs93/vXSHW0o16QCSX+lt0xhEN18IyDZqexVi6bGogP+itcK0fstHwoHmIP9cf8HsXLvPG1uliUMXd60Fhe1qz64RJWGjzuUYOU0a+4pfOz8YrOSY3SUP1Wb2ox94wAhGTIQmyjFbrGKYwC43ct/1CpvAsj/X6OpeE+o6nDdW6PU9aATi9EfDcVr/wJu9PEu4VF3hk57fgyfcBElpH1Ri5LCpAhqUT+xg5Vo12zWacdtWsTicoTLNyRp6Xhs6ei0Ja6V1mIgSuBsHACQjTYdHV2jj8deq9mYFkqpEbwljoqW0PdxBua2qza9eo6QX96V012vBE6wSHg8PshXscWTHYGisyslFXQD7So+VYuw+2n5tWruyeqZQewrTLtAy4F31VJeJtfW56YFLi64HQA0MgZhj5Wv0AtghwhM+5rmLk80sPL0NwgG4olde/ru5a2j0HsM8pNI/cVfKrHsiFECbw88A7gAeA7xZCPPDlfu6iCJO0APL7llViJLYuFkCOSImEoK71qaZng1Q37ESzmi3dkbcT5e3KGshdk1/8wi/yTNKvSBwXHPX+Y3HMTqTd9uZJK4NrkKWIgWrZ3cwZ+ZzI2/TzqonW6n3wvk1YOz19keUUDSf75nYOtqC+wvYoxTJEAXCLwi0z8oOP8nm/RT8e8B+u/E/+bV1Pmmlonc9rwaRHy21xqX+Jy4PL3L9yv3IgdJtFUs42PHrCpimGhbSSTxcqdPIrnyXSD9vxlvr8K4NrdEcxLT01KHBtxjhcti1WkpRacIDtfsh604XmIYpHtbY0BfK1QJWqAZ4TME7HBZCXpZVVzcRXy4x84xWYZDRGOikd9io+K4HpYZg2S95SwchzNp7HscYxLsY9QPAtB3qsNdyF1+Ce9QadUVzMD43OfYIn3b/N677wd+HT/055hRz/U0WTT25oxdIJVS0x3iuqq7A9luoOkewrgzPHh0l3oUZuMAfIu5eZGAaeBuWG0yCS40IPD7RxmpMZha3vIB7ceD7mcBuCtenf7RqeTjI+vfM0pjA52jhagKRX6zJM1flfq28oRp4nzNfvU1YaeaPU7ouVRCeoCpfZzsttSzF2czjt6SAa4GuJZ2gYGPoZujK4gkzrNN2AumPhG4c41zvH5cHl/YlOUEDePMQwT1K3lRTW0tLnIiDPv5MnLDUZ6ysUd4KRfz3wgpTyRSllBPwH4J134HPnRpRkhdF9w2kQGAfI7EuFtJLoOYM5K/FsE0fPwpwIg4ypQVRHJ0HCsW6esOCffeqf8Wt2XGHk5/XvO5ok7CUOcZoxLAO5vwoIBa7D6yBT4tr6lJHPiZXaCjuTEpC7rQrjACrSim/7hGk4lS2G2xCsc30QshI4+wY5zEaR7AQwDDqNde6PYn738LfxE4/9KH/14b86rcl1mzDp0nJbfHrr0wCakXegNi1ts0WNvmHiZ8OptKJBoDjOzc8Qa+A/qUu0tkZbdMZxMYszl1YuWxaHkwTZ2OBab8JG01N6aR4lID+1HigAbB/D85aYJJPCR6S83S4YeUVaUdVDG9omoRt2lfOhZqS+vnfyxq28Gagch4JDXB5dhfYxntjo8MG/9rqF5z5PeObySnfnBd7favDHr/+XavF+z+/D8smipLAAtKUT6s+981Mgt2q06jYJfZbcJTj4CPL8HxFGUVF1VQa4JJk2KBV15L3LhELg6esd2KoTNGfkgf6uTiamQK7ryxfGcHu6MwWw63ga5J7vPM/h4DCO6RQgadh7jOlhSclSfY26XZ9+b125El97mheu7CJnSg9hurCUY1f77zAs2QtPevi6J2JkCKxE7WovDzbJ4jaBa1K3TTw2+JPrf8I4Gc/v6uxtQvMQgzBRZZ46Ob2kn6n82s1GIa1Yta9uRg4cBi6W/n5J/6wSQoj3CCGeEkI8tb2938j9ViNMMlx76gm+Yp9EOpeJ0wzLEIy1J0etdJHzQQOhEHRcnyxPcsq8FEtv6fRw2TNmtQzwoiExpORInDDEYxgmjIuqFUtZztZXFJDrBg7ZPMiV7mJGvlpb5fr4elH+lntbVMK0Kxo5lPxWBlvgr7EziG4qqwA4pkmaSTUWDtgLVlk6/gaWv/Ef8xcf/AF+6JEfmr7Ya0GoGHn+cN2/on2fvSmQC+kyECa1bDBNdrqq2qRg5JufJWqo3MCRIoF4nc4oKuZ4NjyLEJuLlsWRJCGurTOKUtYbbpFXAKDWrkorJ14PP/oneE6ggDxn5EaZkc+RVpZOEBkex/ScxW7UpTOKp4xc68Ar3gq74/mMfK22xu54l2z1Xqyd5+YmOvM4fUBduzzh+f7N/8E/X17ied8Acwq0OSMvtOIl7VjZOT81hLI9Wp5FZozUonnvE4jxLg/L5wpGXi91+MbJ9Fkpyg97lxkLgacTl4EdIEWIMNRz0NA6t5fKAsj7cX9uDXkR+n4swvLw9G4yyRJOtlQOKAfy1LzORAxYT1KEXVfSSj4BS1eufOqpj/MDP/cBRJbwC18UvP8Pzhby1DAe7ku8DlObAbUqkIc96lqiGQgDK/e8H1whi9vUHYu6a+HI9eJen9sM1L0MrSMMQl3/77XAa9PWeYDOZD4jL4Dcrn/VA/kthZTyF6SUj0spH19bW7v5GxZElGS4JWP7NecUhrMdDpqgAAAgAElEQVRLP+opfVzrsfVSK3GrpqtWhGBbT4nJkgYTqR78JNSju4QGcttGlm7a8zLkYJLiACM8+pOEUaQkjWLCeF5L3lfg4K8c5fzOkN97ZlpfXo6c7VUY+WyU3BnzmzavHmC4VWLktwDkpfmmALthh6Xg4PwXe5qR60aN1dqqyuKPO4UbJACZy8gQOHGvWGAKRp7F6gHY/AyR3oYe8A+ANOiE1+mM4wKU1XCFlKuWydE4oW8pe9CNpqekg/zc1JZ4+Eib+w82ObFSTehN0klRLVPWhXPvk4OtUlWBYXK9fjf3xUrb7oU9doeqhhxQ1qjAcm0xI1+rr5HIhO7qSbj+POTWvVLum1azFri06zbPXRvwqUsv8juhqqtvtKujvxYz8nMVRl73QoSQNOwWnHor0rB5q/lplTylysjjeAbItbQSWi6u1vzz1wtb3YuBrqCqZZJEqmdjHnBWYh8jr1FLpk1QJ5rqu7TcFr7tE4nrJPaY9TQF26NulRh5fRmCDYzrz/KAq0D5T8bL/KP/9qXC/nYYDysLFqhBEh3RKkpz1Q97NLMMSxhcsi3sdIiUkqvDK8h4icC1qDsmZjJNpO7TyCddiIfIxkGGYVKcZ9rHpsZZC6SVXCarOcFXPZBfBspL2BH9s69IhEmKa08P+0BNJQevTs6oihVdYVAvMdyWZ+Nkaq7jjqP9wsNVEhEjgWiibqBUA3nXNNkpMaWL6YijWu8bSrcA8pozfUgKvxVtRvRn3vAqHjjU5K/8yqf4wxeqk2SAok0/l3nmArnpqq42pkA+ZeTb4K9zfRBV9d8FkecVoiRTpXaTvQJ094XXgjSipR/we5eV1eksI09Tm4khMMIuo3iEKcwCFKI0UtvR4RZxS8kjruliySaDZJfuKC4YeeBZCLtDJgRHkoQ9QwH5elMvUE0tr3ht3vbABr/9N94wXUBRyccyIy8D+am1gP/4ntfwjQ9sVL5ir3maR6TaSHajLnujCEN3MQauOi/L3rLaNYXdfUCe/327eVDV+uediB/+O/DPX1mxVBVCcHq9wZeu9PiRD/00Uo+N62bVHVvByJNSdVBtSQF5iZHbrgJX12iC1yQ8/FreZny6qLryTA+hH+1wBsiTLIHeJSaWUyQuXUO349vq9ze8JbDr1NOUFNViv8iLHFD36Hi3UlWCXaNWmnB/onWiOBdHgiOMsi1Ca8JamoJVqyY7AdbuY3l4hlc11PPx7W9Troe7Q3WN5y0skzilZ7RgVGXkDvC65Qd50vex0yG9qMc4HSlG7prUbBMZq2MXiKIqqQjtQx75h8gk08Hm7WPYQMPyFwJ5Xu1TcxrK5fIrFHcCyP8YuEcIcVII4QB/HviNO/C5cyNKs2LoMsChmkqCbEdnKoOX66VkZbNm40jBRAiu2+ohz6JVEJJQCDXUFkiZ3khn5PQmvBB1ORbrQa149Ccx46hksgUzjFzQXDnEL7/71Zxc8fnLv/QUnzxbNQLK61RzM6HF0ooCl2JKUDRQ4+riIdJfY3sQsha4/NQf/BS/+syvLjxvTgnIVSdnpDTWeaHPXUuPr7t/WY+4mnQKN0iAJHEIDZWNH8QD6na9OhtSW6PGDTWkwDEdXLHEKNtTXuQ19dqabWK5urIlSdiSarHYaGoWncsrtfnH61mKkecaeVlaAXj1XSv7xpONl+9nVQwILJ9uqIDcr6lr7NfUQrLirRRVB/OkFYDrvp4zuf0MXPgEfOznVXLy4icrr79nI+BPrj1Lz/oY3yoOY0nJ7oyuuo+Rw7RypWDkHoal7lNTKiDrHnsrdxubrOkdhhCCWm4zHE2/d1FH3r3MxLSKGnOpq7qEBvLACZBugyBPEsbDwkxrbuRSxgyQuyUTtlxaAVW+2Y2vMTFDNpIEbK+a7ARYu49D8XlOW1vgBPhL6h7Iy1bnaeQKyNszGrliwd9y5M1cs0yuubtczuXPuI2vGXkSqut4wD9QIQJA0Qw09BQZKBj58kkwLNre0kIgzztFD3nLX92MXEqZAD8MPAk8DfyalPKLN37XS48wziqMvOW2yaI2nfRsZV5nrQTkrZqNLQWhEGxb6iH3UBdlIASpllaibFoV8kKsVs9u2KWbjjkWJ0hhEmIzCBOGUVJ4WwBTRt7bVP9vWiz5Dr/yl1/NwZbH977/E/y9X/8Cl/bU78qB4UznDIEdYBlzJmxbOtkpZXVup946TtxloiQjqMX85pnf5MPnPrzwvOVAHibZvrFu+0LLJy0NiFVGPgXyMLKIDAmTHsNIMaTKbMjNz4AwiXWZoG3Y+OYyodwjzWTByIUQ1Orquh2NEzYTBeTrOlFZJDxLi0jlcG/AyBdFuqYKqwKhBmjsDWPqjs5HaI04nwAD7GPk+UJ8XedfuPI5+I0fVrsHYcK5/1l5/emNBs76k9jC4yeCwyxJMXXA1JGDwVwgj8dg2GCYhXGYyBSQbR9SDWdHtj5SvK1u1ZGZyTCamk+VNfKJYRRe5Vmq/jQsLa04AdJp0NTluaN4tHCoBDB1/ix7xVg1rGRSLKq5tAJKurgeXiYx04KRV5KdQLp6mjoT7p98DpZPsqTlwzIj3w/kGUNrqSqt6FLjNx17M7UMnmt0OK+92LO4je9Y1B2LSWRywD+wuPQQ6Dvq+xVA/tofge/5TwrIF2jk57rnaDpNltyvciAHkFL+lpTytJTylJTy/7gTn7koZhm5bRqkk8P05Ivgf4HfuqIM6Oslttn0bExpKEZuGpC53LOiwGFkGCTxGMcyGGn3PiElL0aKHV3Qgw6OJQmZ7QOC/iRhPE9aSUPlEdGYas9rDZf/8J7X8O2PHOZXP3mBN/3M7/Pe//hZLm6r957tnp0vq4CqIwc1t1PftP24X/isdIQCtqHxPBJZTLmfF24JyGcHLe8LLZ8ct1vYhs3Dqw+rxpV4VAHySWiRiIwEySjq4tt+lZFf+Sys30+kC2psw6Zhr5AZOi9QKtezvT1sCWtpyvmoSd0xpw/MyTeq/8z55X01s0aYhgV7nmXk88I8qLpsg1TXkY8imo72uNaDh8vgvZCRJwNlmPSHPwfXn4Nv/Tk4/Bic/Wjl9aeOdLAbX+LdD34/S9GYZYzCGiGP/LpUgLx9XHmxxKOi5TsV6jhlqoC14xzi2ewI61emYwUD2wdpF0OeQZlmRWkI8YgQCmkl1r0Rwu5gSAfbsJFug5bOOQzjIYPoBkCeA2dQkq/sGsSjYkhF+V47HBwm1YvEagYYRjXZCVyvqSqVtfEZWL6rqHDqjCLSTA113ietJClDq638zPMadC1n1Px13hBZfDEY86KWwWS8hO+a1ByTcZTy46/6cX7wwR/c//26ajJQz1L3QHFfNjbg1Ftou+2FVSvneuc40TqBqLUVkJfM6u5kvPw6O+MM15oCqGMaZJMjxOI6w/a/4beuf5r7w4hDzePFa1o1GyPTQG4IsqTBwaYCK9W2O8F3zKLh5lQc88JYsYwL2qf4WBwXgxv6YaKHNc9IK6AGSDSqScT1psdP/9mH+MjffDPf99rjPPnFq/yDD6rP3Qv3CBaVdRVAHk6rVqJh0Z6/q4H8WvR08VnFwIqZKGvkhQXqImnFU7uZ19YP8ZF3fYSDwcEpmyix4uFE3dAjQzCYdKnb9eqQ383PwMFHiNII27CVV7a3qqQBkdCuT5mzYe+wkRgYwuDs2GWj6U1rlh9+F/zF35x/rFAwy7xpZe7uZiYa7VV2ZIMgzYpkZ80a4mQSR9enl8F7lpHXdaXF9fF11YmYjOHh74Z73gYn3qAGC5SSnluh8gv5jtN/BiZdloVd7IzymAvkSycgi1Wbuk5OJqjPjSMFxMMo4Xezx2hc+2OVkAYCx0dKu+K0pxi5YrRjskJaiTSQm3YHQw9OSe2AJakWxt3JLolMFksrOSOfqSMnS/AsTwFZqbS2zHpXMnWtcmklr0o5b5QtfU8Vu7e9UVzYJc9j5CNnGbKkGA1I2FM7JMfnLanP2JT8xov/FUs4yNTHdyx8x2QUpTxx8glee+i1+79fbxOCDQax+g6FRq6j7bYrLprlONc9p3YjXktdx2RxJduXEy87II/SrJLociyDaO81NPrfx2r3J/j4qb/Er21epd6emvQ3axZmZhIagi0ysiRgI8iB3CCLJ9Qdi0E8QAAPhRFnBpeRUnKhdwGBSsIJ7RXen8SM4tKMT5jqg8lY2dfOiUPtGn//W1/Bp/7uN/IvvusNxc8NucB7QQMUyczcTt2ev6XHnZ0b/knxUL7YfXH/51CtWrm5tKLOjQj70wRX/mDof5vEKZNIA7kwGOkJ64W0MryumNHGA8RZXAD8qmaywuoVDyeAtHZYTS3w19nqJ1NZ5RYib0PvR/1KDfmNol23uSxXqYch5zvbXNob45hDAplBfZrszGOWkavvssr10XXlvNc8Am//J+ofTr5RgUlpsntuztV22xB2WTa8irQipVwsrYDS4HXX5CTrIlOH4SSflpTwO+ljCJnAC78DqJyKmGHkZT/yUCZFp+p4os+ZEYPWyzOnwZLOOVzTXjwL68hz50+/qpED3Ne6m1cfeHXl5eWGm2U57UaVyKLK49zIK3yEWL4L2zRouBa7w6hg7rMLyyROmTj6muU6+aSnSomF4FFjmWYCm8OLNKw1QOC7FjXHqgxl3hejHaivMgjVuQxmgdxrF4twOYbxkK2x8pgpigS+QvLKyw7Iw3ja2QkaoLIaWe9V+OIu6pc/rW5+f2p60/RshDRV+aFMkEmDQw0tSwhBEo6oa0buGw73RDHduM/OZIcL/QscqG/gShCOj2UIBhNVR1635zBy2MfIZ8OzTf70QyeoayvYLF0A5AUjVyO7LGFVNPLNJAARcrb3LG8/8XYAXui8MPejHFMda5RkxTZwobSS5xfKN13+/1pa2RlGyEyB7cgQDHWnXcHIO+fU61fuIUqjQrfe0KVthtWjraUVKSWx2MZJl+DYq7nWn0wTnbcQuUTQi3qVrs4bRatmc1Ws0QxH7E26eLZBw4vws6xIqi7rpGdNa7izsVZbU7NX3/AT8Nc/o8rmQI0eM2w4O9Wse1EPU5iqUW3SY9mqVxh5P+6TyqlbYBE5kPcug/6ew6SHTH32tBXwIEz5rLybrLYCz/62OnZ3GUPW9zNymZACUZYUC+B4UjpnWc7IG6xoRp4D+WJGvqXGsDmlc6QXiZ9/7T/krz/21ysvPxRMm7ya2k00v4b5d7+4N+IFqQFfNwO1fZvOKCoqQeaVH0Y5kOeVK2Gv2GEaToNvGOiEtqnwwXdN6o5ZMd7bF9EA3IBhDuReFciX3CVVQDBj33y+pySc483jXwPy2djHyLVePowSbEOoaoGjVQagpBWLiTDYJUYmDQ631cM6NAw6vT5116IX9WgIm1O6df/Fzotc6F3gWPM4WDWEE9DwLPqTRM34dBcAefPGQJ7Hqt6uR+EC0CpJK0IIfMdX8s9gC7w210YSq36BVKY8ceIJGnZjoU7uzEgrruku9M0obrqSxWs+/DiXVrb7IVI/hCNhMEhG1K2StJK3V6/eTZzFBVM+oncrwuoVLfp74R6ZCPlC9jrkd/0S13qT22PkGjR6Ue+WGbltGrzm0Uc4yhjLnvDJn3orNVs5H6IB3DVdAjuYy8ZByS3bo23VkWuVfq9Th6NfX0l49iLlXaOGLXdZcRqMk3HBLvNkWcNuVIG8dURJA1Aw8r3JHqYMiuEcozAhwyC95+3w/IchiXjv4+9lefj9BYsEBeQSGOn7Kj9vnZEEzYyzVP0stgJWdZf01aHqjVhYfqh7GqonWINs+bvo8CyPtdoaXiYwtX1GDsoFkO+O2LS1PKq92ZfrDnujeP9QCR2TOCXOyUmu2096RR+CcBs8MdR13WIVQ6iKqVwiHS1i5XrQTF+fS7/83DMtHZ6tXDmn54AW0gp8DchBDTyOU1nVyDVADcOEQ3Jb6XVHv77yvmbNBmnRNQyGpMikwfElBeQ9YRKFI+q2mokZCJNTWrd7ofMCF/oXONo8qi6E46uJ72FSnfEJiqnmSbabMPI8ct11OF4APrm0UqolV4x82gzkty5gCINH1x/lVPvUYkZeJDtTNZ/TW1rsm+H4CjzKN50uwcq/23Y/BM3Ih4ZglIwJnJK00rukzkf7uJJW9M+PtUpArhn5pb6qIogmS/QmCZM4uy1GnjPL22HkAM0Dd9FKIjKZqYReMlSMvF6tVpnVx/NYq60tzElw4g2qkkUvgN2wS9Nt6oahPstOPnlqr/LnweBgFchNW5muQcHI98I9bNGgq0vxcrsI84E/oxbfsx/lgH+AJedwRVqxdTng4BXfCkwtbjujGCF1IlWPRYztgDVd514w8oVVK/OAXJOEeD+Qg0p4tlODkCojzxe2S3tjPr30BLz6fy9IUrvusDeK9g+V0DGJM5LcfjYH8hIjF27A4+GAV6++gw3z64oZp3nRwkJ5ZYaRN9zqPZZLlLPyyrneOQRC2R4XQP6VqSV/WQF53pU4q5EDZBIeyFTSjyNVIG/VbJA2V/UCYGQtjrTUye8aNh4xvmvSj/s0EKzZPg2nwWe3Pksn7HC8cRxW74Hlu2i4KoE0KpkUqQ81pjfzAo18NvIStr3BguRcDkql7k6lketmoH6IVT/Hfcv3ETgBp9qnONM5UySMylFOdnbCzuJEJ+hhGs3qTde7DIjiu5UZ+dD1GWZRhZFHvStqS2yYRbIT4EhrBZmZOO6gWJBzM//RqM12X4FH0Qx0C1FOdt4qIwegdURPZlJAO0zGBFJWDNPefPTNvPHIG+e+fbW2Sj/uF23YlTj5RpAZnFdVVDkjJxqCTFnRD39euZIz8oP+wWLOZRG5vFJi5J7RLKSVUZjgOybGqTcriePpXweg4dn0ykB+7g/UsTz2vcAUPHeHEZYaV06SuErqsnwaesL9NW1CNZtcLOIlAPkPPvSDfFffYyztyrGUpZVw41F4x08XHkRLdZu9UVQw8rK0IqVUA9G9GY087BXX0/Qa1EXEOw/9ME35YLGjzsuIc0fTfRENwQkYTBIMAZ5dhc3clmI24Xmue45DwSG1YBZA/rWZncXgZXeOtAJwb/yMsvVcr5ovNj0bmdlk+Q3hLhc3wcCwcIlUsjMa0JBqC3aqdYo/uKxu/KPNo/C9/xW+6R8TeBa9sWKNNbu6xSpalMtGTzeIXKPuDW168+b45UMB8qagfLjEcAuCNbaHQ2LrHK/aUCOl7m7fTSfs7KtPhmqyc2+yt1gfz0MbZxXRu6yYkV5crg/CQiPfbR8hQ2mohbQyuKoWP6ho5Eu+i0yaOO50kcgZeThucXFXPcgvRSPvR/1bqiEvonW0aLHuRT0GaYgv7Ip52Y8//uO856H3zH17UUs+3t+5y5HHlU58VskrvbCnGLk+p8s66Ztfq4KR+2rHM1cnt2pqvupkF99qF80xwyhVXuS2p+a2PvPfIUtpuNZUI+9cxL74CQAG2vgsZ+R7owhHDw2RqccoSolMH0eCiTlNdi6SVgbXqolOuCmQv/HIG3nrBEaaDOQmd+NkzCROudYL903SWfIdOsO4ON/l4Q9hkiEl2K6rdsflZKdm5Fa9qQ+pxyBKiuqT2k2llQG4DWWY5Vr7drI5kM+WIJ7rnZvWz8/LO93BeHkBuTaomcfIAe4Jv6RqeM0qw23WLLKs5IhXX8MQBnWrztCycImpO6bSyDMJXpNT7VOFr8mxxjGlgZoWTc9iS7PGirQCCugMu7I1v1EUW/a0zpmt/ZPWC901l1acQCV6hoqRXwtfQIq4APJTbaUlztPJ8wUvbwhqlz1T5oU2zipCu7/lsd0Pabq6ISVQ38Mva+Sj7ULbLFet+I5JNjlG5j1XsM6L/Yv45jJIhzPb6jy8lKqVYTy8pRryItrHaKUlRp7FBLexENwQyC1XVbPoevKCketzuqyTvoUTp2ZqeSKwwvJLjHyUjAjTkIbdLuaejqKk8CLn/m9VVRYXPlbkcwD4n/8UO/dD12W2uUa+O4zxTHUtZaZM4UIrUPNQTa84xkBY8J/fDbtnp8eWhIpllnNEUMhA8zTyPDxihnn5oT2VVi53tHnVcjWHs1R36IcJVwdbWMIqABSmJM+z9XyAsrSiQdSqNfUh9fUuRpuM5dJKPAfIpVTSiqOklYa7f/ecSytlRi6lLGrI1YF9TSMvYh4jtzVA1ZhwJDqzL9EJaouZyekDerihO7TsgJFp4YmoKD9spCm4De5uTwc8lOteA9cqhvnWZy/qgQfh4EP77WgXRJ5Ek2mN5+cBeQ4qyZSRD+OBuhn8NbqZGqH22PpjAMUxz9PJZxuCbiitAHgt0lGHD3zmkpJq5gD5qn4wtrQRWT2aFIw4lhmsaEaeTRm5EAIveozMGPDUtacA1ca84iomemZb6Z/rt6ORl+ZS3hYjry3R1BbH3ajLgAR/UQJ4TtwQyEG5M259EcYdulFXAbl+kJcCJVHlILkX7mEbdvGZ8xm5V0gxbWe5SHYOw3Qq8939jWon8KXfoOFZDCaJ6gz9zK9gH389UAJy01O+O6OoqKCSqaemJZlKf66VpCp/7wJ84b/Ac09Oj61oBpoxwpvHyC98HC5/uvirS0Q/1WBaYuSX9tR7ZicuLenk+OXBNVbrqxhiigOTRIFwrQDy6wqES4zcrk+BfBimRdIyB/JhOEdaiYaABMcvGPls5OZyZY18a7TFOBlPGbldUyQv/JpGPlcjzwHqYeNFTLK5QG4aAkPoG0vCkZa66ep2nbFp4hJTsw3lJ5HE4Da5q61Knja06X0eDc8uVu76rLTy5p+CH/gft/x9Ti+dxjEcTLm6j5Ff3B1xbagYlNRA3rAbDPRDGHorZO4ZluyjBSNYra3SdJpzGXmuR4+iCaNkdHNpxWvR6+7wY//xc3zhck8BeWlS+PYgZL2httrbejENhttTRi5EIa3EaVzRro/VHsXE48lzChAu9i+yVlOLxJntAYFr7avVvVGUr89tMXIhaGnNf2e8QwQE1gIdeE6s1XV35yIgX1L+Illvk37U10A+7TSsW/UiWZrnLWa1YgDaJ9SflleaH7rMKEoJk1Qx8rySwg3g1Fvh6d+k4ZoY8YDsg38VhIlzv0py9vSkLM/y6E0S0kwWicOckY+11FIX6rr5to+ZVy7tlRh5XkM+y8gLIC/tLH77J+HJnyr+6hIyTG3iNJsmO5MRF3dVwnOWkecNZFvDLdZrVSlnop9JzzbAX1Hlh/EIZFpYUtuaeGSTfsVio2arP+cmO3WpI24wtbCdCdtUc0/LVSvneueAqVnYNO/0NUZO45M/ywedv8vXP/UT8Dv/AJ79UAHqj4nn1IuOPD73vX1TJyBTn8MtdZP6ts/YNPCIsJ2YTGY0k6jCyPNBu3mUL+Q+acUwwJj52Q3ikfVH+Pj/9nHuah+uMPJnrvZ448/8Hu/+lc8D8MO//An+8IXr+I7PQGfsrxNg1s5xV+PB4n1CCO5u3z1fWtHnqautBxY2A+XhNhEadJ6/uKmYRImRXx+ErAXKtW5LN3H43c2iaiQSohhbF2cxVknu+rd/4U/xlmNv4nfP/y6jeMTWaItDvqrMOLM1uK1EJ1QHDN8WIwea2ns6H6/m38hzeyaW3CUMYSwGcp0zGfYukclMlanljMxtslJbmTLyyR5trz0fyAtppVYA+bruk+iOYqWRlxPv938r9De5b/hJ/r3zTxAXPg7v/L+xfe0vpMHJMz014g5o5v77mccgTJhoR8R6aa5noTvvlprO5jUD6WMFFJjm0b82HV0H2FnEBIfuOK5874t7I2xTqFF/pcgHaG+Pt4tFNI/JPGllMj3XoHJfADLsMyyx63wRnKuR5925jtLIFxGMttuuAnm59DAPr/U1IAcInSX6skar8wX4o38Bv/ougi/+ewAeM57nmnt8oUOepRlHmrQ40FI3TWAHjE3Bqgf3HNA3bDQGt8FabY0Vb6UisYAagpBHbRbIX0LYps3d6wEvlID8v3/+CgL4obeqSSmkEZ+5sEdgB0RZTAT87nATYUa8euMbKp+XlyDOVq7kQN6P1M12UyD3Wjh6msrVi/rBbU7tPbf7IWsNl7pVZ1uDi793AUuo8xPbXjFTM0qjCiNfCVy+5dQT7IV7fPCFDwJwNFCAujOMbksfhxlp5XaqVgBv6TielAWQB4t8b+aEaZiF1e3c0EDe0349ipFPO2SXveUKkJcZecXStb4Mx14LBx8pXn8wUEDeGcdK7y3XNt/7BBgWb/vsj3K/uMD1b34/PPTnikW2rJHvap29reUHlexMmGhG7qM+N7CDaZNNGciHOSOfAXJrRlrJMvX+/pVCKrSySQHks9LK4XZt39Srok0/3Cm8bvKoMvI1NVg830Hk+nS+SEcDhtFUWimSnfM08hIjH4YzRnnlY5vxWznXO0fNqrFeL52XrwG5is1T7+L74vfx2W/7PXjfFTj1Vuof/pu8yfgsrzKe51Lw4ML35jM8ZdIohgyoKfDwwJrN6YPqAjWiEbgNhBD88jt+mR9+9Icrn1NOdszTy15K3L0ecHFvVNyMH/rCVV59coU//egJANpOxlY/LMq/hobB73W/iEzq/KlDVSnpVPsUvai3r3LFNASmIejHCkhuJq1It0FdjjDI6F47p36oGflQe82sNVx82y/Axd85g2mYmBLleKhzBVEW7ZM8XnfoddStOr/0xV8C4Hhr6q1xOxUrUGXkt1NHDkDrKM005bIGW/82gBymk57mhga37kB51KuqFc0SvWYFyDthh7bXnrbNlxm5EPDuD8HD7yqkmCNN9dl7w0j7/pTuxdoS3P02MtPhL8U/ydZh5Y6Ye9Dk5Xuu6RaMfMnTbDXzGIQpQ6HuNV9PTA2cEiPfOz8dpFE4Hy7QyPPvMeko2wIkdFWVklFi5LZpYxkWo3jEpd0RR5f3d9Iu1R0QMeO0XwVIpolKzzKhvqp+T87+84oRbbFBOKiAcn7uxvPKD3MgdwKGYTpXWgHVpiVt/ycAACAASURBVF92QDzbO8vx5vGKjp/Pwv1KxMsKyCsaueXAn/sl5PoD/Gv7/2JJDLh8AyD3dQ2uTIICyH3bZwiQhMXN3cjSQts82jy6zye84U2BYl/54UuMe9YbSKn04Re2Bjy/NeDtr9goGoKWPbjWmxTlXzumyef7nyXuv4KDraqmm1euLEp4DhKdbLtJsrOvH+RlKyTc1V2aGsi3+4pRrQUudbtOJtV18fcuQDjAQZKUzLXiNN4neXiWx5uOvonNoWLCdy9PTc5uF8gNYRSVK7crrdA+RjPL2NS+0cGC5p9FsVJbYXu8YHRhbUm55mmwK6pWDBssr8rIw73FGnkpdie7NJwGK74Cus441nrvzL347f+az7zz/+Nj2SuKypV8MS1r5Lkt7Lqv7geZ1pRGLtT5DOYBeRaDtoJlsK0Ayp65ZvkuKWfkZWvZvXOQxhgyJZQ2XZ20zacEXdwb70t0ggJyYaljL5cewpSRu3Y+QxfY1RKj3m2gnx8jHqjFLy8/tPNk5w2kFTegP4lvWVo53z1flVVgf0nvHYyXFZBPq1byxE4D8T2/xnXUhbraemjhewM9GYi0WYxGU0CeQTIptpuNNCtmBs79HPcGGvlLjLvXFVN4YWvAk19U7dDf9IoDRdXKikeFkX+o2SaSE9L+Q9VZlEwrVxbp5KMcyG8irVweq4f+HXfXaUb6IWxME5IAh5dqxZYYUF2Rl/4YK5NE3nQBLFetlCP3h6lZNY42pwzrdqUVmMort5XsBFVLnmZ0tZRxu0C+VltbzMgNE+or9PS/F1Urnhq0vewtszfZI85iemGPJe/WgHzFWylkhs4oYhgm+yuoam28JXW9ZoG8opFraeVP3/0EP/mqv4OMlxmGCVEqGEhPNUihDbPKk3dyeWVeDTmofJHlTYE819KhMoN0glN0qNasGr1wyO4w2pfoBCWBeJ4uT63PJjsVNhRVK+VjnGHk6UQ964GWVkxD4FrG/PJDfa6k7TOM0lsC8iiN2BxuThOdeXxNWlExr2pFNA/x7vSn+D/jP0/fv2vRW2noErmGtYyptTcF5GkFyAOZFdUWcz+nkuy8M9LKidU6hpgC+cNH22rWpAa/tiPZ6oVFZcEHghqOaNAS9xXfJY8Vb4WW25rLyB3TYJR2sYQ1fyJRKS6MNJDfU+eA2CHyVou69o+d2cGxDB452i4aqwSCmpTwpV/HRhKXhlfHaTwXYF93+HX4ts+RxhFqjlXUut8uI4epvHK7GjntaVMQgO9v3ODF+2O1tqqGMMsFhkv+Oj295W65rUo53EpthVSmXOxdRCJpu9Nk59xuUVR1zbK3XPhzb/dD4lTOBZhcBsibgmY1ctdy2R3GOKbBgaDNd9//XYBgGKZEaUafunoeKDFyPeG+qCUfbu+vWMnDrpUYeQnI984Xdq65tAIKyHfHCjjnMXIAv64W3NlkZ5jMaOSgrH9hysgtjxQDNJCXn1/ftSqdnRd3R5zfGSqfFWBi1EkzuVBOXfKWGMZD/t0X/x1/ePkPyWSmzLLK8RUE8juDRP+LIr9Y5TpygE3zCP8qPMB7rcUMueX60IelkgGSb/vESKIyI3fbN2zoCe5wshPUDuPEis9Hn9vm85e6/K0n9MOipZWWqx5Y31IsZMuAdfkq7GD/zS6E4IHlB3jq6lNIKStdaI5lMM56tL32Yp8VHS/0LN4OPLgiCcUuHXuNnAN97MUdHjvWxrOnMzp920e4LXj6N7BXXOKSE165IajyvU2XH3n0R4oEaeApm9KXwshzALxtaSU4QLOUFw6CW7NXyGO1tkoiEzphZ37ewV+lG22DOcPImeYpznQV4Cx5S5Wk37zYnexyqn2KumNim6Jonpm3O2wUQD7DyOMBlrCwDZu9YcSSr7ziLVMx02GUYJk2A1kj0Fp4wcgPP66kkTIjP7BA0rRqU408l2W8VoWRh5SkFbtORwP50aX59fw1b0gINyg/NMHOpZUZRi4EE6OOyE23SqBcs81K1cpP/ufPk0rJrz2oXjvUFgaBO/+Zf/3h1/PbZ3+bn3nqZ4qfnWyerL7Ia6vzkURVk7U7EC8vRp7sZ+QAtv67ZS4Gp5Ote4l23shJ/9HiZ0XyMA2nQL68mI2DavfP405JKwCn1gM+d0mt1m9/hWY4GpRadkaUZoUzHYA5foS1BYD3tuNv41zvHM93nq/83LEMwqxX6YhbFM/sqXPZYMxRq8M1qUCnM4r40pUer71LPSw58Pi2DxuvgNEOjpTE9vRBLLfoz8b33P89vOu+dwHTB+ulMPJCWrndZKdh0CrLQ8GtGZ7lcdOmoGCdXjzAMiy12JQ6DQsg1zJY222rpJ+wbgjky94yQgjadYfLHV36OWd3mAN57oBY1siLrs5RVLB7UNdgGCZEScaAGn6pq5jhjkrgLp2cMvLBLTLywRYIAw4+UmHk0vQKq4FcWgHmJjsBbLePkNa+qVp5Dbhnm0VuQiVVxbRaBYiMOjW5f/Gr6ylBeZy9PuTy3lg3BMFA21EsSnY+sPIAH3jnB/jQd36I9736fXz/K79/OiIxj3xn8BVoCnpZAXmY7O/shGn7uWMu/jor9Trh1jdztDVlTQWQZxH9uI8jJe4N9HGYgo1jGkVX6Z2Ie7ROfu9Gg7vW9I1nmCBMmrb63qNQ/e5l4TDoHGMtmA/kbzn2Fgxh7Jvh6VomoezftGIlTjO+lFtlT7ocFDucjRT4f+LsLlLCa0/ptvwSI+eAGp9mGzaR9taWUs6tWpkXOfDcbh05TNv0b1taQc19BTClxLtF58o8CiAfLS5B7CWTkoVtSVrRu8MXO4o55nmLfYOIdcRZTCfsFO9r12w2c0Y+hym6loljGYWPT76Y9qP+1GdlGBX12aAkBqWRZwyoE+RdxYYLUV+VlC7fpdhuPIGwu79iJQ+7Nm0IGm6rapLlkxVGbjr1SrJzGI+o2SYr/vzrKKw+Imvu21FOkryO3ChyE6AN0IzpcxqZdXzU757Nd+WMPExSrvUnbPUnyEkf7DqDWG3bFpUf5nE4OMx33/fdvPdV790/qeor2Kb/sgLynJG7M9UiOUO/EbDmTPpAa8r2CiBH0h9s3TTRCdMV+U7JKnnkCc+3v3Jma2+5+Jb63sOhwJCSt/nHuD5IFjLy1doqr9p4Ff/jfLXL1LEMYtm7aaLz/M6Q3Zz99zfxswHPjhpEScbHX9zBtQwePqpuylzSUIxcA3lpEk2iZzPeiuQRuBYNz3pJuYeXnOwEWhqM/UwinFvv7ITS7M7JDYCclFYxaakkrdSq0kq+U1oE5Hl5W74Qt+u2Yo0sBphmyW+l6LrN4iojL4Fm3TEZhClxkjESNeoaiBt5X4KvwXjvLAxUYv7GjFzXww+3FZtvH1NeMCNVRmm6tYpGPkrGnFr3F0p/mdEli/ebd03K5YegSxCZsmAdseXjo72S3KpMmmvkm50JUkKcSsJRt2jPh/3TgW4rvgbkKnJGPsu8nVuQVvIhBnOB3DAY9DdpZDcHcts08GzjjsoqAK+5a4UHD7f4zscOV//BtPFNdZOOdq/zL69t84PrbyZKsoVADvBNx7+JF7sv8sLeNOnpmgYJ/ZuWHj5ztU8fvbXdegaAS9kyZ7YHfOzMDo+fWCoqhyqMPAdyq1YAeay35rfClJd9h8PtW/c6KcdLLj8EmrrqIkDcsk9OHjkj3x4tKEH01+iZBk1Tf69wOuig5bTUjNKukilyIPcsj/Ec18C8VDE3W2vXnaldxIL7MXD3AzlMz9feMGJ5jrQSpxlD4eNHmr1qw7qCVScTuPJ5/aY5VSugqlaSEiP3V9UwaVCDqgHH8+mVgDxKx5xeX+CyCMR0SKIGaVZteJvEauBM0USUlyC6VSBPrIBA5Iy8LK1Y+OMrMN7j0t60GSsc9goLW1gsrdxSfLUCuRDiu4QQXxRCZEKI+b3xdzDCJEMIsGcAO2fiN2Lkjxxt8zfeeg9vvnd6002BXNAfbWkgv7FGDhC49h1n5IfaNX7zR17P8ZUZRmi61I0cyDd53XiCdFRZ2Y2A/G3H34ZA8OHzU3nFtjJScXOflWev9pGGjbTrsK083q/KFf7ozA7PXO3z2rumCeO8akUB+QOwdALbaxdAno+/uhXt+n3ffD8/++cfvenr5kXOMF8SkGtTNF/c/jWt23XqVv2G3Z1dw6RpOKqJJhoUD7RpmLRdda5qVq34DosYed4MVDDy2vScLqqmaHjTAcxlIHctlzSTdMZxhZHn1RtRmjE26jww7POWo2/hoXyEmr9ajF5D2+IuBHK7PmXkgy1VplieQQo4Xp3OWN0jlvDIiLhnYzGQj+UeWdIswD+PSZzilSXXXO6ZYeSpXWLkjqWMtc7/ET+2/ff4f7vvht/6ycK0C5TlLW7AMMqnA30ZQN4+Dq//sYpn0Z2KL5eRfwH4DuCjd+BYbhphkuKYxr5tV87Ib6SR26bBj33j6cqF8K0pI++HHQKJ2vrdJJqedVOt7I6F5WKR0PAsoo7ayu6gmNvqAo0cFFN8bOOxirxiWOoGvZm08szVPidXfYTXgm3FnK4bK/zyx84BU30cZhi5XYO/8Tmc4EDBxHNAvxXJ4+hynXsPLH6IbxRflrSiWWIgXto1Xa2tLp4UFKzRMwyawpwmuUrgkoNyeZdUs2qM0zlAPqkC+awkMi8KB0Sqi6lnenTHMVLCcr28IJgMwoQokUyETzPs87Nv+mesxqF+wdoUyC98TP9sEZCX6siH19V7c0a+rZw73ZpfSCvj0AQjLPJFszGKR0TZCJlMh2rkMYlTlegsvsh8Ri5tH1/oBLFrwX/5y/CL7+CuyRfVc9W5UGHk2aRf+KwAc21sbzlah+Ft/+CWyOLtxpcF5FLKp6WUz96pg7lZREm2L9EJSjKAG0sr8yIfJjsSgn48pGH7t2R6FXjWHWfkC8O0IQnZaHpI3SF4NVWM7kaMHJS88kLnhSKZJkxVSnUzIH/2al8BqtsshloEa8c4t6OGVD90ZFr1Uqla0WGZ1pSR60nsL4Up3058OdJKa1k1Ufnm7SdZQQH5wu5OXwN5RsUwK48icVnyh1/EyGelldYtMfKptGKVFirHdIuuzgojdyyGYUqcZky0Rzlhf1o+WF+B5hHVnXrlc8V3nBt2XSU7oyHEQ2V166+qn2tG7pWAfDgxEUbKqbX58lq+68mSBnujOYy8AuTzGbl0GgQ62eknXfjCf4ZHv5d//uAH+BT3wXiXi7tjDujKKRkNlfPh5A4w8q9g/C/TyIUQ7xFCPCWEeGp7e8FNf5Oo2aZqlJmJW0l2zovplCCDvkxp3KLPxl954yl+4PUnb/7COxGmC6mqrTa0DruZqAVoUdVKHrm88uR5ZRdr2YppzEorSZoVD/UwTLiwO+K+jcZU06uvcOqQAo/HTyxXznNFWtFhG/Z+aeUlMOXbiS+HkTdXVF4kMG+/7BFu7LeS1pYZGEI1HeXaqDe9zxYy8nnSymQH27CLxrB2/ealsIE7lVZMw0ToR/65K2FhtbCvaiVS5YehqfMkYV/VkBu2OnbTUjvXLFGlfotqovNkZ96e76+rHET7eGFoVfcDJnHGJE7pDBURW16wKdsaqaYiGTcLj5g8JnFWHcG2gJHjBvhMcC2Bdenj6mePfA+W57OdBjDa5dLeiLvWfJZ9ByOeDpUQ4s6WHN/JuCnyCSF+RwjxhTn/vfN2fpGU8heklI9LKR9fW1uwgt8kfvKJ+/jQj+6fn3gr0sq8yNnk0BAMDEGjfmvH9S0PHeTtr7i9xpGXHOb/3965Bzd2nvf5+Q5wcCMAkuBlV7vLvUjam6SVrNVaK8mKalmKJcWqlTqKIscT2VN3NJ2mrWur1fiSNPWkGad106aZJrFsJ03aUZMmzs3jxqotx7Wdae1GsVRJ0d3RiruytEst7wRIAMTXP75zDg5IAMTtgAD4PjOcJS4EvrOH/J0Xv++92LCeZ1c6RjQ3A9Fh3swqwpaqiMiqMZmY5OSuk/zBi3/ATHaGZMIZiBGq/OX+nf99hpO/+HXe//nv8si3TAbF0d2pcjST3svx3eb7Gy6tvAhUi8hty95krQQekbfhkSfjGSxgaLS1i/Pe1F7OLZ3juYvPbXpsmXW0UqSLhYqGWS5u5kpFRG7Hq2925mYZi4951qI//7tWpk/FlCAAbYTo/MI6n/rTZza9zlA05KUfroXcJlOLJiL3NULz7JVaGSvgFAStmlxzKEfJo+WKx/iQeY/FXIGLS2YDc7WKrQR4n3qqWivF9creRzUichVNYqt1MhHjjROOwd7rSNghZnUSnZvj9dkV9o3GmUxFsYtZiAyxtFYkGdk85q1X2FL5tNa3a62vqvL1Z91YYCO4m5/NWithK0zcirBghVi1LJJNFoN0hXAUimtMpqIMFS6ikxNmOk8yuqnNZzU+efqTrBRWeOhbDxF1+lTkVis/1fzNDxdJRcOcm8/ya39hslyO7U6XI8f0Xt5+KEPYUrzrWKUfWuGRO0R86YfNZK20Qzt55JayuP6SG7jqsrtaeu8PXfkhxuJjfOx/fWzTAN5Fx05J53N1rZVGInK3GMjF3eyM26FNrRpc0rEwy/kipZJGa03JGa121SXj/K0zjWljRF7Spqy/LORLJl3Qb6G4Ql7LVgFfRO62unWe69uHSg6Z8HshV2DG+a+rVQzlRuSlYsorInJZLaxXpiUnqkfkltPlcTy6Bq/9Jex7O4SjJKJh5nUSpdfJLc8xNZpgVzpGpGS6oa7UmA7UK/RV+mEtIk4qXCsFOolwjDedn0/5Wqn2DKEorBeYTMcYY571+AQzy2tb+uMuR0aP8OmbPs2TF57kOzO/D8D8SmUk/9rFFa7aO8y3/vmt/Ld/cJp/e+/V7B9LlP8I0nt429QIz/yrO4zA+5hMTBIPxzmQKkdZtmV73rj7b9eslWYrOx2+8O4vcO+Re1v62Uwsw6+881c4nz3PJ//ykxV9V9xOg+n8Sn1rJdaYtVIh5E4kPVSjbBxM1orWsJIv8vp8Du1E5CenJvlH77yMXekoY8nK9EOAuWyBgjstaXXRSR/0NRRrJCK3Y6BLZroUlEXf3fBEkXK6OJ6dyzJvriuVvdh9zGRniIaihEl4fdRdjLWydUQecqYEHQjNw5vPwIF3AMYymdPmwjWqltmXibM7ZZPQuS1b2PYC7aYf/j2l1DngRuB/KKX+51Y/EwSRBtIPazEUjnPeFfKRLvnezRCyYd1E5OMskIuOm+k8TfQjuevQXTxwxQOsFBfRxQRvzK1VPD49m+XAWALLUtx0+Tj3nXIuaF5EbtIdq23wDkeH+c793+HGPTd691VYK+uVDZuCwi1MCvqCUYtrJq7h4bc/zLfPfZsvPP0F7/6FvBHv4eyiz1rZLOT+tgmxUIxcMbdpOIjb+dDF9cjrFVD5+628dH7Js1ZidoyH7zzG//n4beVuopQLi+azeYr2RmvF1zrWE/IaGStgNjXBVHLCZmvFjnsXoyfOzKGdMviaEXnuAhPxCUYSUW/wtMum9MOxy+C2X4Dj761cUtxE5DfylLnIHCwL+SzmsVGW2TeaYG/S/P+XIkmWBjki11r/idZ6n9Y6qrXepbW+o1MLa4ZWPXIwKYhvOmPIUv5f1F7BZ61MqHkWwxnHWmnOQvjodR/l9O7T6MIEZ315sstrRd5azpsIfCM+j7we0VC0wjus2OzsUtaK1/0w4Pepx/1H7+eOg3fwuf/3ObJO/rRnrWTnyhG57+O+28XPL9AJO4FGs7ZevuBqrZnNzXqeOviFvHZEnqwQ8uWykDtW1EZ7zo3u57MFCmFn19GzVvxC7gQ99YTc7Uk+P22KoJwmcF5EHo55+zxGyM25qyXkM9kZJhOTjCZs5la2yFpRCn7kY5vWF3Yi8uvzf2U2b/ea8pe4HWJem+MdUUtMjSbYEzefrJa1mWNaq2FWLzAY1kqLHjnAkJ3gLTcij7SWwxwooQisF9id0KRVjlk1wlvL+aYicjD7AY/86CNMrnzEG24LMH3RfH8gU6U03Y0ch+sL+Ua2I2tlV2IXCrX1UOkAUUrxnkPvoaiLvDRn8u89a8UdPWYPmawPhyvHruQzP/IZbtlX3sSv1sp2ubBMvpSvEPy4bXqp1IsU3UEoS6sFXjq/RMhJQfSPx/PjvlaxpCm6+x7Zt0xUXhGRXwY3/ZNNEW8FbkQ+91rZH4eKiNwV8qfOzRN1Li7ZQg1rxZnVOToUqWqtNDLoJZowQn5Z/nnYex04XToTkTBzONlg1jKTqSi7Y+Z3eGE9yvJq7XmdvcBgCHmL6YcAQ3a5+CBpVy9E2FZCEWOtWEYQzqwmWS/pLVMPq76UFWL/6DBnfQUP07PGmDxQLSIfPWiilrHLNz9Wh4rNzlJ3Njuv23UdX7v3a0yltnef41jGtCB+YdbkSXtCXiqZ/iQbsyiU4u5L766wnqoNl3BzyP0XKqUUI3G7bkTuWStrxlqJOqmC0Ro58/6LgraTgILZM86DPiG3LHj3vzYWRi1sX0TuLxqKDZuWruEYaUfI88USB0ZHNx23n5nsDBPxCUYT9mZrpbhemX5Yg+iQCU4stGergGk65nrkBxJrWJZiMmqyfWYLNsuDbK30Cm1ZK760r62GLWwL4SgU88TXTFXfc0vmj3y8hZ7dAFOZOGdny38oZ5yIvKq1ctlt8NALnkfeKLZlUywVKemSF5EHbXkopdg91KWU0DrsHtrNcHS4LORri0RUmJjWcPGVCn+8FvWE3B+RA+zPJLzRhdVIO0K+mCvwyoVl4o6Qu++xEX/Fsh22jA3k9vUeatJ6dCPy3Ozmnx09AHackKW8i83lE7WFfKWwQraYNRF5IrJ1QVANokO+v/EDPiGPhFgiQVFbTMXM+4/b5nd3xhHytqo6A6Z3V9YEkZA5gS1ZKz47JRnp1Yg87w25/f5F84fYSkQOMDVq2oYurhZIx2xeu5glMxSp6LPuoVTzf7yUNzaLpWJTJfqDgFKKY5ljnpAv5BcYdi2KuTOw5+SWr1FNyL0+K/FK6+iLHzxFuE4Ak4ya//fnfrjIaqHEUCTKXK5eRF4Ww0jIgmjKfJKASmulEfz2zUYv/fjfhZwzOSlus7Ra5OjkON+arp614qYeTsQnuJCIMLeS9wanaK1ZLZQ2dUWthnL2J0oqhDVVHlyesMNoLOZJsjts3n84ZKyt8zlb0g+7QXvWivkjU6iKXOiewbFWXCF/ddWssVmP3MVt2O/65NOzK+yv0cS/VfztUruVtdJLHBs9xstzL3uzONNusFAqNhWR+wWtVkQ+kojU9W7daPeJ10wlZSpqxLWWR+5/rUjYMlbQ0hvmjlYjcticb37Lv4A7fgkotxo4vjuDQnkXsMX8It+c/ibnV8573SUnE5NkhmyKJe31P1nz9yLfCidYs/Zc683whHJG1rxOMmaZeouQ49VPr1gU64x56wV6d2VNMBy3CVuqpf4nrngn7SSW6sHrWjhiRkMtX6CE8lKkWrZWRl0hz3HlnmFeu5jlugP1e680iyfk64WuZa30EsfGjpEv5Xl14VUW84uk/W2DY1vbd1Ujcqdhlr8CtBESEVMs9IwzfSodM68dq9GOwJ/KaLsRufdgc4Op8U2Jqlc45GbfHN01TCwc8zY7f+Op3+DR5x8FyokIE4kJ7/lzKwVSMXtzL/J6hMKmV8yRygQ7d59hjiQHMULuDl5+cc4ZQN3DeeS9u7ImeN/JvVy9b7ilXWVXyHsyYwW8XissnycXHqFImGjYatmvc6eTn53Nki+W+OF8jvdd21xWyla40Xe+lO9a1kovcTxzHIAXZ19kMb/IrsQkWGETkW/s/VEFN1reaK2MREea/n9USpGMhlnIFdg7Evc88loReSRsEQlZ5NdLlUJuhc0GZTM0KOTDcZu4HWLvSJxEOEGumGO9tM5jrz7G6d2nuWXfLTx54UlWCivsS+7jTNJ8uri4ssb+sQSrBRORNxzI/ez3Km0f8DJe5nSKK9edNFFHyF+YbWw60HbSuytrgpgd4qq9jTW82ogXkfeiPw7GWtHrsPQGa7FxWDa2Sqs9H4bjNqlYmLNzWV6fz1HSsH9jD/Q2qbBWupS10kscSB8gGory/OzzLKwtcGT0iBGypTfa2uxsNbUyFTNCfnR3yrvI1orIwfjk+awZ1OBdeBJjFSPTGsIv5HXyze+9bh9vmxrBspRX1frE+Se4uHqRTxz9BHccvIMHrnzAe/5E0qzdbfpVHrzc4Pqim//WLUsRt0PM6RTR/OvmzjUj5BfWysPBe5Ue9BK6S89H5G5nufmzFJ3ikVb9cTAR2tRogrOzWV67WCf1sA0qrJX1PCEVItRAe+BBIWyFOTJ6xIvI05F0zZLxalTLI29HyN1Pqod3Jb05krUiciinIEZCqhyRt1Is53+POhH5u47t4sFbTBpj3I6TLWT56qtfJR6OV+TXu7i//zPLjpAXm7BW6pCIhFiyUlirc2bgRH6ZghWj5Mik5JH3MD0v5G52wcI5lNPXot5AiUaYysQ5O5djetYtBgpus7NYKu4oW8XlWOYYz198npXCSqWQN2CtuB0lN1orbh/yZnEzko7uSnnnolbWCpQtBDtklS88Qy28d73NzhokwgkW84s8Pv04t07dWjVN0u0N40bkubwbkbcn5PFIiPVYBrW+Zpp9rS1RDJc/rfbyZqcIuSvkdq8KuSOCawvYwyZPup2IHMyG57m5LGfeMhPL2329jbgbm/lSnnwpv6MyVlyOZY6xVFgCIB31R+Rb+8zVPPJ2rRWAIz4hrx+RG0GssFYaFOIKwlFAmcg82tjfVzwc56kLT7GwtsBdh6p3o7RDFpmhiM9acYeytydn6ZiNnXQuWNlZyC+jfZZrL0fkvbuyLtHzEXm4LLLxzB4shTe9pFWmMmaD6PvTc+zPJDreY3mjtbKT/HEXt8ITnEKzZOPWiqUsr3EWmGKYxfwik4k6fU3qkIqFUQoun0xin23EI/dF7OFIdwAADaJJREFU5KE2rBWljE/u72O+BYlwgqIukoqkuGnPTTWfN5GMloXcsVYaKdGvx2fed4LJ19+ExzC9ZfIrKN8FSIS8h+n9zc6ykEeHd/PbH3o7J1rc2HVxM1eePjfPbcfrtCFtkY2bnTsxIj88ehhLWZR0ieHosC8ib+zc+VvZnl06C8D+1NbzZKtx0+VGhGN2yPu0FA1vba1EQhaE3Yi8xYZy4VhT0Xzc2SC9ff/tdVNWJ1JRzyNfK3TGWrlmagRKTgZXbhbWlgnFfBG5bHb2LqlIatubLdXFL4LJSd55dJKxdj1yJ5e8pOFghzc6oZx+WCjt3Ig8Ho5zKG06BKYjaUg5Q0vijeXs+4V8enEagP3p1oT8vlNT/Or91wLm933IHqq7b+FtdoZ96YfN5pC72InmhNzxxO88dGfd502kopuslXaFHCgfZ3YW8kvY8bQ3tCPRidcPiN69xHSJdCTNIz/6CCfGT2z3Uqrjj5zqNfFvgn2jZfHudOohlFMN3RL9nbjZCXA0c5QfLPzACPnx98JPWDB+pKGfjYVjmyLyTjQE++njP82tU7fWfY7brrUij7zViPyK98LE0YaffunwpRxIH+D63dfXfZ4r5Fprcs2mH9bDbYGQm4O1ZVTmMiZTURZzhYYmcm0XO17IgYqhCD2H/+Nlh4Q8Hgkxnozy1vJaxzNWAC/FLb+ep7Be2FFVnX5OjJ/gsTOPmU97dgxOND6BKB6OeyX6Z5fOMhYb60gLiXQkTTpT36dPeB65gkuugcPvBl9fkqa48zNNPf1nrvgZPnD8A1tWWU8ko6wVSyytFZur7NwK9xNT9qIpCIommUzHKG0Y8tFriJD3Oq4IWnbzlXV1mMrEjZAHba3s0KwVgPuO3seJiRNNl9WDEXI3j3x6abqr7XmTfmslkYEP/GHX3htoqFWGl0u+tNZ8ZWc9QmEzBCNrPHIiKaZG4+SLpa1/dhsRIe91XGslOdl8ZV0dpkYTPH1ugT0j1duZtoN/szO/nt+x1kokFOGaiWta+tl4OO71V5lenOb0JS1GxC0w5AhiK22hu0WlkJuIPBru0HoTGTNMo7AC0SQ/f/cVrDgNunoVEfJex41m643UaoH7Tk1xaHyopY6RW+FPPyyUCl6Bi9A47mbnanGV89nzXY3IvfTDTgljAFQIeXGdaNjqXBptIgPzZl+CyBC72kz37QYi5L2Om37YIX/c5ebD49x8OJgZpRUFQev5isHCQmO4Qv76sun70WrqYStU5JH3KG4//pmlNVbzjQ2VaJh4Bs4/a77v1bTkDfTumRIMrrXSSmXdNrExIt+p1ko7uELuph52MyJ/x2XjfPjmQxy/pEeL5DDN3+yQYmbZeOQdyVhxSWTKPdgbrEjdbiQi73U8a6XzhTtBIQVB7RO34+QKOaaX2sshb4XhhM3P331F196vFSxLMe5UdxbWS52NyP058zshIldKfVYp9YJS6mml1J8opeQzdKeJpADV9CT77WTjZudOLAhql3goTr6U58ziGdKRtKkOFSpwc8lXC+ttl+dX4B+nV6XlbS/S7ueRrwNXaa2vBl4CPtH+koQKhsbgg1+Ga96/3StpmJAVIqRC5awVicibxq1wfHnu5a764/3ERDLKBSf9sJF5nQ2T8FXfRnpw/GMV2hJyrfXXtNZuXs53gX3tL0nYxKFbKpv09wG2ZXseuUTkzeMK+UtzL3XVH+8n/BF5rJMZNv6IvFeb6W2gk5udfx/4aq0HlVIPKqWeUEo9MTMz08G3FXoR27I9j3ynVna2g9s8KlfMMZUWIa/GRCrK7MoaK/licB75oFgrSqnHlVLPVvm6x/ecTwFF4NFar6O1/rzW+pTW+tTERP9kYAitYYfsHV8Q1A7+gQpirVRnIhWlpOGH86udz1px6ZPNzi2zVrTWt9d7XCn1IeBu4Date7whgdA1wlaY1eIq63pdPPIW8Au5WCvVcXPJZ1fywW12DoqQ10MpdSfwMPB3tNbZzixJGAQiVsRr+iQeefNURORdTD3sJ/yTrTprrThCbg91tC1GkLS7yv8EpICvK6WeUkp9rgNrEgYAO2SzUjDDncVaaR53FFs8HGcs1mIv8AFnMlUune+okNtx00e9TzJWoM2IXGt9eacWIgwWtmWTLTgRuWx2No0bke9P7e/4KL5BYTxV/r1qd17nJuIZCPfP721/fG4Q+g7bslkpmohchLx53EZj4o/XJhEJey13O9KLvOLFR/vGHwcp0RcCIhKKML82D4i10gpuRC6ph/WZSEVZXutw+iGYgRrrvd261o8IuRAItuXzyCVrpWnSkTQ/dfSnuPNg/dmVO52JZJRX31oh3mlr5Z5f7+zrBYwIuRAIfiGXrJXmUUrxczf83HYvo+dxM1c6HpH3GeKRC4HgVna63wtCEIiQG0TIhUDw2ymy2SkERVnId7aU7eyjFwLDH4WLkAtB4VZ3drT7YR8iQi4Egl/IxVoRgmIybYTcTUPcqYiQC4Hgt1ZEyIWguPnycT5779Wc3D+69ZMHmJ19GRMCQ6wVoRuEQxY/eUpy7SUiFwLBn3IoEbkgBIsIuRAIkrUiCN1DhFwIBNnsFITuIUIuBIJ45ILQPUTIhUCoEHIp0ReEQBEhFwLB75GHLUmOEoQgESEXAsGNyG3LlsEIghAwIuRCILhCLv64IASPCLkQCK61IhkrghA8IuRCILgbnLLRKQjBI0IuBILnkct0IEEInLaEXCn1i0qpp5VSTymlvqaU2tOphQn9jVgrgtA92o3IP6u1vlpr/TbgK8C/7MCahAFANjsFoXu0JeRa60XfzSFAt7ccYVDwhFw8ckEInLYrNZRSvwQ8ACwAt9Z53oPAgwD79+9v922FHseNxMUjF4Tg2TIiV0o9rpR6tsrXPQBa609praeAR4F/XOt1tNaf11qf0lqfmpiY6NwRCD2JROSC0D22jMi11rc3+FqPAn8O/EJbKxIGAslaEYTu0W7WymHfzXuAF9pbjjAo+Ev0BUEIlnY98l9WSh0FSsBrwD9sf0nCIOBG4pK1IgjB05aQa61/olMLEQYL8cgFoXtIZacQCF5BkHjkghA4IuRCIIhHLgjdQ4RcCASp7BSE7iFCLgRC2ApjKUsickHoAjKDSwiMh657iNOXnN7uZQjCwCNCLgTGA1c+sN1LEIQdgVgrgiAIfY4IuSAIQp8jQi4IgtDniJALgiD0OSLkgiAIfY4IuSAIQp8jQi4IgtDniJALgiD0OUrr7s9LVkrNYPqXt8I48FYHl9Mv7MTj3onHDDvzuHfiMUPzx31Aa71pVua2CHk7KKWe0Fqf2u51dJudeNw78ZhhZx73Tjxm6Nxxi7UiCILQ54iQC4Ig9Dn9KOSf3+4FbBM78bh34jHDzjzunXjM0KHj7juPXBAEQaikHyNyQRAEwYcIuSAIQp/TV0KulLpTKfWiUuoVpdTHt3s9QaCUmlJKfVMp9ZxS6m+UUh9x7s8opb6ulHrZ+Xd0u9faaZRSIaXUk0qprzi3Dymlvuec7/+ulBq4AaBKqRGl1JeUUi8opZ5XSt046OdaKfVR53f7WaXU7ymlYoN4rpVSv62UuqCUetZ3X9Vzqwy/5hz/00qpk828V98IuVIqBPw6cBdwBfB+pdQV27uqQCgCD2mtrwBuAH7WOc6PA9/QWh8GvuHcHjQ+Ajzvu/1vgP+gtb4cmAM+vC2rCpb/CDymtT4GXIM5/oE910qpvcA/BU5pra8CQsD9DOa5/h3gzg331Tq3dwGHna8Hgd9s5o36RsiB64FXtNZ/q7XOA78P3LPNa+o4Wus3tNbfd75fwvxh78Uc6+86T/td4Me3Z4XBoJTaB7wH+KJzWwHvAr7kPGUQj3kYuAX4LQCtdV5rPc+An2vMiMm4UioMJIA3GMBzrbX+NjC74e5a5/Ye4L9ow3eBEaXUJY2+Vz8J+V7grO/2Oee+gUUpdRC4FvgesEtr/Ybz0JvArm1aVlD8KvAwUHJujwHzWuuic3sQz/chYAb4z46l9EWl1BADfK611q8D/w6Yxgj4AvDXDP65dql1btvSt34S8h2FUioJ/BHwz7TWi/7HtMkZHZi8UaXU3cAFrfVfb/daukwYOAn8ptb6WmCFDTbKAJ7rUUz0eQjYAwyx2X7YEXTy3PaTkL8OTPlu73PuGziUUjZGxB/VWv+xc/d596OW8++F7VpfALwDeK9S6gzGMnsXxjsecT5+w2Ce73PAOa3195zbX8II+yCf69uBV7XWM1rrAvDHmPM/6Ofapda5bUvf+knI/wo47OxuRzAbJF/e5jV1HMcb/i3gea31v/c99GXgg873HwT+rNtrCwqt9Se01vu01gcx5/UvtNYfAL4J3Os8baCOGUBr/SZwVil11LnrNuA5BvhcYyyVG5RSCed33T3mgT7XPmqd2y8DDzjZKzcACz4LZmu01n3zBfwY8BLwA+BT272egI7xZszHraeBp5yvH8N4xt8AXgYeBzLbvdaAjv+dwFec7y8F/i/wCvCHQHS71xfA8b4NeMI5338KjA76uQY+DbwAPAv8VyA6iOca+D3MPkAB8+nrw7XOLaAwWXk/AJ7BZPU0/F5Soi8IgtDn9JO1IgiCIFRBhFwQBKHPESEXBEHoc0TIBUEQ+hwRckEQhD5HhFwQBKHPESEXBEHoc/4/xc2fsWhOkP8AAAAASUVORK5CYII=\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "### Vectorization\n", "\n", "What is the purpose of the `batch_size` argument? In ELFI, operations are vectorized, meaning that instead of simulating a single MA2 sequence at a time, we simulate a *batch* of them. A vectorized function takes vectors as inputs, and computes the output for each element in the vector. Vectorization is a way to make operations efficient in Python. \n", "\n", "In the MA2 simulator above we rely on numpy to carry out the vectorized calculations. The arguments `t1` and `t2` are going to be **vectors** of length `batch_size` and the function returns a 2D array of shape `(batch_size, n_obs)` with each row corresponding to a single argument pair. Notice that for convenience, the funtion also works with scalars as they are first converted to vectors." ], "metadata": { "id": "-KATe4f55XNK" } }, { "cell_type": "markdown", "source": [ "There is a built-in tool (`elfi.tools.vectorize`) in ELFI to vectorize operations that are not vectorized. It is basically a `for` loop wrapper.\n" ], "metadata": { "id": "gy5-MsG45hZJ" } }, { "cell_type": "markdown", "source": [ "# Defining the model\n", "\n", "ELFI includes a modeling syntax, where the generative model is specified as a directed acyclic graph ([DAG](https://en.wikipedia.org/wiki/Directed_acyclic_graph)). This provides an intuitive means to describe rather complex dependencies conveniently. Often the target of the generative model is a distance between the simulated and observed data. To start creating our model, we create an empty one, called... `model`" ], "metadata": { "id": "1_C0nHSc86fj" } }, { "cell_type": "markdown", "source": [ "" ], "metadata": { "id": "lBD77S7a_nZM" } }, { "cell_type": "code", "source": [ "model = elfi.new_model()" ], "metadata": { "id": "5JqeenFC_n7g" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "As discussed in the lectures, we need to define *prior* distributions for the unknown parameters $\\theta_1, \\theta_2$. In ELFI the priors can be any of the continuous and discrete distributions available in `scipy.stats` (we'll shortly have an example of custom priors). For simplicity, let's start by assuming that both parameters follow `Uniform(0, 2)`." ], "metadata": { "id": "yFuHup5l9FOS" } }, { "cell_type": "code", "source": [ "# a node is defined by giving a distribution from scipy.stats together with any arguments (here 0 and 2)\n", "elfi.Prior(ss.uniform, 0, 2, name='t1', model = model)\n", "\n", "# some scipy.stats-distributions are accessible via keywords\n", "elfi.Prior('uniform', 0, 2, name='t2', model = model);" ], "metadata": { "id": "87-VqEFp5gqH" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Next, we define the *simulator* node for our model DAG with the `MA2` function, and give the priors to it as arguments. This means that the parameters for the simulations will be drawn from the priors. Because we have the observed data available for this node, we provide it here as well:" ], "metadata": { "id": "4815YVbn9iCC" } }, { "cell_type": "code", "source": [ "elfi.Simulator(MA2, model['t1'], model['t2'], observed=xobs, name='MA2');" ], "metadata": { "id": "ok56p1pd97Rn" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "In model building stage, it if often convenient to check whether the end result looks like it's supposed to." ], "metadata": { "id": "vKyyuBeUAOik" } }, { "cell_type": "code", "source": [ "elfi.draw(model)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 209 }, "id": "jB1jarWFeaiS", "outputId": "09e3dba2-3943-4450-d3e2-56bcd8a15bc3" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\nt1\n\nt1\n\n\n\nMA2\n\nMA2\n\n\n\nt1->MA2\n\n\n\n\n\nt2\n\nt2\n\n\n\nt2->MA2\n\n\n\n\n\n" }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "markdown", "source": [ "You can test that the model is working by using `generate` method to draw random realisations of the graph." ], "metadata": { "id": "yY4ZKK6vBrTi" } }, { "cell_type": "code", "source": [ "model_output = model.generate(batch_size=1, outputs=['t1', 't2', 'MA2'])\n", "print(model_output)\n", "plt.plot(model_output['MA2'].T)\n", "plt.title('t1:' + str(model_output['t1'][0]) + ', t2:' + str(model_output['t2'][0]));" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 485 }, "id": "pDuXwZKcBcMf", "outputId": "eba993b0-adbd-4062-8c4c-0e45b428d8f1" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'MA2': array([[ 2.42, 3.1 , 2.76, 1.35, -0.43, 2.16, 3.34, 1.88, 1.68,\n", " 0.18, 2.3 , 1.55, 2. , 0.61, 0.4 , -1.6 , -3.22, -1.32,\n", " 2.84, 1.92, 0.54, -0.13, 3.24, 2.02, 0.64, -2.08, -0.2 ,\n", " 2.12, -0.82, -2.05, -3.49, 1.57, 2.8 , 0.49, -3.16, -1.71,\n", " -0.62, -2.05, -3.17, -2.11, -0.45, 0.09, 2.73, 1.05, 0.89,\n", " -0.59, 2.46, 1.85, 1.32, -0.39, 2.23, 0.34, 2.08, -1.2 ,\n", " 0.03, -1.63, 0.75, 0.18, 1.43, 2.78, 2.94, 3.5 , -0.58,\n", " -1.13, -0.58, 4.6 , 3.42, 2.31, 0.99, 1.18, 0.85, -0.19,\n", " 1.99, -1.46, -1.52, -1.69, 1.1 , 2.53, 0.12, 0.38, 0.62,\n", " 1.89, 0.97, 0.05, -0.16, -2.09, -2.17, -0.38, -0.66, 1.56,\n", " 0.52, -0.25, -0.06, -0.46, 1.36, 1.93, 3.68, 2.01, 0.49,\n", " 1.07]]), 't2': array([1.45]), 't1': array([1.37])}\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "But how does one compare the simulated sequences with the observed sequence? Looking at the plot of just a few observed sequences above, a direct pointwise comparison would probably not work very well: the three sequences look quite different although they were generated with the same parameter values. Indeed, the comparison of simulated sequences is often the most difficult (and ad hoc) part of ABC. Typically one chooses one or more summary statistics and then calculates the discrepancy between those.\n", "\n", "Here, we will apply the intuition arising from the definition of the MA(2) process, and use the autocovariances with lags 1 and 2 as the summary statistics. Note that since the rows of `x` correspond to independent simulations, we have to tell this numpy function to take row-wise means by the keyword argument `axis=1`:" ], "metadata": { "id": "zv6ExHrLAbxF" } }, { "cell_type": "code", "source": [ "def autocov(x, lag=1):\n", " C = np.mean(x[:,lag:] * x[:,:-lag], axis=1)\n", " return C" ], "metadata": { "id": "-eZ2yW3iAx-G" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "As is familiar by now, a `Summary` node is defined by giving the autocovariance function and the simulated data (which includes the observed as well):" ], "metadata": { "id": "d7aMrNjyA6D8" } }, { "cell_type": "code", "source": [ "elfi.Summary(autocov, model['MA2'], name='S1')\n", "elfi.Summary(autocov, model['MA2'], 2, name='S2'); # the optional keyword lag is given the value 2" ], "metadata": { "id": "DuBAtY6IBAXD" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "elfi.draw(model)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 313 }, "id": "NKNTY_FLBLvI", "outputId": "99e8aa4f-aa40-4982-a5f7-4280b81375b1" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\nt1\n\nt1\n\n\n\nMA2\n\nMA2\n\n\n\nt1->MA2\n\n\n\n\n\nt2\n\nt2\n\n\n\nt2->MA2\n\n\n\n\n\nS1\n\nS1\n\n\n\nMA2->S1\n\n\n\n\n\nS2\n\nS2\n\n\n\nMA2->S2\n\n\n\n\n\n" }, "metadata": {}, "execution_count": 14 } ] }, { "cell_type": "markdown", "source": [ "We choose the discrepancy as the common Euclidean L2-distance. ELFI can use many common distances directly from `scipy.spatial.distance` like this:" ], "metadata": { "id": "BgDE4P-o27Sd" } }, { "cell_type": "code", "source": [ "elfi.Distance('euclidean', model['S1'], model['S2'], name='d');" ], "metadata": { "id": "6Mfm83Oh25_b" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "elfi.draw(model)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 409 }, "id": "8HyIjPrq4CjR", "outputId": "a176bb5e-decc-4a38-8743-657bb75f408b" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\nt1\n\nt1\n\n\n\nMA2\n\nMA2\n\n\n\nt1->MA2\n\n\n\n\n\nt2\n\nt2\n\n\n\nt2->MA2\n\n\n\n\n\nS1\n\nS1\n\n\n\nMA2->S1\n\n\n\n\n\nS2\n\nS2\n\n\n\nMA2->S2\n\n\n\n\n\nd\n\nd\n\n\n\nS1->d\n\n\n\n\n\nS2->d\n\n\n\n\n\n" }, "metadata": {}, "execution_count": 16 } ] }, { "cell_type": "markdown", "source": [ "# Modifying the model\n", "\n", "Although the above definition is perfectly valid, let's use the same priors as in [*Marin et al. (2012)*](http://link.springer.com/article/10.1007/s11222-011-9288-2) that guarantee that the problem will be identifiable (loosely speaking, the likelihood willl have just one mode). Marin et al. used priors for which $-2<\\theta_1<2$ with $\\theta_1+\\theta_2>-1$ and $\\theta_1-\\theta_2<1$ i.e. the parameters are sampled from a triangle (see below)." ], "metadata": { "id": "DZ-d27T-4ZGm" } }, { "cell_type": "markdown", "source": [ "# Custom priors\n", "\n", "In ELFI, custom distributions can be defined similar to distributions in `scipy.stats` (i.e. they need to have at least the `rvs` method implemented for the simplest algorithms). To be safe they can inherit `elfi.Distribution` which defines the methods needed. In this case we only need these for sampling, so implementing a static `rvs` method suffices. As was in the context of simulators, it is important to accept the keyword argument `random_state`, which is needed for ELFI's internal book-keeping of pseudo-random number generation. Also the `size` keyword is needed (which in the simple cases is the same as the `batch_size` in the simulator definition)." ], "metadata": { "id": "i6Y3msta4ogw" } }, { "cell_type": "code", "source": [ "# define prior for t1 as in Marin et al., 2012 with t1 in range [-b, b]\n", "class CustomPrior_t1(elfi.Distribution):\n", " def rvs(b, size=1, random_state=None):\n", " u = ss.uniform.rvs(loc=0, scale=1, size=size, random_state=random_state)\n", " t1 = np.where(u<0.5, np.sqrt(2.*u)*b-b, -np.sqrt(2.*(1.-u))*b+b)\n", " return t1\n", "\n", "# define prior for t2 conditionally on t1 as in Marin et al., 2012, in range [-a, a]\n", "class CustomPrior_t2(elfi.Distribution):\n", " def rvs(t1, a, size=1, random_state=None):\n", " locs = np.maximum(-a-t1, t1-a)\n", " scales = a - locs\n", " t2 = ss.uniform.rvs(loc=locs, scale=scales, size=size, random_state=random_state)\n", " return t2" ], "metadata": { "id": "azrsTDTN4XE2" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "These indeed define a uniform distribution on a triangle." ], "metadata": { "id": "Y4w_cjj_5CFz" } }, { "cell_type": "code", "source": [ "t1_1000 = CustomPrior_t1.rvs(2, 1000)\n", "t2_1000 = CustomPrior_t2.rvs(t1_1000, 1, 1000)\n", "plt.scatter(t1_1000, t2_1000, s=4, edgecolor='none');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 265 }, "id": "c_YrMPD05NLp", "outputId": "d2f9d4b2-30bf-41f8-8618-9c4103f2ef04" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "model['t1'].become(elfi.Prior(CustomPrior_t1, 2))\n", "model['t2'].become(elfi.Prior(CustomPrior_t2, model['t1'], 1))\n", "\n", "elfi.draw(model)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 505 }, "id": "m6FQLWbX5ymR", "outputId": "9042ef5d-c411-4d22-82cd-a2f934f8740e" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\nMA2\n\nMA2\n\n\n\nS1\n\nS1\n\n\n\nMA2->S1\n\n\n\n\n\nS2\n\nS2\n\n\n\nMA2->S2\n\n\n\n\n\nd\n\nd\n\n\n\nS1->d\n\n\n\n\n\nS2->d\n\n\n\n\n\nt1\n\nt1\n\n\n\nt1->MA2\n\n\n\n\n\nt2\n\nt2\n\n\n\nt1->t2\n\n\n\n\n\nt2->MA2\n\n\n\n\n\n" }, "metadata": {}, "execution_count": 22 } ] }, { "cell_type": "markdown", "source": [ "# Rejection ABC\n", "\n", "The simplest ABC algorithm samples parameters from their prior distributions, runs the simulator with these and compares them to the observations. The samples are either accepted or rejected depending on how large the distance is. The accepted samples represent samples from the approximate posterior distribution.\n", "\n", "In ELFI, ABC methods are initialized either with a node giving the distance, or with the `ElfiModel` object and the name of the distance node. Depending on the inference method, additional arguments may be accepted or required. \n", "\n", "A common optional keyword argument, accepted by all inference methods, `batch_size` defines how many simulations are performed in each passing through the graph. \n", "\n", "Another optional keyword is the seed. This ensures that the outcome will be always the same for the same data and model. If you leave it out, a random seed will be taken." ], "metadata": { "id": "2S1ZJckw6UN6" } }, { "cell_type": "code", "source": [ "rej = elfi.Rejection(model['d'], batch_size=100, seed=seed)" ], "metadata": { "id": "Nrp-Al7h6PxB" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "N = 1000\n", "rejABC_posterior = rej.sample(N, threshold=0.2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zW0B0gdF7G9S", "outputId": "0dad4cfe-e831-4cba-f5ed-6af55743aa9f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Progress [==================================================] 100.0% Complete\n" ] } ] }, { "cell_type": "code", "source": [ "rejABC_posterior.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "y2BX6lW2-nsA", "outputId": "9d4b9f9d-7c0e-448c-874c-69e5d1226113" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Method: Rejection\n", "Number of samples: 1000\n", "Number of simulations: 34200\n", "Threshold: 0.2\n", "Parameter Mean 2.5% 97.5%\n", "t1: 0.108 -0.169 0.386\n", "t2: 0.224 -0.075 0.598\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "The `sample` method returns a `Sample` object, which contains several attributes and methods. Most notably the attribute `samples` contains an `OrderedDict` (i.e. an ordered Python dictionary) of the posterior numpy arrays for all the model parameters (`elfi.Prior`s in the model). For rejection sampling, other attributes include e.g. the `threshold`, which is the threshold value resulting in the requested quantile. " ], "metadata": { "id": "QJU-N1pg8nuB" } }, { "cell_type": "code", "source": [ "rejABC_posterior.plot_pairs();" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 577 }, "id": "B4OyJFB6825f", "outputId": "39d1186d-10b1-4c0f-b761-bbebdd365530" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "# Sequential Monte Carlo ABC\n", "\n", "SMC-ABC sampler is initialised just like the Rejection sampler. Modelwise, a major difference is that the algorithm requires a prior pdf." ], "metadata": { "id": "zSByAbJUAFB6" } }, { "cell_type": "code", "source": [ "# define prior for t1 as in Marin et al., 2012 with t1 in range [-b, b]\n", "class CustomPrior_t1(elfi.Distribution):\n", " def rvs(b, size=1, random_state=None):\n", " u = ss.uniform.rvs(loc=0, scale=1, size=size, random_state=random_state)\n", " t1 = np.where(u<0.5, np.sqrt(2.*u)*b-b, -np.sqrt(2.*(1.-u))*b+b)\n", " return t1\n", " \n", " def pdf(x, b):\n", " p = 1./b - np.abs(x) / (b*b)\n", " p = np.where(p < 0., 0., p) # disallow values outside of [-b, b] (affects weights only)\n", " return p\n", "\n", " \n", "# define prior for t2 conditionally on t1 as in Marin et al., 2012, in range [-a, a]\n", "class CustomPrior_t2(elfi.Distribution):\n", " def rvs(t1, a, size=1, random_state=None):\n", " locs = np.maximum(-a-t1, t1-a)\n", " scales = a - locs\n", " t2 = ss.uniform.rvs(loc=locs, scale=scales, size=size, random_state=random_state)\n", " return t2\n", " \n", " def pdf(x, t1, a):\n", " locs = np.maximum(-a-t1, t1-a)\n", " scales = a - locs\n", " p = ss.uniform.pdf(x, loc=locs, scale=scales)\n", " p = np.where(scales>0., p, 0.) # disallow values outside of [-a, a] (affects weights only)\n", " return p\n", " \n", " \n", "# Redefine the priors\n", "model['t1'].become(elfi.Prior(CustomPrior_t1, 2, model=model))\n", "model['t2'].become(elfi.Prior(CustomPrior_t2, model['t1'], 1))" ], "metadata": { "id": "Tgsub_qqBYee" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "smc = elfi.SMC(model['d'], batch_size=100, seed=seed)" ], "metadata": { "id": "4-2NWKzWZwzb" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "For SMC ABC, we have to define the number of output samples, the number of populations and a *schedule* i.e. a list of thresholds to use for each population. " ], "metadata": { "id": "c-6HcWpoAukw" } }, { "cell_type": "code", "source": [ "N = 1000\n", "thresholds = [0.7, 0.2, 0.05]\n", "result_smc = smc.sample(N, thresholds, bar=False)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "K5Hdtm-qAtAI", "outputId": "c17efdb0-0c13-4298-99db-ff8d772c1d6f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.inference.samplers:---------------- Starting round 0 ----------------\n", "INFO:elfi.methods.inference.samplers:---------------- Starting round 1 ----------------\n", "INFO:elfi.methods.inference.samplers:---------------- Starting round 2 ----------------\n" ] } ] }, { "cell_type": "code", "source": [ "result_smc.summary(all=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fwSDXJoKZzAT", "outputId": "a076b2db-b492-495d-dab6-5932f08389d3" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Method: SMC\n", "Number of samples: 1000\n", "Number of simulations: 94500\n", "Threshold: 0.05\n", "Parameter Mean 2.5% 97.5%\n", "t1: 0.099 -0.107 0.325\n", "t2: 0.244 0.012 0.526\n", "\n", "\n", "Population 0:\n", "Method: Rejection within SMC-ABC\n", "Number of samples: 1000\n", "Number of simulations: 2800\n", "Threshold: 0.683\n", "Parameter Mean 2.5% 97.5%\n", "t1: 0.074 -0.572 0.707\n", "t2: 0.128 -0.493 0.888\n", "\n", "\n", "Population 1:\n", "Method: Rejection within SMC-ABC\n", "Number of samples: 1000\n", "Number of simulations: 16500\n", "Threshold: 0.2\n", "Parameter Mean 2.5% 97.5%\n", "t1: 0.094 -0.182 0.354\n", "t2: 0.235 -0.072 0.629\n", "\n", "\n", "Population 2:\n", "Method: Rejection within SMC-ABC\n", "Number of samples: 1000\n", "Number of simulations: 75200\n", "Threshold: 0.05\n", "Parameter Mean 2.5% 97.5%\n", "t1: 0.099 -0.107 0.325\n", "t2: 0.244 0.012 0.526\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "result_smc.plot_marginals(all=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 887 }, "id": "sjDGqXACGVtJ", "outputId": "2a1e0ce4-aa30-43c4-ef19-38620c45ff27" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "In the end `SMC` required 94500 samples to obtain 1000 samples below threshold = 0.05. Let's run rejection ABC until we have 1000 samples below threshold 0.05:" ], "metadata": { "id": "VrWdyevsZtC5" } }, { "cell_type": "code", "source": [ "N = 1000\n", "rejABC_posterior = rej.sample(N, threshold=0.05)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JRV6Q8M5aAu3", "outputId": "56136975-4438-4cab-df35-a748760c6853" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "" ] } ] }, { "cell_type": "code", "source": [ "rejABC_posterior.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FMWuvsOdabLb", "outputId": "7e77d909-b747-4b5b-efa8-766fc5ec3209" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Method: Rejection\n", "Number of samples: 1000\n", "Number of simulations: 503600\n", "Threshold: 0.05\n", "Parameter Mean 2.5% 97.5%\n", "t1: 0.100 -0.131 0.355\n", "t2: 0.243 0.002 0.517\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "# BOLFI \n", "\n", "In practice inference problems often have a complicated and computationally heavy simulator, and one simply cannot run it for millions of times. The Bayesian Optimization for Likelihood-Free Inference [BOLFI](http://jmlr.csail.mit.edu/papers/v17/15-017.html) framework is likely to prove useful in such situation: a statistical model (usually [Gaussian process](https://en.wikipedia.org/wiki/Gaussian_process), GP) is created for the discrepancy, and its minimum is inferred with [Bayesian optimization](https://en.wikipedia.org/wiki/Bayesian_optimization). This approach typically reduces the number of required simulator calls by several orders of magnitude.\n", "\n", "This tutorial demonstrates how to use BOLFI to do LFI in ELFI." ], "metadata": { "id": "37ayda3MHeUx" } }, { "cell_type": "markdown", "source": [ "# Fitting the surrogate model\n", "\n", "Now we can immediately proceed with the inference. However, when dealing with a Gaussian process, it may be beneficial to take a logarithm of the discrepancies in order to reduce the effect that high discrepancies have on the GP. (Sometimes you may want to add a small constant to avoid very negative or even -Inf distances occurring especially if it is likely that there can be exact matches between simulated and observed data.) In ELFI such transformed node can be created easily:" ], "metadata": { "id": "ppj4KEn8HtzZ" } }, { "cell_type": "code", "source": [ "elfi.Operation(np.log, model['d'], name='log_d');" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DmaL_NnEHrvW", "outputId": "6c3e953a-a6b0-4085-e7d6-546a1e8c5327" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Operation(name='log_d')" ] }, "metadata": {}, "execution_count": 35 } ] }, { "cell_type": "code", "source": [ "elfi.draw(model)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 640 }, "id": "3AvgYzIcIbsa", "outputId": "6c464cd3-cc4e-401b-995d-6676b5ddaa00" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "image/svg+xml": "\n\n\n\n\n\n%3\n\n\n\nMA2\n\nMA2\n\n\n\nS1\n\nS1\n\n\n\nMA2->S1\n\n\n\n\n\nS2\n\nS2\n\n\n\nMA2->S2\n\n\n\n\n\nd\n\nd\n\n\n\nS1->d\n\n\n\n\n\nS2->d\n\n\n\n\n\nlog_d\n\nlog_d\n\n\n\nd->log_d\n\n\n\n\n\nt1\n\nt1\n\n\n\nt1->MA2\n\n\n\n\n\nt2\n\nt2\n\n\n\nt1->t2\n\n\n\n\n\nt2->MA2\n\n\n\n\n\n" }, "metadata": {}, "execution_count": 36 } ] }, { "cell_type": "markdown", "source": [ "As BOLFI is a more advanced inference method, its interface is also a bit more involved as compared to for example rejection sampling. But not much: Using the same graphical model as earlier, the inference could begin by defining a Gaussian process (GP) model, for which ELFI uses the [GPy](https://sheffieldml.github.io/GPy/) library. This could be given as an `elfi.GPyRegression` object via the keyword argument `target_model`. In this case, we are happy with the default that ELFI creates for us when we just give it each parameter some `bounds` as a dictionary.\n", "\n", "Other notable arguments include the `initial_evidence`, which gives the number of initialization points sampled straight from the priors before starting to optimize the acquisition of points, `update_interval` which defines how often the GP hyperparameters are optimized, and `acq_noise_var` which defines the diagonal covariance of noise added to the acquired points. Note that in general BOLFI does not benefit from a `batch_size` higher than one, since the acquisition surface is updated after each batch (especially so if the noise is 0!)." ], "metadata": { "id": "sPZnFF0hICih" } }, { "cell_type": "code", "source": [ "bolfi = elfi.BOLFI(model['log_d'], batch_size=1, initial_evidence=20, update_interval=10, \n", " bounds={'t1':(-2, 2), 't2':(-1, 1)}, acq_noise_var={'t1':0.1, 't2':0.1}, seed=seed)" ], "metadata": { "id": "rFTpcZeaIBVh" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Sometimes you may have some samples readily available. You could then initialize the GP model with a dictionary of previous results by giving `initial_evidence=result.outputs`.\n", "\n", "The BOLFI class can now try to `fit` the surrogate model (the GP) to the relationship between parameter values and the resulting discrepancies. We'll request only 100 evidence points (including the `initial_evidence` defined above)." ], "metadata": { "id": "-FizoEk2Iomy" } }, { "cell_type": "code", "source": [ "post = bolfi.fit(n_evidence=100)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XgTf3ww0Ip9j", "outputId": "f333e72e-a0c0-40f6-c19b-c53d206d63a9" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.inference.bolfi:BOLFI: Fitting the surrogate model...\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Progress [==================================================] 100.0% Complete\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.6062) of the GP discrepancy mean function as a threshold\n" ] } ] }, { "cell_type": "code", "source": [ "bolfi.target_model" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2BLn1shQK9JL", "outputId": "a07ab912-1d90-4df8-e6de-771e8bf3ee28" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\n", "Name : GP regression\n", "Objective : 78.53641077165285\n", "Number of Parameters : 4\n", "Number of Optimization Parameters : 4\n", "Updates : True\n", "Parameters:\n", " \u001b[1mGP_regression. \u001b[0;0m | value | constraints | priors \n", " \u001b[1msum.rbf.variance \u001b[0;0m | 0.3630657802137458 | +ve | Ga(0.0083, 1)\n", " \u001b[1msum.rbf.lengthscale \u001b[0;0m | 0.48910102787502585 | +ve | Ga(1.3, 1) \n", " \u001b[1msum.bias.variance \u001b[0;0m | 0.03436487581949289 | +ve | Ga(0.0021, 1)\n", " \u001b[1mGaussian_noise.variance\u001b[0;0m | 0.12611712861991034 | +ve | " ] }, "metadata": {}, "execution_count": 39 } ] }, { "cell_type": "code", "source": [ "bolfi.plot_gp();" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 577 }, "id": "5CMrOITia0Us", "outputId": "192f6f3d-8722-4fd6-f205-0ef6a425965e" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "gp = elfi.GPyRegression(model.parameter_names, bounds={'t1':(-2, 2), 't2': (-1, 1)})\n", "prior = ModelPrior(model)\n", "\n", "acq = MaxVar(model = gp, prior = prior)\n", "\n", "bolfi = elfi.BOLFI(model['log_d'],\n", " batch_size = 1,\n", " initial_evidence = 20,\n", " update_interval = 1,\n", " target_model = gp,\n", " acquisition_method = acq\n", " )\n", "\n", "bolfi.fit(100)\n", "bolfi.plot_gp();" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 628 }, "id": "Sy-5LXEhN-hK", "outputId": "41d4173d-d91f-4b3c-fe5f-9c7cf8d67136" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.inference.bolfi:BOLFI: Fitting the surrogate model...\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Progress [==================================================] 100.0% Complete\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.7532) of the GP discrepancy mean function as a threshold\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "gp = elfi.GPyRegression(model.parameter_names, bounds={'t1':(-2, 2), 't2': (-1, 1)})\n", "prior = ModelPrior(model)\n", "\n", "acq = RandMaxVar(model = gp,\n", " prior = prior,\n", " sampler='metropolis',\n", " sigma_proposals={'t1': 4 / 10, 't2': 2/10})\n", "\n", "bolfi = elfi.BOLFI(model['log_d'],\n", " batch_size = 1,\n", " initial_evidence = 20,\n", " update_interval = 1,\n", " target_model = gp,\n", " acquisition_method = acq\n", " )\n", "\n", "bolfi.fit(100)\n", "bolfi.plot_gp();" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "8QxKERu0W6ao", "outputId": "d8f5d3ab-335a-4fcd-bc1b-8632d6a78ea9" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.inference.bolfi:BOLFI: Fitting the surrogate model...\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.080\n", "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.380\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Progress [==========----------------------------------------] 21.0% Complete\r" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.380\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Progress [===========---------------------------------------] 22.0% Complete\r" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.480\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Progress [===========---------------------------------------] 23.0% Complete\r" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "gp = elfi.GPyRegression(model.parameter_names, bounds={'t1':(-2, 2), 't2': (-1, 1)})\n", "prior = ModelPrior(model)\n", "\n", "acq = ExpIntVar(model = gp,\n", " prior = prior,\n", " sampler='metropolis',\n", " sigma_proposals={'t1': 4 / 10, 't2': 2 / 10})\n", "\n", "bolfi = elfi.BOLFI(model['log_d'],\n", " batch_size = 1,\n", " initial_evidence = 20,\n", " update_interval = 1,\n", " target_model = gp,\n", " acquisition_method = acq\n", " )\n", "\n", "bolfi.fit(100)\n", "bolfi.plot_gp();" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 696 }, "id": "G3Z0EKOXW7oy", "outputId": "301bf3da-eec6-4c34-d2f2-f7450cbc89fe" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.inference.bolfi:BOLFI: Fitting the surrogate model...\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "" ] }, { "output_type": "stream", "name": "stderr", "text": [ " /usr/local/lib/python3.7/dist-packages/scipy/stats/_distn_infrastructure.py:1740: RuntimeWarning:divide by zero encountered in true_divide\n", " /usr/local/lib/python3.7/dist-packages/GPy/kern/src/stationary.py:168: RuntimeWarning:overflow encountered in true_divide\n", " /usr/local/lib/python3.7/dist-packages/GPy/kern/src/rbf.py:52: RuntimeWarning:overflow encountered in square\n", " /usr/local/lib/python3.7/dist-packages/GPy/kern/src/rbf.py:76: RuntimeWarning:invalid value encountered in multiply\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Progress [==================================================] 100.0% Complete\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.8637) of the GP discrepancy mean function as a threshold\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "# Sampling from the posterior surrogate\n", "\n", "Finally, samples from the posterior can be acquired with an MCMC sampler. By default it runs 4 chains, and half of the requested samples are spent in adaptation/warmup. Note that depending on the smoothness of the GP approximation, the number of priors, their gradients etc., **the default sampler may be slow**." ], "metadata": { "id": "z9LXoOqeXgPV" } }, { "cell_type": "code", "source": [ "BOLFI_posterior = bolfi.sample(200, info_freq=100)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bb7WE8gNXbtS", "outputId": "42f63de7-1834-43d6-f620-d90f08b68d40" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.8637) of the GP discrepancy mean function as a threshold\n", "INFO:elfi.methods.mcmc:NUTS: Performing 200 iterations with 100 adaptation steps.\n", "INFO:elfi.methods.mcmc:NUTS: Iterations performed: 100/200...\n", "INFO:elfi.methods.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n", "INFO:elfi.methods.mcmc:NUTS: Acceptance ratio: 0.431. After warmup 4 proposals were outside of the region allowed by priors and rejected, decreasing acceptance ratio.\n", "INFO:elfi.methods.mcmc:NUTS: Performing 200 iterations with 100 adaptation steps.\n", "INFO:elfi.methods.mcmc:NUTS: Iterations performed: 100/200...\n", "INFO:elfi.methods.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n", "INFO:elfi.methods.mcmc:NUTS: Acceptance ratio: 0.377. After warmup 3 proposals were outside of the region allowed by priors and rejected, decreasing acceptance ratio.\n", "INFO:elfi.methods.mcmc:NUTS: Performing 200 iterations with 100 adaptation steps.\n", "INFO:elfi.methods.mcmc:NUTS: Iterations performed: 100/200...\n", "INFO:elfi.methods.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n", "INFO:elfi.methods.mcmc:NUTS: Acceptance ratio: 0.420. After warmup 2 proposals were outside of the region allowed by priors and rejected, decreasing acceptance ratio.\n", "INFO:elfi.methods.mcmc:NUTS: Performing 200 iterations with 100 adaptation steps.\n", "INFO:elfi.methods.mcmc:NUTS: Iterations performed: 100/200...\n", "INFO:elfi.methods.mcmc:NUTS: Adaptation/warmup finished. Sampling...\n", "INFO:elfi.methods.mcmc:NUTS: Acceptance ratio: 0.407. After warmup 2 proposals were outside of the region allowed by priors and rejected, decreasing acceptance ratio.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "4 chains of 200 iterations acquired. Effective sample size and Rhat for each parameter:\n", "t1 468.46550943959176 1.00682048837027\n", "t2 495.36243962449896 0.9988746763107251\n" ] } ] }, { "cell_type": "code", "source": [ "BOLFI_posterior.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2lFLCEzPUK7y", "outputId": "b664a38d-4174-47e9-8dd5-b64bcc857bca" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Method: BOLFI\n", "Number of samples: 400\n", "Number of simulations: 100\n", "Threshold: -1.86\n", "Parameter Mean 2.5% 97.5%\n", "t1: 0.040 -0.236 0.317\n", "t2: 0.198 -0.084 0.500\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "BOLFI_posterior.plot_traces();" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 730 }, "id": "hW7GoiXicQT6", "outputId": "1f64ebd1-2f22-47b8-99e1-d0198f49d806" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ]],\n", " dtype=object)" ] }, "metadata": {}, "execution_count": 48 }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "Instead of `nuts` sampler, we can also utilise simple `metropolis` mcmc-sampler which is often sufficient for small-scale problems. " ], "metadata": { "id": "vHghYg41cXDu" } }, { "cell_type": "code", "source": [ "BOLFI_posterior = bolfi.sample(2000, algorithm='metropolis', sigma_proposals={'t1': 4 / 10, 't2': 2 / 10})" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6CtslytUcm50", "outputId": "b73ba379-5bbb-4da8-d2e7-283619589085" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "INFO:elfi.methods.posteriors:Using optimized minimum value (-1.8637) of the GP discrepancy mean function as a threshold\n", "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.269\n", "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.267\n", "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.267\n", "INFO:elfi.methods.mcmc:elfi.methods.mcmc: Total acceptance ratio: 0.256\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "4 chains of 2000 iterations acquired. Effective sample size and Rhat for each parameter:\n", "t1 1108.3714284055893 1.0044533953223462\n", "t2 660.2994743601151 1.0065897137304802\n" ] } ] }, { "cell_type": "code", "source": [ "BOLFI_posterior.summary()\n", "BOLFI_posterior.plot_traces();" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 866 }, "id": "4YYdTUMPdMSt", "outputId": "9ab8e95c-7a53-4950-94ac-3fda790b1cab" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Method: BOLFI\n", "Number of samples: 4000\n", "Number of simulations: 100\n", "Threshold: -1.86\n", "Parameter Mean 2.5% 97.5%\n", "t1: 0.063 -0.200 0.342\n", "t2: 0.182 -0.038 0.445\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "array([[,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ]],\n", " dtype=object)" ] }, "metadata": {}, "execution_count": 52 }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "# Assignment \n", "\n", "We model a first-come-first-serve single server queue model (M/G/1 : Memoryless/General/Single server) and infer it's three parameters $\\theta_1$, $\\theta_2$ and $\\theta_3$. \n", "\n", "In the model, the customer service times $U_n$ are uniformly distributed in the interval $[\\theta_1, \\theta_2]$ and arrival times $W_n$ between customers are exponentially distributed with rate $\\theta_3$. \n", "\n", "Start simulation with an empty queue and assume that the simulator output (observed data) are the customer departure times $y_1, \\ldots, y_{50}$, defined as\n", "$$y_n = U_n, \\quad \\text{if} \\quad \\sum_{i=1}^n W_i \\leq \\sum_{i=1}^{n-1}Y_i$$ \n", "$$ y_n = U_n + \\sum_{i=1}^n W_i - \\sum_{i=1}^{n-1}Y_i, \\quad \\text{otherwise} $$\n", "\n", "The priors can be set as \n", "$$\\theta_1 \\thicksim \\mathsf{Uniform}(0,10) \\\\ \\theta_2 - \\theta_1 \\thicksim \\mathsf{Uniform}(0, 10) \\\\ \\theta_3 \\thicksim \\mathsf{Uniform}(0, 0.5)$$\n", "\n", "Use e.g. $0-$, $0.25-$, $0.5-$, $0.75-$ and $1-$quantiles as summary statistics and euclidean distance. \n", "\n", "Use the following observed data: \n", "\n" ], "metadata": { "id": "IfM8LutpUNKn" } }, { "cell_type": "code", "source": [ "xobs = np.array([[ 7.53, 9.99, 2.72, 2.28, 10.14, 8.95, 1.89, 6.16, 20.78,\n", " 15.1 , 3.05, 1.86, 1.09, 4.3 , 3.08, 2.13, 4.78, 3.62,\n", " 1.74, 4.48, 4.71, 1.05, 1.72, 3.36, 1.02, 1.86, 4.99,\n", " 3.86, 3.25, 29.54, 28.94, 2.8 , 2.67, 4.55, 1.07, 4.32,\n", " 1.06, 1.52, 3.93, 3.86, 3.32, 2.07, 1.83, 6.26, 2.81,\n", " 1.56, 2.44, 2.68, 4.83, 4.24]])" ], "metadata": { "id": "SCu7wS_Ke4lX" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Tasks\n", "# 1) Implement the model as ELFI-graph\n", "# 2) Infer the parameters using \n", "## - Rejection ABC\n", "## - SMC-ABC\n", "## - BOLFI using MaxVar -acquisition\n", "# 3) Plot the results" ], "metadata": { "id": "dbU5F72wfV0K" } }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "4nJ7JQ-JeuOp" }, "execution_count": null, "outputs": [] } ] } ================================================ FILE: day_5/5_yingzhen/classification.ipynb ================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "ProbAI 2022 BNN Tutorial - classification.ipynb", "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "source": [ "# A Hands-on Tutorial for Bayesian Neural Networks\n", "\n", "(Part 2: classification)\n", "\n", "[Yingzhen Li](http://yingzhenli.net/home/en/)\n", "\n", "(As part of the BNN lecture at [ProbAI 2022](https://probabilistic.ai/))\n", "\n", "In this tutorial, you will implement various Bayesian neural network methods based on variational inference.\n", "\n", "We will go through classification tasks to see the applications of uncertainty estimation in practice, including a case study on detecting OOD examples.\n", "\n", "**How to use this tutorial notebook?**\n", "\n", "* Read the descriptions in the text;\n", "* Fill in the missing code whenever you see a block that looks like below:\n", "\n", "```\n", "### beginning of your code ###\n", "[insert your own code here]\n", "### end of your code ###\n", "```\n", "There will be hints provided in the code blocks as well to guide you through.\n", "\n", "**GPU usage**: For this demo, it is recommended to use GPU to accelate training. To do so, click on \"Runtime > Change runtime type\", and select \"GPU\" for the \"Hardware accelerator\" option.\n", "\n", "Let us set up the required packages, and then, enjoy 😊" ], "metadata": { "id": "A9-xaxhnZGmM" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1ZmBUecUY3E4" }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import math\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.distributions as dist\n", "from torch.utils.data import Dataset, DataLoader\n", "from torchvision import datasets, transforms\n", "\n", "EPS = 1e-5 # define a small constant for numerical stability control" ] }, { "cell_type": "markdown", "source": [ "# Training BNNs on image classfication tasks\n", "\n", "In this part you will train a BNN on an image classfication task. More specifically, we will build a Bayesian convolutional neural network (Bayesian CNN) and test a number of approximate posterior inference methods. The dataset in use is MNIST (hand-written digit classification)." ], "metadata": { "id": "P_gLdppUaIlv" } }, { "cell_type": "markdown", "source": [ "Let's set up the MNIST dataset first." ], "metadata": { "id": "fodHNzqXe0Kf" } }, { "cell_type": "code", "source": [ "# setting up the MNIST dataset\n", "transform=transforms.Compose([\n", " transforms.ToTensor(),])\n", "train_data = datasets.MNIST('../data', train=True, download=True,\n", " transform=transform)\n", "test_data = datasets.MNIST('../data', train=False,\n", " transform=transform)\n", "train_loader = torch.utils.data.DataLoader(train_data, batch_size=100, shuffle=True)\n", "test_loader = torch.utils.data.DataLoader(test_data, batch_size=100, shuffle=False)" ], "metadata": { "id": "g8uOOI_efF8S" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Implementing a Bayesian CNN with MC dropout\n", "\n", "In below we construct a Bayesian CNN with MC dropout and set-up the prediction function with dropout activated." ], "metadata": { "id": "I2ATbBeZay9s" } }, { "cell_type": "code", "source": [ "# construct a Bayesian CNN for MNIST data\n", "def make_bayesian_cnn(in_channel, n_class, dropout_prob=0.1, activation='LeakyReLU'):\n", " nonlinearity = getattr(nn, activation)() if isinstance(activation, str) else activation\n", " net = nn.Sequential()\n", " # add in conv layers\n", " channel_sizes = [in_channel, 32, 64, 128]\n", " for i, (c_in, c_out) in enumerate(zip(channel_sizes[:-1], channel_sizes[1:])):\n", " net.add_module(f'Conv{i}', nn.Conv2d(c_in, c_out, kernel_size=3, padding='same'))\n", " if dropout_prob > EPS: # if setting dropout_prob=0.0 then constructing a deterministic CNN\n", " net.add_module(f'Dropout{i}', nn.Dropout(dropout_prob))\n", " net.add_module(f'Nonlinarity{i}', nonlinearity)\n", " net.add_module(f'Pooling{i}', nn.MaxPool2d(kernel_size=2, ceil_mode=True))\n", " # add in the last fully connected layer\n", " # the image sizes goes from 28*28 -> 14*14 -> 7*7 -> 4*4\n", " net.add_module(f'Flatten', nn.Flatten(start_dim=1, end_dim=-1))\n", " net.add_module(f'Linear', nn.Linear(128*4*4, n_class))\n", " return net\n", "\n", "# define the prediction function with Monte Carlo sampling using K samples\n", "def predict(bnn, x_test, K=1, use_dropout=True, reduce_mean=True):\n", " y_pred = []\n", " if use_dropout:\n", " bnn.train() # this will turn on dropout in prediction time, if applicable\n", " else:\n", " bnn.eval() # turn off dropout, if applicable\n", " for _ in range(K):\n", " y_pred.append(F.softmax(bnn(x_test), dim=-1))\n", " # shape (K, batch_size, y_dim) or (batch_size, y_dim) if K = 1\n", " y_pred = torch.stack(y_pred, dim=0).squeeze(0)\n", " if reduce_mean and K > 1: # \"ensemble\"\n", " y_pred = y_pred.mean(0)\n", " return y_pred" ], "metadata": { "id": "MgJ1eCUZbT4f" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Adding some helper functions..." ], "metadata": { "id": "wPwN7r3X5W_M" } }, { "cell_type": "code", "source": [ "def to_numpy(x):\n", " return x.detach().cpu().numpy() # convert a torch tensor to a numpy array\n", "\n", "# helper functions for training\n", "def train_step(net, opt, dataloader, device):\n", " logs = []\n", " for _, (x, y) in enumerate(dataloader):\n", " x = x.to(device); y = y.to(device)\n", " opt.zero_grad() # opt is the optimiser\n", " y_logit = net(x)\n", " # training accruacy (on a mini-batch)\n", " pred = y_logit.data.max(1, keepdim=True)[1] # get the index of the max logit\n", " acc = pred.eq(y.data.view_as(pred)).float().cpu().mean()\n", " # training loss\n", " nll = F.nll_loss(F.log_softmax(y_logit, dim=-1), y)\n", " loss = nll # note that by using AdamW optimizer, weight-decay is implicitly handled there\n", " loss.backward()\n", " opt.step()\n", " logs.append([to_numpy(nll), to_numpy(acc)])\n", " return np.array(logs)\n", "\n", "# define the training function\n", "def train_network(net, opt, dataloader, device, N_epochs=2000, verbose=True):\n", " net.train()\n", " logs = []\n", " for i in range(N_epochs):\n", " logs_epoch = train_step(net, opt, dataloader, device)\n", " if verbose:\n", " print(\"Epoch {}, last mini-batch nll={}, acc={}\".format(i+1, logs_epoch[-1][0], logs_epoch[-1][1]))\n", " logs.append(logs_epoch)\n", " return np.concatenate(logs, axis=0)" ], "metadata": { "id": "qPHRp8RCxtCy" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now we train a Bayesian CNN using MC-dropout" ], "metadata": { "id": "leKbq9FN5cCn" } }, { "cell_type": "code", "source": [ "# constructing and training a Bayesian CNN\n", "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n", "print(\"using {}\".format(device))\n", "dropout_prob = 0.1\n", "activation = 'ReLU'\n", "dropout_bnn = make_bayesian_cnn(in_channel=1, n_class=10, dropout_prob=dropout_prob, \n", " activation=activation)\n", "dropout_bnn.to(device)\n", "print(dropout_bnn)\n", "\n", "# start training\n", "learning_rate = 1e-3\n", "weight_decay=0.1\n", "N_epochs = 5\n", "# note that weight decay (i.e., prior loss) is implicitly handled by AdamW optimizer\n", "opt = torch.optim.AdamW(dropout_bnn.parameters(), lr=learning_rate, \n", " weight_decay=weight_decay)\n", "# the training loop starts\n", "logs = train_network(dropout_bnn, opt, train_loader, device, N_epochs)\n", "\n", "# plot the training curve\n", "def plot_training_loss(logs, dropout_prob, weight_decay):\n", " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n", " ax1.plot(np.arange(logs.shape[0]), logs[:, 0], 'r-', label='nll')\n", " ax2.plot(np.arange(logs.shape[0]), logs[:, 1], 'r-', label='acc')\n", " ax1.legend()\n", " ax2.legend()\n", " ax1.set_xlabel('epoch')\n", " ax2.set_xlabel('epoch')\n", " ax1.set_title('p={}, weight_decay={}'.format(dropout_prob, weight_decay))\n", " ax2.set_title('p={}, weight_decay={}'.format(dropout_prob, weight_decay))\n", " plt.show()\n", "\n", "plot_training_loss(logs, dropout_prob, weight_decay)" ], "metadata": { "id": "97QSPqBgxasT" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Training is finished, we now evaluate its test accuracy. Remember to turn on dropout in test time and do multiple forward passes." ], "metadata": { "id": "woEZgYdi5iSc" } }, { "cell_type": "code", "source": [ "# test the BNN\n", "def evaluate(model, dataloader, device, K=50):\n", " accuracy = 0\n", " for x, y in test_loader:\n", " x = x.to(device); y = y.to(device)\n", " # the \"predict\" function: by default dropout is on and we use K > 1 MC samples\n", " y_pred = predict(model, x, K, reduce_mean=True)\n", " pred = y_pred.data.max(1, keepdim=True)[1] # get the index of the max probability\n", " accuracy += pred.eq(y.data.view_as(pred)).float().cpu().sum()\n", " accuracy = accuracy / len(dataloader.dataset) * 100 # accuracy in percentage\n", " return to_numpy(accuracy)\n", "\n", "accuracy = evaluate(dropout_bnn, test_loader, device, K=5)\n", "print('Test Accuracy: {}%'.format(accuracy))" ], "metadata": { "id": "x5Tac3ju1h5U" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## MFVI for Bayesian CNN\n", "\n", "We will also train a Bayesian CNN with MFVI approximations. For this we will use the ```Bayesianize``` package. ```Bayesianize``` is a lightweight pytorch package which can convert any MLPs or CNNs to their \"Bayesian\" version using variational inference.\n", "\n", "First we clone and import the package." ], "metadata": { "id": "-pq1rMfHCwkO" } }, { "cell_type": "code", "source": [ "!git clone https://github.com/microsoft/bayesianize.git" ], "metadata": { "id": "0ez5JMLVDTN4" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import sys\n", "sys.path.append('bayesianize/')\n", "import bnn" ], "metadata": { "id": "vbEzUhvoDXvT" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "We prepare some helper training functions in accordance to the usage of the ```Bayesianize``` package. The idea is similar to the ones in the regression tutorial: we need to compute both the \"data losses\" (i.e., the negative log-likelihood terms) as well as the KL terms." ], "metadata": { "id": "BZJiHXvB60Fh" } }, { "cell_type": "code", "source": [ "# helper functions for training\n", "def train_bnn_step(net, opt, dataloader, device, beta):\n", " logs = []\n", " N_data = len(dataloader.dataset)\n", " for _, (x, y) in enumerate(dataloader):\n", " x = x.to(device); y = y.to(device)\n", " opt.zero_grad() # opt is the optimiser\n", " y_logit = net(x)\n", " # training accruacy (on a mini-batch)\n", " pred = y_logit.data.max(1, keepdim=True)[1] # get the index of the max logit\n", " acc = pred.eq(y.data.view_as(pred)).float().cpu().mean()\n", " # training loss\n", " nll = F.nll_loss(F.log_softmax(y_logit, dim=-1), y)\n", " kl = sum(m.kl_divergence() for m in net.modules() if hasattr(m, \"kl_divergence\"))\n", " loss = N_data * nll + beta * kl\n", " loss.backward()\n", " opt.step()\n", " logs.append([to_numpy(nll), to_numpy(acc), to_numpy(kl)])\n", " return np.array(logs)\n", "\n", "# define the training function\n", "def train_bnn(net, opt, dataloader, device, N_epochs=2000, beta=1.0, verbose=True):\n", " net.train()\n", " logs = []\n", " for i in range(N_epochs):\n", " logs_epoch = train_bnn_step(net, opt, dataloader, device, beta)\n", " if verbose:\n", " print(\"Epoch {}, last mini-batch nll={}, acc={}, kl={}\".format(\n", " i+1, logs_epoch[-1][0], logs_epoch[-1][1], logs_epoch[-1][2]))\n", " logs.append(logs_epoch)\n", " return np.concatenate(logs, axis=0)" ], "metadata": { "id": "hsprjWfBF4Sd" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now we train a Bayesian CNN with MFVI as for approximate posterior inference." ], "metadata": { "id": "IQbuHwZZ7PjY" } }, { "cell_type": "code", "source": [ "activation = 'ReLU'\n", "# use dropout_prob = 0.0 effectively constructs a deterministic CNN\n", "mfvi_bnn = make_bayesian_cnn(in_channel=1, n_class=10, dropout_prob=0.0, \n", " activation=activation)\n", "# use the \"Bayesianize\" package to make a Bayesian CNN with MFVI\n", "# inference='ffg' means using fully-factorised Gaussian for VI, i.e., MFVI\n", "# see https://github.com/microsoft/bayesianize/blob/main/bnn/nn/mixins/variational/ffg.py\n", "bnn.bayesianize_(mfvi_bnn, inference='ffg', init_sd=0.02)\n", "mfvi_bnn.to(device)\n", "print(mfvi_bnn)\n", "\n", "# start training\n", "learning_rate = 1e-3\n", "N_epochs = 5\n", "beta = 1.0\n", "opt = torch.optim.Adam(mfvi_bnn.parameters(), lr=learning_rate)\n", "# the training loop starts\n", "logs = train_bnn(mfvi_bnn, opt, train_loader, device, N_epochs, beta)\n", "\n", "# plot the training curve\n", "def plot_training_loss(logs, title):\n", " fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12, 4))\n", " ax1.plot(np.arange(logs.shape[0]), logs[:, 0], 'r-', label='nll')\n", " ax2.plot(np.arange(logs.shape[0]), logs[:, 1], 'r-', label='acc')\n", " ax3.plot(np.arange(logs.shape[0]), logs[:, 2], 'r-', label='KL')\n", " for ax in [ax1, ax2, ax3]:\n", " ax.legend()\n", " ax.set_xlabel('epoch')\n", " ax1.set_title(title)\n", " plt.show()\n", "\n", "plot_training_loss(logs, title='MFVI')" ], "metadata": { "id": "H5WrGFI6D53-" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Training is finished, then we evaluate the accuracy of MFVI Bayesian CNN." ], "metadata": { "id": "otYKHXCKKtqm" } }, { "cell_type": "code", "source": [ "accuracy = evaluate(mfvi_bnn, test_loader, device, K=5)\n", "print('Test Accuracy: {}%'.format(accuracy))" ], "metadata": { "id": "sEjZezhBIXGt" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Case study 2: Detecting adversarial attacks\n", "\n", "Neural networks has been shown to be vulnerable to adversarial attacks. In this case study, we will see whether BNNs can be more robust or not, as well as how to use uncertainty measures from BNNs to detect adversarial examples.\n", "\n", "\n", "But first let's set-up the helper functions for the adversarial attack. Here we consider the $L_\\infty$-norm projected gradient descent (PGD) attack [(Madry et al. ICLR 2018)](https://arxiv.org/abs/1706.06083) which is one of the strongest attack considerred in adversarial robustness literature." ], "metadata": { "id": "jzYt8P6t-i5R" } }, { "cell_type": "code", "source": [ "# PGD attack for one step\n", "def pgd_one_step(x, x_grad, x_clean, epsilon, lr, x_min, x_max):\n", " # use scaled element-wise sign of the data gradient for attack\n", " x_adv = x + lr * x_grad.sign()\n", " x_adv = torch.clamp(x_adv, min=x_min, max=x_max) # make sure x is valid\n", " # project the attack back to the epsilon-ball (L-inf norm)\n", " x_adv = x_clean + torch.clamp(x_adv - x_clean, min=-epsilon, max=epsilon)\n", " return x_adv\n", "\n", "def pgd_attack(x, y, model, predict_func, epsilon, T, lr, x_min, x_max):\n", " x_adv = x.detach().clone()\n", " x_adv.requires_grad = True # we need to compute gradients w.r.t. the input\n", " # perform PGD attack for T steps\n", " for t in range(T):\n", " # Forward pass the data through the model and get loss\n", " y_pred = predict_func(x_adv, reduce_mean=True) # return prediction probabilities\n", " loss = F.nll_loss(torch.log(torch.clamp(y_pred, min=EPS)), y)\n", " # One-step PGD using gradients of loss w.r.t. input in backward pass\n", " if type(model) is list:\n", " for net in model:\n", " net.zero_grad()\n", " else:\n", " model.zero_grad()\n", " loss.backward()\n", " x_adv.data = pgd_one_step(x_adv.data, x_adv.grad.data, x, epsilon, lr, x_min, x_max)\n", " return x_adv" ], "metadata": { "id": "cNuL8oCJ_dC3" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "We first test the PGD attack on a deterministic network (as to validate the implementation). For this deterministic network, we just need to turn off dropout during test time." ], "metadata": { "id": "YfD0zwEO9ZW2" } }, { "cell_type": "code", "source": [ "# testing adversarial attacks\n", "# we will use 100 test samples as examples\n", "x_test, y_test = next(iter(test_loader))\n", "x_test = torch.tensor(x_test, device=device)\n", "y_test = torch.tensor(y_test, device=device)\n", "\n", "# define some helper functions for the attack\n", "def run_attack(model, x_test, y_test, predict_func, epsilon_list, T):\n", " acc_adv = []\n", " pred_adv = []\n", " prob_adv = []\n", " x_adv = []\n", " for epsilon in epsilon_list:\n", " lr = epsilon / T * 1.5\n", " x_adv_ = pgd_attack(x_test, y_test, model, predict_func, \n", " epsilon, T, lr, x_min=0.0, x_max=1.0)\n", " # get the accuracy on crafted adversarial examples\n", " y_pred = predict_func(x_adv_, reduce_mean=True)\n", " pred = y_pred.data.max(1, keepdim=True)[1] # get the index of the max probability\n", " acc_adv.append(to_numpy(pred.eq(y_test.data.view_as(pred)).float().mean()))\n", " pred_adv.append(to_numpy(pred))\n", " prob_adv.append(to_numpy(y_pred))\n", " x_adv.append(to_numpy(x_adv_))\n", " return np.array(acc_adv), np.array(pred_adv), np.array(prob_adv), np.array(x_adv)\n", "\n", "# helper function for visualization\n", "def visualize(images, epsilons, pred_clean, pred_adv):\n", " cnt = 0\n", " plt.figure(figsize=(8, 2 * len(epsilons)))\n", " for i in range(len(epsilons)):\n", " for j in range(len(images[i])):\n", " cnt += 1\n", " plt.subplot(len(epsilons),len(images[0]),cnt)\n", " plt.xticks([], [])\n", " plt.yticks([], [])\n", " if j == 0:\n", " plt.ylabel(\"Eps: {:.2f}\".format(epsilons[i]), fontsize=14)\n", " plt.title(\"{} -> {}\".format(pred_clean[j], pred_adv[i, j]))\n", " plt.imshow(images[i, j][0], cmap=\"gray\")\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "# we first test a deterministic NN's robustness, by turning off dropout\n", "predict_deterministic = lambda x, reduce_mean: predict(dropout_bnn, x, K=1, use_dropout=False, \n", " reduce_mean=reduce_mean)\n", "\n", "def test_robustness(model, predict_func, x_test, y_test):\n", " # first test clean accuracy\n", " y_pred = predict_func(x_test, reduce_mean=True)\n", " entropy = -(y_pred * torch.log(y_pred)).sum(-1).mean()\n", " print(\"entropy (clean)\", to_numpy(entropy))\n", " pred_clean = y_pred.data.max(1, keepdim=True)[1] # get the index of the max probability\n", " acc_clean = to_numpy(pred_clean.eq(y_test.data.view_as(pred_clean)).float().mean())\n", " pred_clean = to_numpy(pred_clean)\n", " print(\"clean accruacy for this batch: {}%\".format(acc_clean * 100))\n", " \n", " # now run the attack\n", " T = 10\n", " epsilon_list = np.arange(0.05, 0.41, 0.05)\n", " acc_adv, pred_adv, prob_adv, x_adv = run_attack(model, x_test, y_test, predict_func, epsilon_list, T)\n", " print(\"adv accruacy for this batch (%): {}\".format(acc_adv * 100))\n", " # visualise the adversarial examples\n", " visualize(x_adv[:, :5], epsilon_list, pred_clean[:5], pred_adv[:, :5])\n", " return x_adv\n", "\n", "x_adv = test_robustness(dropout_bnn, predict_deterministic, x_test, y_test)" ], "metadata": { "id": "2i8dSWHmGcDc" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now testing the MC-dropout Bayesian CNN's robustness. \n", "\n", "In this case we will also look at the uncertainty produced by the BNN on the adversarial examples. Specifically, you will implement:\n", "\n", "* total uncertainty: as the total entropy\n", "* aleatoric uncertainty: as the conditional entropy\n", "\n", "Epistemic uncertainty is the mutual information which is essentially total entropy minus conditional entropy." ], "metadata": { "id": "5XV-VU6IQqIi" } }, { "cell_type": "code", "source": [ "# now we test Bayesian CNN's robustness, by using MC-dropout\n", "predict_mcdropout = lambda x, reduce_mean: predict(dropout_bnn, x, K=5, use_dropout=True,\n", " reduce_mean=reduce_mean)\n", "x_adv = test_robustness(dropout_bnn, predict_mcdropout, x_test, y_test)\n", "\n", "# also compute uncertainty measures\n", "### beginning of your code ###\n", "# Hints: for below two functions, the input y_prob_samples contains classification \n", "# probability vector from the BNN using weights sampled from the approximate posterior. \n", "# It should have shape y_prob_samples.shape = (K, batch_size, n_class),\n", "# with K the number of MC samples and n_class = 10 in MNIST case.\n", "# We would like to compute the entropy/conditional entropy using y_prob_samples,\n", "# and the returned tensor should have shape (batch_size,).\n", "\n", "def total_entropy(y_prob_samples):\n", " entropy =\n", " return entropy\n", "\n", "def conditional_entropy(y_prob_samples):\n", " cond_entropy = \n", " return cond_entropy\n", "\n", "### end of your code ###\n", "\n", "def compute_uncertainty(predict_func, x_adv, reduce_mean=True):\n", " logs = []\n", " for i in range(x_adv.shape[0]):\n", " x = torch.tensor(x_adv[i], device=device)\n", " y_prob_samples = predict_func(x, reduce_mean=False)\n", " entropy = total_entropy(y_prob_samples)\n", " cond_entropy = conditional_entropy(y_prob_samples)\n", " if reduce_mean:\n", " entropy = entropy.mean(); cond_entropy = cond_entropy.mean()\n", " mutual_info = entropy - cond_entropy\n", " logs.append([to_numpy(entropy), to_numpy(cond_entropy), to_numpy(mutual_info)])\n", " return np.array(logs)\n", "\n", "logs = compute_uncertainty(predict_mcdropout, x_adv)\n", "print(\"total uncertainty (entropy):\", logs[:, 0])\n", "print(\"aleatoric uncertainty (cond. entropy):\", logs[:, 1])\n", "print(\"epistemic uncertainty (mutual info):\", logs[:, 2])" ], "metadata": { "id": "yrCJ71w3RA-_" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "And similarly for the MFVI Bayesian CNN, test its adversarial robustness." ], "metadata": { "id": "G6910FAQ9gOf" } }, { "cell_type": "code", "source": [ "# now we test Bayesian CNN's robustness, by using MFVI\n", "predict_mfvi = lambda x, reduce_mean: predict(mfvi_bnn, x, K=5, reduce_mean=reduce_mean)\n", "x_adv = test_robustness(mfvi_bnn, predict_mfvi, x_test, y_test)\n", "# also compute epistemic uncertainty\n", "logs = compute_uncertainty(predict_mfvi, x_adv)\n", "print(\"total uncertainty (entropy):\", logs[:, 0])\n", "print(\"aleatoric uncertainty (cond. entropy):\", logs[:, 1])\n", "print(\"epistemic uncertainty (mutual info):\", logs[:, 2])" ], "metadata": { "id": "KBVKHfvdCRkR" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Can ensembles help?\n", "\n", "From the above examples we see that the trained BNNs help a bit in adversarial robustness but not too much, especially for the MC-dropout one. From the printed uncertainty measures, we suspect the trained networks may be still not uncertain enough on the adversarial examples.\n", "\n", "Next we will train an Ensemble of MFVI-BNNs. This means we will train multiple MFVI-BNNs independently with different random initialisation of the variational parameters, and then ensemble them together to get the final model. Although diversity is not explicitly encouraged, the hope is that the randomness in initialisation and SGD steps would allow different independent runs to result in different local optima, resulting in a more diversed set of neural networks.\n", "\n", "Please run the code in below block, it might take a few minutes to train the MFVI-BNNs in the ensemble." ], "metadata": { "id": "-XNhVvJnf6NG" } }, { "cell_type": "code", "source": [ "# train an ensemble of MFVI networks (independently) and see what happens\n", "mfvi_bnn_list = [mfvi_bnn]\n", "N_ensemble = 5\n", "activation = 'ReLU'\n", "learning_rate = 1e-3\n", "N_epochs = 5\n", "beta = 1.0\n", "\n", "for i in range(1, N_ensemble):\n", " print(\"train a new MFVI CNN (network {})\".format(i+1))\n", " net = make_bayesian_cnn(in_channel=1, n_class=10, dropout_prob=0.0, \n", " activation=activation)\n", " bnn.bayesianize_(net, inference='ffg', init_sd=0.02)\n", " net.to(device)\n", " print(net)\n", " # start training\n", " opt = torch.optim.Adam(net.parameters(), lr=learning_rate)\n", " logs = train_bnn(net, opt, train_loader, device, N_epochs, beta)\n", " mfvi_bnn_list.append(net)" ], "metadata": { "id": "FdvAe0Smf8w6" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Training is now finished, let's see how the ensemble performs on clean data." ], "metadata": { "id": "Uct-1Us2MuzT" } }, { "cell_type": "code", "source": [ "# test the ensemble on clean test data\n", "def predict_ensemble(x_test, K=5, reduce_mean=True):\n", " y_prob_samples = []\n", " for net in mfvi_bnn_list:\n", " y_prob_samples.append(predict(net, x_test, K=K, reduce_mean=False))\n", " y_prob_samples = torch.concat(y_prob_samples, axis=0)\n", " if reduce_mean:\n", " return y_prob_samples.mean(0)\n", " else:\n", " return y_prob_samples\n", "\n", "accuracy = 0\n", "K = 5\n", "for x, y in test_loader:\n", " x = x.to(device); y = y.to(device)\n", " y_pred = predict_ensemble(x, K)\n", " pred = y_pred.data.max(1, keepdim=True)[1] # get the index of the max probability\n", " accuracy += pred.eq(y.data.view_as(pred)).float().cpu().sum()\n", "accuracy = to_numpy(accuracy / len(test_loader.dataset) * 100) # accuracy in percentage\n", "print('Test Accuracy: {}%'.format(accuracy))" ], "metadata": { "id": "pg3t3s-Gjsu-" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Then we perform PGD attacks on the ensemble BNN." ], "metadata": { "id": "4vuukXBVMzfb" } }, { "cell_type": "code", "source": [ "# test for adversarial attacks\n", "x_adv = test_robustness(mfvi_bnn_list, predict_ensemble, x_test, y_test)\n", "# also compute epistemic uncertainty\n", "logs = compute_uncertainty(predict_ensemble, x_adv)\n", "print(\"total uncertainty (entropy):\", logs[:, 0])\n", "print(\"aleatoric uncertainty (cond. entropy):\", logs[:, 1])\n", "print(\"epistemic uncertainty (mutual info):\", logs[:, 2])" ], "metadata": { "id": "m4_eVAjYjpxJ" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Quantitative metric for detection results\n", "\n", "The ensemble BNN above makes a bit of improvement on robustness but not too much, however, in terms of uncertainty measures, the ensemble becomes more uncertain on the adversarial inputs. But at the same time, the total entropy on clean inputs remains relatively close to the single MFVI-BNN case. This is helpful as it will be easier to separate clean/adversarial examples by looking at the uncertainty measures and picking appropriate thresholds for dectection.\n", "\n", "In below blocks, we will quantify the quality of uncertainty by considering its effectiveness as a tool for adversarial example detection. The idea is, for a given uncertainty measure (e.g., total entropy):\n", "\n", "* for a given threshold, predict the input as \"adversarial\" if uncertainty > threshold;\n", "* search for the best threshold so that it returns the best true positive rate (TPR) of detecting adversarial example, while maintaining the false positive rate (FPR) to be below 5%.\n", "\n" ], "metadata": { "id": "cAMnjnwM9dkH" } }, { "cell_type": "code", "source": [ "# below are helper functions for the adversarial example detection test\n", "\n", "def get_uncertainty_adv_examples(model, predict_func, x_test, y_test, epsilon_list, T=10):\n", " # first get uncertainties on clean examples, return tensor of shape (3, batch_size)\n", " uncertainty_clean = compute_uncertainty(predict_func, x_test.unsqueeze(0), reduce_mean=False)[0]\n", " # now run the attack\n", " acc_adv, pred_adv, prob_adv, x_adv = run_attack(model, x_test, y_test, predict_func, epsilon_list, T)\n", " print(\"adv accruacy for this batch (%): {}\".format(acc_adv * 100))\n", " # get uncertainties on adversarial examples, return tensor of shape (len(epsilon_list, 3, batch_size))\n", " uncertainty_adv = compute_uncertainty(predict_func, x_adv, reduce_mean=False)\n", " return uncertainty_clean, uncertainty_adv\n", "\n", "def compute_detection_error(thresholds, uncertainty, label):\n", " # compute the detection error\n", " # here if uncertainty > threshold then we say it is an OOD example\n", " # where for label, 0 means in-distribution, 1 means OOD\n", " # we assume the first half of the labels are 0 and the second half of the labels are 1\n", " tpr = []; fpr = []\n", " N = int(label.shape[0] / 2)\n", " for t in thresholds:\n", " pred = np.maximum(np.sign(uncertainty - t), 0)\n", " fpr.append(1 - np.mean(np.where(pred[:N] == label[:N], 1, 0)))\n", " tpr.append(np.mean(np.where(pred[N:] == label[N:], 1, 0)))\n", " return fpr, tpr\n", "\n", "def run_detect(model, x_test, y_test, predict_func, epsilon_list, min_fpr = 0.05):\n", " thresholds = np.arange(0.0, np.log(10), 0.05)\n", " fpr_total = 0.0; tpr_total = 0.0\n", " N_data = x_test.shape[0]; batch_size = 100; N_batches = int(N_data / batch_size)\n", " for n in range(N_batches):\n", " x = x_test[n*batch_size:(n+1)*batch_size].to(device)\n", " y = y_test[n*batch_size:(n+1)*batch_size].to(device)\n", " uncertainty_clean, uncertainty_adv = get_uncertainty_adv_examples(model, predict_func, x, y, epsilon_list)\n", " # we assume the first half of the labels are 0 and the second half of the labels are 1\n", " label = np.concatenate([np.zeros(x.shape[0]), np.ones(x.shape[0])])\n", " # len(uncertainty_adv) == len(epsilon_list)\n", " fpr = np.zeros((len(uncertainty_adv), 3, len(thresholds)))\n", " tpr = np.zeros((len(uncertainty_adv), 3, len(thresholds)))\n", " for i in range(len(uncertainty_adv)):\n", " for j in range(3):\n", " uncertainty = np.concatenate([uncertainty_clean[j], uncertainty_adv[i][j]])\n", " fpr[i, j], tpr[i, j] = compute_detection_error(thresholds, uncertainty, label)\n", " fpr_total = fpr_total + fpr * batch_size / N_data\n", " tpr_total = tpr_total + tpr * batch_size / N_data\n", " # now we compute the best tpr given that the corresponding fpr <= min_fpr\n", " mask = np.where(fpr_total <= min_fpr, 1, 0)\n", " tpr_best = np.max(tpr_total * mask, axis=-1)\n", " # plot the result\n", " plt.subplots(1, 1, figsize=(5, 4))\n", " plt.plot(epsilon_list, tpr_best[:, 0], 'k-', label='total entropy')\n", " plt.plot(epsilon_list, tpr_best[:, 1], 'b-', label='cond entropy')\n", " plt.plot(epsilon_list, tpr_best[:, 2], 'r-', label='mutual info')\n", " plt.legend()\n", " plt.xlabel('epsilon')\n", " plt.title('Best TPR (with FPR <= %.2f)' % min_fpr)\n", " plt.show()\n", " return tpr_best" ], "metadata": { "id": "qXcybj_-Qpu1" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now we can test for adversarial detection. To do so, we randomly sample 1000 (10*100) datapoints from the test data." ], "metadata": { "id": "dC0_tU4fmdJ9" } }, { "cell_type": "code", "source": [ "# get N_batch*100 test images to test detection\n", "count_batch = 0\n", "N_batch = 10\n", "x_clean = []; y_clean = []\n", "for x, y in test_loader:\n", " x_clean.append(x); y_clean.append(y)\n", " count_batch += 1\n", " if count_batch >= N_batch:\n", " break\n", "x_clean = torch.concat(x_clean, dim=0); y_clean = torch.concat(y_clean, dim=0)" ], "metadata": { "id": "lB_VDIV6OuiK" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "First we evaluate the MC-dropout method." ], "metadata": { "id": "iPx7CvK-OvdV" } }, { "cell_type": "code", "source": [ "# test detection for the MC-dropout net\n", "epsilon_list = np.arange(0.05, 0.41, 0.05)\n", "tpr_dropout = run_detect(dropout_bnn, x_clean, y_clean, predict_mcdropout, epsilon_list)" ], "metadata": { "id": "u_7dewHElx12" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Then we evaluate the MFVI Bayesian CNN." ], "metadata": { "id": "JWjFNv08mjp3" } }, { "cell_type": "code", "source": [ "# test detection for the mfvi net\n", "tpr_mfvi = run_detect(mfvi_bnn, x_clean, y_clean, predict_mfvi, epsilon_list)" ], "metadata": { "id": "bSNKODDHl09g" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Finally we evaluate the ensemble MFVI Bayesian CNN." ], "metadata": { "id": "ZXgUsw6PmmkP" } }, { "cell_type": "code", "source": [ "# test detection for the ensemble method\n", "tpr_ensemble = run_detect(mfvi_bnn_list, x_clean, y_clean, predict_ensemble, epsilon_list)" ], "metadata": { "id": "OnQzSgaamvML" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Hopefully you can see that the ensemble BNN performs significantly better than the other two BNNs (MC-dropout and single MFVI-BNN) in terms of detecting adversarial examples.\n", "\n", "There are ways to improve the ensemble BNN further in terms of both robustness and detection to adversarial examples, but they are beyond the scope of this tutorial." ], "metadata": { "id": "lVrAAQcGPLo1" } } ] } ================================================ FILE: day_5/5_yingzhen/classification_solution.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "A9-xaxhnZGmM" }, "source": [ "# A Hands-on Tutorial for Bayesian Neural Networks\n", "\n", "(Part 2: classification)\n", "\n", "[Yingzhen Li](http://yingzhenli.net/home/en/)\n", "\n", "(As part of the BNN lecture at [ProbAI 2022](https://probabilistic.ai/))\n", "\n", "In this tutorial, you will implement various Bayesian neural network methods based on variational inference.\n", "\n", "We will go through classification tasks to see the applications of uncertainty estimation in practice, including a case study on detecting OOD examples.\n", "\n", "**How to use this tutorial notebook?**\n", "\n", "* Read the descriptions in the text;\n", "* Fill in the missing code whenever you see a block that looks like below:\n", "\n", "```\n", "### beginning of your code ###\n", "[insert your own code here]\n", "### end of your code ###\n", "```\n", "There will be hints provided in the code blocks as well to guide you through.\n", "\n", "**GPU usage**: For this demo, it is recommended to use GPU to accelate training. To do so, click on \"Runtime > Change runtime type\", and select \"GPU\" for the \"Hardware accelerator\" option.\n", "\n", "Let us set up the required packages, and then, enjoy 😊" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1ZmBUecUY3E4" }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import math\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.distributions as dist\n", "from torch.utils.data import Dataset, DataLoader\n", "from torchvision import datasets, transforms\n", "\n", "EPS = 1e-5 # define a small constant for numerical stability control" ] }, { "cell_type": "markdown", "metadata": { "id": "P_gLdppUaIlv" }, "source": [ "# Training BNNs on image classfication tasks\n", "\n", "In this part you will train a BNN on an image classfication task. More specifically, we will build a Bayesian convolutional neural network (Bayesian CNN) and test a number of approximate posterior inference methods. The dataset in use is MNIST (hand-written digit classification)." ] }, { "cell_type": "markdown", "metadata": { "id": "fodHNzqXe0Kf" }, "source": [ "Let's set up the MNIST dataset first." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 423, "referenced_widgets": [ "03a0fad49b1e4dc0b0c409156365865c", "e8ebcdf1581d45b994c606d888155996", "a24a771a62004294aacdcac61f1bc521", "317ffb4085c04c70949cf0a0d442e052", "32269b33ba2c42ffa0e45a464b303cc5", "c19757295f4d4e70935fecd9c86d1de7", "430285617ec64d33b1919372106842c9", "b5285a3635174370b2eaa6722a41c839", "6ea72b01eef643df8b1353bdc555079c", "17f5f4ec6ab04924843586e161c73370", "c1eb6fade65446eba9999ae65895dc05", "bbff5e2bf05e455b80c9bb8c4ee83e01", "72e5e883baa14b3aaabdb81b37d0d498", "59b48708db0c4d599c25b78d9c88af6b", "48ff319ca88f4ac28d9caaf2ced874ca", "c9329f7456f94fd68a281c0288c12c19", "ece1748d891043b182b52832e3e3c67f", "db833c0752ba421ca4c9fdae51e6bec1", "7e8866578c24477a8ac3aa807e99c9ed", "1eb9c0011ab941f287576dbb447dc198", "b7a98b5945204fd8b68c2321eaf463c8", "0877736d2ab84fb09ba0e0f3409a8c32", "1866612b3bbf4d30b9ab5f385e61e48b", "4253aae8670e43dcaf1f41c2768e3f59", "4704907589824f828e125126f8b7a7d1", "2e3373bc8d21449dacf0fef22ad0aaa6", "8d2e90b4172c46d893850a89b4a4af96", "fa594532acad4d6ea687388df12a0c24", "106220bc749d49618d446d77f53a35e0", "6fb2fb53a94142c6906336437ca69f86", "315a5c13479b47fc996286faad3ffc38", "e14d270aed994c0ab6495c2ce33c5758", "787d49e0a71b4a1eb689f49f1ef5ae8e", "63728074beea4aa687647d26511ffc86", "82795cdde81648d0b7ded1cfcaf5d324", "809ab57ea24a444f9727d32efcd4e4d4", "c10fdf858f384369a7026c523cea9258", "5943413fff614ed7b369042ebaf945d5", "593f695216924cc2ba94c7b9b997f2ed", "e9ba9e960c874442ba6c702533af54e4", "2c1a790629fc4134bae1b2629052e372", "8d33969138fb4549acd4ae29db580cb1", "cccd480b105f471ea0ed663acd18590f", "47770267ecf246f49091c84bc859c8d0" ] }, "id": "g8uOOI_efF8S", "outputId": "b688e906-61d2-41ab-cb7d-589d66891bf8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "03a0fad49b1e4dc0b0c409156365865c", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/9912422 [00:00 EPS: # if setting dropout_prob=0.0 then constructing a deterministic CNN\n", " net.add_module(f'Dropout{i}', nn.Dropout(dropout_prob))\n", " net.add_module(f'Nonlinarity{i}', nonlinearity)\n", " net.add_module(f'Pooling{i}', nn.MaxPool2d(kernel_size=2, ceil_mode=True))\n", " # add in the last fully connected layer\n", " # the image sizes goes from 28*28 -> 14*14 -> 7*7 -> 4*4\n", " net.add_module(f'Flatten', nn.Flatten(start_dim=1, end_dim=-1))\n", " net.add_module(f'Linear', nn.Linear(128*4*4, n_class))\n", " return net\n", "\n", "# define the prediction function with Monte Carlo sampling using K samples\n", "def predict(bnn, x_test, K=1, use_dropout=True, reduce_mean=True):\n", " y_pred = []\n", " if use_dropout:\n", " bnn.train() # this will turn on dropout in prediction time, if applicable\n", " else:\n", " bnn.eval() # turn off dropout, if applicable\n", " for _ in range(K):\n", " y_pred.append(F.softmax(bnn(x_test), dim=-1))\n", " # shape (K, batch_size, y_dim) or (batch_size, y_dim) if K = 1\n", " y_pred = torch.stack(y_pred, dim=0).squeeze(0)\n", " if reduce_mean and K > 1: # \"ensemble\"\n", " y_pred = y_pred.mean(0)\n", " return y_pred" ] }, { "cell_type": "markdown", "metadata": { "id": "wPwN7r3X5W_M" }, "source": [ "Adding some helper functions..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qPHRp8RCxtCy" }, "outputs": [], "source": [ "def to_numpy(x):\n", " return x.detach().cpu().numpy() # convert a torch tensor to a numpy array\n", "\n", "# helper functions for training\n", "def train_step(net, opt, dataloader, device):\n", " logs = []\n", " for _, (x, y) in enumerate(dataloader):\n", " x = x.to(device); y = y.to(device)\n", " opt.zero_grad() # opt is the optimiser\n", " y_logit = net(x)\n", " # training accruacy (on a mini-batch)\n", " pred = y_logit.data.max(1, keepdim=True)[1] # get the index of the max logit\n", " acc = pred.eq(y.data.view_as(pred)).float().cpu().mean()\n", " # training loss\n", " nll = F.nll_loss(F.log_softmax(y_logit, dim=-1), y)\n", " loss = nll # note that by using AdamW optimizer, weight-decay is implicitly handled there\n", " loss.backward()\n", " opt.step()\n", " logs.append([to_numpy(nll), to_numpy(acc)])\n", " return np.array(logs)\n", "\n", "# define the training function\n", "def train_network(net, opt, dataloader, device, N_epochs=2000, verbose=True):\n", " net.train()\n", " logs = []\n", " for i in range(N_epochs):\n", " logs_epoch = train_step(net, opt, dataloader, device)\n", " if verbose:\n", " print(\"Epoch {}, last mini-batch nll={}, acc={}\".format(i+1, logs_epoch[-1][0], logs_epoch[-1][1]))\n", " logs.append(logs_epoch)\n", " return np.concatenate(logs, axis=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "leKbq9FN5cCn" }, "source": [ "Now we train a Bayesian CNN using MC-dropout" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 732 }, "id": "97QSPqBgxasT", "outputId": "4a1097ab-247b-49f1-a484-e0871aa34a9c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "using cuda:0\n", "Sequential(\n", " (Conv0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", " (Dropout0): Dropout(p=0.1, inplace=False)\n", " (Nonlinarity0): ReLU()\n", " (Pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Conv1): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", " (Dropout1): Dropout(p=0.1, inplace=False)\n", " (Nonlinarity1): ReLU()\n", " (Pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Conv2): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same)\n", " (Dropout2): Dropout(p=0.1, inplace=False)\n", " (Nonlinarity2): ReLU()\n", " (Pooling2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Flatten): Flatten(start_dim=1, end_dim=-1)\n", " (Linear): Linear(in_features=2048, out_features=10, bias=True)\n", ")\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1, last mini-batch nll=0.09077185392379761, acc=0.9599999785423279\n", "Epoch 2, last mini-batch nll=0.04196837544441223, acc=0.9900000095367432\n", "Epoch 3, last mini-batch nll=0.024989120662212372, acc=0.9900000095367432\n", "Epoch 4, last mini-batch nll=0.024478323757648468, acc=0.9900000095367432\n", "Epoch 5, last mini-batch nll=0.019361017271876335, acc=0.9900000095367432\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# constructing and training a Bayesian CNN\n", "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n", "print(\"using {}\".format(device))\n", "dropout_prob = 0.1\n", "activation = 'ReLU'\n", "dropout_bnn = make_bayesian_cnn(in_channel=1, n_class=10, dropout_prob=dropout_prob, \n", " activation=activation)\n", "dropout_bnn.to(device)\n", "print(dropout_bnn)\n", "\n", "# start training\n", "learning_rate = 1e-3\n", "weight_decay=0.1\n", "N_epochs = 5\n", "# note that weight decay (i.e., prior loss) is implicitly handled by AdamW optimizer\n", "opt = torch.optim.AdamW(dropout_bnn.parameters(), lr=learning_rate, \n", " weight_decay=weight_decay)\n", "# the training loop starts\n", "logs = train_network(dropout_bnn, opt, train_loader, device, N_epochs)\n", "\n", "# plot the training curve\n", "def plot_training_loss(logs, dropout_prob, weight_decay):\n", " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n", " ax1.plot(np.arange(logs.shape[0]), logs[:, 0], 'r-', label='nll')\n", " ax2.plot(np.arange(logs.shape[0]), logs[:, 1], 'r-', label='acc')\n", " ax1.legend()\n", " ax2.legend()\n", " ax1.set_xlabel('epoch')\n", " ax2.set_xlabel('epoch')\n", " ax1.set_title('p={}, weight_decay={}'.format(dropout_prob, weight_decay))\n", " ax2.set_title('p={}, weight_decay={}'.format(dropout_prob, weight_decay))\n", " plt.show()\n", "\n", "plot_training_loss(logs, dropout_prob, weight_decay)" ] }, { "cell_type": "markdown", "metadata": { "id": "woEZgYdi5iSc" }, "source": [ "Training is finished, we now evaluate its test accuracy. Remember to turn on dropout in test time and do multiple forward passes." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x5Tac3ju1h5U", "outputId": "1e4c1d6f-97ca-45bd-fffc-4a390e1983c2" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Test Accuracy: 99.19999694824219%\n" ] } ], "source": [ "# test the BNN\n", "def evaluate(model, dataloader, device, K=50):\n", " accuracy = 0\n", " for x, y in test_loader:\n", " x = x.to(device); y = y.to(device)\n", " # the \"predict\" function: by default dropout is on and we use K > 1 MC samples\n", " y_pred = predict(model, x, K, reduce_mean=True)\n", " pred = y_pred.data.max(1, keepdim=True)[1] # get the index of the max probability\n", " accuracy += pred.eq(y.data.view_as(pred)).float().cpu().sum()\n", " accuracy = accuracy / len(dataloader.dataset) * 100 # accuracy in percentage\n", " return to_numpy(accuracy)\n", "\n", "accuracy = evaluate(dropout_bnn, test_loader, device, K=5)\n", "print('Test Accuracy: {}%'.format(accuracy))" ] }, { "cell_type": "markdown", "metadata": { "id": "-pq1rMfHCwkO" }, "source": [ "## MFVI for Bayesian CNN\n", "\n", "We will also train a Bayesian CNN with MFVI approximations. For this we will use the ```Bayesianize``` package. ```Bayesianize``` is a lightweight pytorch package which can convert any MLPs or CNNs to their \"Bayesian\" version using variational inference.\n", "\n", "First we clone and import the package." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0ez5JMLVDTN4", "outputId": "cc4ef5b7-a89a-4da9-fd45-380a56d8d0ee" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cloning into 'bayesianize'...\n", "remote: Enumerating objects: 74, done.\u001b[K\n", "remote: Counting objects: 100% (74/74), done.\u001b[K\n", "remote: Compressing objects: 100% (66/66), done.\u001b[K\n", "remote: Total 74 (delta 14), reused 0 (delta 0), pack-reused 0\u001b[K\n", "Unpacking objects: 100% (74/74), done.\n" ] } ], "source": [ "!git clone https://github.com/microsoft/bayesianize.git" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vbEzUhvoDXvT" }, "outputs": [], "source": [ "import sys\n", "sys.path.append('bayesianize/')\n", "import bnn" ] }, { "cell_type": "markdown", "metadata": { "id": "BZJiHXvB60Fh" }, "source": [ "We prepare some helper training functions in accordance to the usage of the ```Bayesianize``` package. The idea is similar to the ones in the regression tutorial: we need to compute both the \"data losses\" (i.e., the negative log-likelihood terms) as well as the KL terms." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hsprjWfBF4Sd" }, "outputs": [], "source": [ "# helper functions for training\n", "def train_bnn_step(net, opt, dataloader, device, beta):\n", " logs = []\n", " N_data = len(dataloader.dataset)\n", " for _, (x, y) in enumerate(dataloader):\n", " x = x.to(device); y = y.to(device)\n", " opt.zero_grad() # opt is the optimiser\n", " y_logit = net(x)\n", " # training accruacy (on a mini-batch)\n", " pred = y_logit.data.max(1, keepdim=True)[1] # get the index of the max logit\n", " acc = pred.eq(y.data.view_as(pred)).float().cpu().mean()\n", " # training loss\n", " nll = F.nll_loss(F.log_softmax(y_logit, dim=-1), y)\n", " kl = sum(m.kl_divergence() for m in net.modules() if hasattr(m, \"kl_divergence\"))\n", " loss = N_data * nll + beta * kl\n", " loss.backward()\n", " opt.step()\n", " logs.append([to_numpy(nll), to_numpy(acc), to_numpy(kl)])\n", " return np.array(logs)\n", "\n", "# define the training function\n", "def train_bnn(net, opt, dataloader, device, N_epochs=2000, beta=1.0, verbose=True):\n", " net.train()\n", " logs = []\n", " for i in range(N_epochs):\n", " logs_epoch = train_bnn_step(net, opt, dataloader, device, beta)\n", " if verbose:\n", " print(\"Epoch {}, last mini-batch nll={}, acc={}, kl={}\".format(\n", " i+1, logs_epoch[-1][0], logs_epoch[-1][1], logs_epoch[-1][2]))\n", " logs.append(logs_epoch)\n", " return np.concatenate(logs, axis=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "IQbuHwZZ7PjY" }, "source": [ "Now we train a Bayesian CNN with MFVI as for approximate posterior inference." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 662 }, "id": "H5WrGFI6D53-", "outputId": "fc5148a8-9746-414e-98ab-43f1e4f98de2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sequential(\n", " (Conv0): FFGConv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity0): ReLU()\n", " (Pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Conv1): FFGConv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity1): ReLU()\n", " (Pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Conv2): FFGConv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity2): ReLU()\n", " (Pooling2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Flatten): Flatten(start_dim=1, end_dim=-1)\n", " (Linear): FFGLinear(in_features=2048, out_features=10, bias=True, prior mean=0.00, prior sd=1.00)\n", ")\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1, last mini-batch nll=0.08229530602693558, acc=0.9800000190734863, kl=322783.625\n", "Epoch 2, last mini-batch nll=0.03010711260139942, acc=0.9900000095367432, kl=261299.515625\n", "Epoch 3, last mini-batch nll=0.11150902509689331, acc=0.9599999785423279, kl=205361.4375\n", "Epoch 4, last mini-batch nll=0.03091660514473915, acc=0.9800000190734863, kl=158070.546875\n", "Epoch 5, last mini-batch nll=0.1486053615808487, acc=0.949999988079071, kl=120133.6953125\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "activation = 'ReLU'\n", "# use dropout_prob = 0.0 effectively constructs a deterministic CNN\n", "mfvi_bnn = make_bayesian_cnn(in_channel=1, n_class=10, dropout_prob=0.0, \n", " activation=activation)\n", "# use the \"Bayesianize\" package to make a Bayesian CNN with MFVI\n", "# inference='ffg' means using fully-factorised Gaussian for VI, i.e., MFVI\n", "# see https://github.com/microsoft/bayesianize/blob/main/bnn/nn/mixins/variational/ffg.py\n", "bnn.bayesianize_(mfvi_bnn, inference='ffg', init_sd=0.02)\n", "mfvi_bnn.to(device)\n", "print(mfvi_bnn)\n", "\n", "# start training\n", "learning_rate = 1e-3\n", "N_epochs = 5\n", "beta = 1.0\n", "opt = torch.optim.Adam(mfvi_bnn.parameters(), lr=learning_rate)\n", "# the training loop starts\n", "logs = train_bnn(mfvi_bnn, opt, train_loader, device, N_epochs, beta)\n", "\n", "# plot the training curve\n", "def plot_training_loss(logs, title):\n", " fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12, 4))\n", " ax1.plot(np.arange(logs.shape[0]), logs[:, 0], 'r-', label='nll')\n", " ax2.plot(np.arange(logs.shape[0]), logs[:, 1], 'r-', label='acc')\n", " ax3.plot(np.arange(logs.shape[0]), logs[:, 2], 'r-', label='KL')\n", " for ax in [ax1, ax2, ax3]:\n", " ax.legend()\n", " ax.set_xlabel('epoch')\n", " ax1.set_title(title)\n", " plt.show()\n", "\n", "plot_training_loss(logs, title='MFVI')" ] }, { "cell_type": "markdown", "metadata": { "id": "otYKHXCKKtqm" }, "source": [ "Training is finished, then we evaluate the accuracy of MFVI Bayesian CNN." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sEjZezhBIXGt", "outputId": "f8edb599-01d7-4b9d-fdcb-b65ac83a5290" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Test Accuracy: 98.63999938964844%\n" ] } ], "source": [ "accuracy = evaluate(mfvi_bnn, test_loader, device, K=5)\n", "print('Test Accuracy: {}%'.format(accuracy))" ] }, { "cell_type": "markdown", "metadata": { "id": "jzYt8P6t-i5R" }, "source": [ "# Case study 2: Detecting adversarial attacks\n", "\n", "Neural networks has been shown to be vulnerable to adversarial attacks. In this case study, we will see whether BNNs can be more robust or not, as well as how to use uncertainty measures from BNNs to detect adversarial examples.\n", "\n", "\n", "But first let's set-up the helper functions for the adversarial attack. Here we consider the $L_\\infty$-norm projected gradient descent (PGD) attack [(Madry et al. ICLR 2018)](https://arxiv.org/abs/1706.06083) which is one of the strongest attack considerred in adversarial robustness literature." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cNuL8oCJ_dC3" }, "outputs": [], "source": [ "# PGD attack for one step\n", "def pgd_one_step(x, x_grad, x_clean, epsilon, lr, x_min, x_max):\n", " # use scaled element-wise sign of the data gradient for attack\n", " x_adv = x + lr * x_grad.sign()\n", " x_adv = torch.clamp(x_adv, min=x_min, max=x_max) # make sure x is valid\n", " # project the attack back to the epsilon-ball (L-inf norm)\n", " x_adv = x_clean + torch.clamp(x_adv - x_clean, min=-epsilon, max=epsilon)\n", " return x_adv\n", "\n", "def pgd_attack(x, y, model, predict_func, epsilon, T, lr, x_min, x_max):\n", " x_adv = x.detach().clone()\n", " x_adv.requires_grad = True # we need to compute gradients w.r.t. the input\n", " # perform PGD attack for T steps\n", " for t in range(T):\n", " # Forward pass the data through the model and get loss\n", " y_pred = predict_func(x_adv, reduce_mean=True) # return prediction probabilities\n", " loss = F.nll_loss(torch.log(torch.clamp(y_pred, min=EPS)), y)\n", " # One-step PGD using gradients of loss w.r.t. input in backward pass\n", " if type(model) is list:\n", " for net in model:\n", " net.zero_grad()\n", " else:\n", " model.zero_grad()\n", " loss.backward()\n", " x_adv.data = pgd_one_step(x_adv.data, x_adv.grad.data, x, epsilon, lr, x_min, x_max)\n", " return x_adv" ] }, { "cell_type": "markdown", "metadata": { "id": "YfD0zwEO9ZW2" }, "source": [ "We first test the PGD attack on a deterministic network (as to validate the implementation). For this deterministic network, we just need to turn off dropout during test time." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "2i8dSWHmGcDc", "outputId": "55f4d4f7-87e2-47b3-dc4c-1df0736389da" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", " after removing the cwd from sys.path.\n", "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", " \"\"\"\n", "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "entropy (clean) 0.048386477\n", "clean accruacy for this batch: 100.0%\n", "adv accruacy for this batch (%): [95. 79. 29. 2. 0. 0. 0. 0.]\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# testing adversarial attacks\n", "# we will use 100 test samples as examples\n", "x_test, y_test = next(iter(test_loader))\n", "x_test = torch.tensor(x_test, device=device)\n", "y_test = torch.tensor(y_test, device=device)\n", "\n", "# define some helper functions for the attack\n", "def run_attack(model, x_test, y_test, predict_func, epsilon_list, T):\n", " acc_adv = []\n", " pred_adv = []\n", " prob_adv = []\n", " x_adv = []\n", " for epsilon in epsilon_list:\n", " lr = epsilon / T * 1.5\n", " x_adv_ = pgd_attack(x_test, y_test, model, predict_func, \n", " epsilon, T, lr, x_min=0.0, x_max=1.0)\n", " # get the accuracy on crafted adversarial examples\n", " y_pred = predict_func(x_adv_, reduce_mean=True)\n", " pred = y_pred.data.max(1, keepdim=True)[1] # get the index of the max probability\n", " acc_adv.append(to_numpy(pred.eq(y_test.data.view_as(pred)).float().mean()))\n", " pred_adv.append(to_numpy(pred))\n", " prob_adv.append(to_numpy(y_pred))\n", " x_adv.append(to_numpy(x_adv_))\n", " return np.array(acc_adv), np.array(pred_adv), np.array(prob_adv), np.array(x_adv)\n", "\n", "# helper function for visualization\n", "def visualize(images, epsilons, pred_clean, pred_adv):\n", " cnt = 0\n", " plt.figure(figsize=(8, 2 * len(epsilons)))\n", " for i in range(len(epsilons)):\n", " for j in range(len(images[i])):\n", " cnt += 1\n", " plt.subplot(len(epsilons),len(images[0]),cnt)\n", " plt.xticks([], [])\n", " plt.yticks([], [])\n", " if j == 0:\n", " plt.ylabel(\"Eps: {:.2f}\".format(epsilons[i]), fontsize=14)\n", " plt.title(\"{} -> {}\".format(pred_clean[j], pred_adv[i, j]))\n", " plt.imshow(images[i, j][0], cmap=\"gray\")\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "# we first test a deterministic NN's robustness, by turning off dropout\n", "predict_deterministic = lambda x, reduce_mean: predict(dropout_bnn, x, K=1, use_dropout=False, \n", " reduce_mean=reduce_mean)\n", "\n", "def test_robustness(model, predict_func, x_test, y_test):\n", " # first test clean accuracy\n", " y_pred = predict_func(x_test, reduce_mean=True)\n", " entropy = -(y_pred * torch.log(y_pred)).sum(-1).mean()\n", " print(\"entropy (clean)\", to_numpy(entropy))\n", " pred_clean = y_pred.data.max(1, keepdim=True)[1] # get the index of the max probability\n", " acc_clean = to_numpy(pred_clean.eq(y_test.data.view_as(pred_clean)).float().mean())\n", " pred_clean = to_numpy(pred_clean)\n", " print(\"clean accruacy for this batch: {}%\".format(acc_clean * 100))\n", " \n", " # now run the attack\n", " T = 10\n", " epsilon_list = np.arange(0.05, 0.41, 0.05)\n", " acc_adv, pred_adv, prob_adv, x_adv = run_attack(model, x_test, y_test, predict_func, epsilon_list, T)\n", " print(\"adv accruacy for this batch (%): {}\".format(acc_adv * 100))\n", " # visualise the adversarial examples\n", " visualize(x_adv[:, :5], epsilon_list, pred_clean[:5], pred_adv[:, :5])\n", " return x_adv\n", "\n", "x_adv = test_robustness(dropout_bnn, predict_deterministic, x_test, y_test)" ] }, { "cell_type": "markdown", "metadata": { "id": "5XV-VU6IQqIi" }, "source": [ "Now testing the MC-dropout Bayesian CNN's robustness. \n", "\n", "In this case we will also look at the uncertainty produced by the BNN on the adversarial examples. Specifically, you will implement:\n", "\n", "* total uncertainty: as the total entropy\n", "* aleatoric uncertainty: as the conditional entropy\n", "\n", "Epistemic uncertainty is the mutual information which is essentially total entropy minus conditional entropy." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "yrCJ71w3RA-_", "outputId": "07753092-3ac6-4877-cc2f-1c204fa66548" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "entropy (clean) 0.031681836\n", "clean accruacy for this batch: 100.0%\n", "adv accruacy for this batch (%): [95. 82. 32. 2. 0. 0. 0. 0.]\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "total uncertainty (entropy): [0.11934087 0.3424316 0.52679455 0.25995743 0.0980616 0.0801347\n", " 0.07231481 0.11620294]\n", "aleatoric uncertainty (cond. entropy): [0.11407898 0.3258734 0.50757354 0.25181732 0.09615013 0.078594\n", " 0.07047283 0.11284649]\n", "epistemic uncertainty (mutual info): [0.00526189 0.0165582 0.01922101 0.00814012 0.00191147 0.0015407\n", " 0.00184198 0.00335645]\n" ] } ], "source": [ "# now we test Bayesian CNN's robustness, by using MC-dropout\n", "predict_mcdropout = lambda x, reduce_mean: predict(dropout_bnn, x, K=5, use_dropout=True,\n", " reduce_mean=reduce_mean)\n", "x_adv = test_robustness(dropout_bnn, predict_mcdropout, x_test, y_test)\n", "\n", "# also compute uncertainty measures\n", "\n", "def total_entropy(y_prob_samples):\n", " y_prob = y_prob_samples.mean(0)\n", " return -(y_prob * torch.log(y_prob)).sum(-1)\n", "\n", "def conditional_entropy(y_prob_samples):\n", " return -(y_prob_samples * torch.log(y_prob_samples)).sum(-1).mean(0)\n", "\n", "def compute_uncertainty(predict_func, x_adv, reduce_mean=True):\n", " logs = []\n", " for i in range(x_adv.shape[0]):\n", " x = torch.tensor(x_adv[i], device=device)\n", " y_prob_samples = predict_func(x, reduce_mean=False)\n", " entropy = total_entropy(y_prob_samples)\n", " cond_entropy = conditional_entropy(y_prob_samples)\n", " if reduce_mean:\n", " entropy = entropy.mean(); cond_entropy = cond_entropy.mean()\n", " mutual_info = entropy - cond_entropy\n", " logs.append([to_numpy(entropy), to_numpy(cond_entropy), to_numpy(mutual_info)])\n", " return np.array(logs)\n", "\n", "logs = compute_uncertainty(predict_mcdropout, x_adv)\n", "print(\"total uncertainty (entropy):\", logs[:, 0])\n", "print(\"aleatoric uncertainty (cond. entropy):\", logs[:, 1])\n", "print(\"epistemic uncertainty (mutual info):\", logs[:, 2])" ] }, { "cell_type": "markdown", "metadata": { "id": "G6910FAQ9gOf" }, "source": [ "And similarly for the MFVI Bayesian CNN, test its adversarial robustness." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "KBVKHfvdCRkR", "outputId": "303340b0-5c07-43b7-f328-717065140211" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "entropy (clean) 0.09574664\n", "clean accruacy for this batch: 98.99999499320984%\n", "adv accruacy for this batch (%): [91.99999 76. 44. 14. 1. 0. 0. 0. ]\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "total uncertainty (entropy): [0.26186725 0.53992885 0.7552825 0.7608884 0.67691636 0.5337778\n", " 0.4432854 0.4012931 ]\n", "aleatoric uncertainty (cond. entropy): [0.18721826 0.3845276 0.54575104 0.5465816 0.5060795 0.4052427\n", " 0.32411587 0.28704536]\n", "epistemic uncertainty (mutual info): [0.07464899 0.15540126 0.20953149 0.21430677 0.17083687 0.12853506\n", " 0.11916953 0.11424774]\n" ] } ], "source": [ "# now we test Bayesian CNN's robustness, by using MFVI\n", "predict_mfvi = lambda x, reduce_mean: predict(mfvi_bnn, x, K=5, reduce_mean=reduce_mean)\n", "x_adv = test_robustness(mfvi_bnn, predict_mfvi, x_test, y_test)\n", "# also compute epistemic uncertainty\n", "logs = compute_uncertainty(predict_mfvi, x_adv)\n", "print(\"total uncertainty (entropy):\", logs[:, 0])\n", "print(\"aleatoric uncertainty (cond. entropy):\", logs[:, 1])\n", "print(\"epistemic uncertainty (mutual info):\", logs[:, 2])" ] }, { "cell_type": "markdown", "metadata": { "id": "-XNhVvJnf6NG" }, "source": [ "## Can ensembles help?\n", "\n", "From the above examples we see that the trained BNNs help a bit in adversarial robustness but not too much, especially for the MC-dropout one. From the printed uncertainty measures, we suspect the trained networks may be still not uncertain enough on the adversarial examples.\n", "\n", "Next we will train an Ensemble of MFVI-BNNs. This means we will train multiple MFVI-BNNs independently with different random initialisation of the variational parameters, and then ensemble them together to get the final model. Although diversity is not explicitly encouraged, the hope is that the randomness in initialisation and SGD steps would allow different independent runs to result in different local optima, resulting in a more diversed set of neural networks.\n", "\n", "Please run the code in below block, it might take a few minutes to train the MFVI-BNNs in the ensemble." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FdvAe0Smf8w6", "outputId": "da033c9b-d4ca-4020-93be-221337ca1a37" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train a new MFVI CNN (network 2)\n", "Sequential(\n", " (Conv0): FFGConv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity0): ReLU()\n", " (Pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Conv1): FFGConv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity1): ReLU()\n", " (Pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Conv2): FFGConv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity2): ReLU()\n", " (Pooling2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Flatten): Flatten(start_dim=1, end_dim=-1)\n", " (Linear): FFGLinear(in_features=2048, out_features=10, bias=True, prior mean=0.00, prior sd=1.00)\n", ")\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1, last mini-batch nll=0.10069575905799866, acc=0.9700000286102295, kl=322410.0\n", "Epoch 2, last mini-batch nll=0.029820876196026802, acc=0.9900000095367432, kl=260510.546875\n", "Epoch 3, last mini-batch nll=0.09871918708086014, acc=0.9700000286102295, kl=204004.53125\n", "Epoch 4, last mini-batch nll=0.04804856702685356, acc=0.9800000190734863, kl=156878.75\n", "Epoch 5, last mini-batch nll=0.02621012181043625, acc=0.9900000095367432, kl=119781.921875\n", "train a new MFVI CNN (network 3)\n", "Sequential(\n", " (Conv0): FFGConv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity0): ReLU()\n", " (Pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Conv1): FFGConv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity1): ReLU()\n", " (Pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Conv2): FFGConv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity2): ReLU()\n", " (Pooling2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Flatten): Flatten(start_dim=1, end_dim=-1)\n", " (Linear): FFGLinear(in_features=2048, out_features=10, bias=True, prior mean=0.00, prior sd=1.00)\n", ")\n", "Epoch 1, last mini-batch nll=0.03743768855929375, acc=0.9800000190734863, kl=322481.71875\n", "Epoch 2, last mini-batch nll=0.04986201226711273, acc=0.9900000095367432, kl=260540.515625\n", "Epoch 3, last mini-batch nll=0.021377606317400932, acc=0.9900000095367432, kl=203769.203125\n", "Epoch 4, last mini-batch nll=0.03626023605465889, acc=0.9800000190734863, kl=156212.09375\n", "Epoch 5, last mini-batch nll=0.12863950431346893, acc=0.9700000286102295, kl=118506.0078125\n", "train a new MFVI CNN (network 4)\n", "Sequential(\n", " (Conv0): FFGConv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity0): ReLU()\n", " (Pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Conv1): FFGConv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity1): ReLU()\n", " (Pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Conv2): FFGConv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity2): ReLU()\n", " (Pooling2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Flatten): Flatten(start_dim=1, end_dim=-1)\n", " (Linear): FFGLinear(in_features=2048, out_features=10, bias=True, prior mean=0.00, prior sd=1.00)\n", ")\n", "Epoch 1, last mini-batch nll=0.04194052144885063, acc=0.9900000095367432, kl=322351.03125\n", "Epoch 2, last mini-batch nll=0.03986191004514694, acc=0.9800000190734863, kl=260487.46875\n", "Epoch 3, last mini-batch nll=0.05239361897110939, acc=0.9800000190734863, kl=204208.953125\n", "Epoch 4, last mini-batch nll=0.15761151909828186, acc=0.9599999785423279, kl=157246.28125\n", "Epoch 5, last mini-batch nll=0.025944700464606285, acc=0.9900000095367432, kl=120404.96875\n", "train a new MFVI CNN (network 5)\n", "Sequential(\n", " (Conv0): FFGConv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity0): ReLU()\n", " (Pooling0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Conv1): FFGConv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity1): ReLU()\n", " (Pooling1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Conv2): FFGConv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=same, prior mean=0.00, prior sd=1.00)\n", " (Nonlinarity2): ReLU()\n", " (Pooling2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", " (Flatten): Flatten(start_dim=1, end_dim=-1)\n", " (Linear): FFGLinear(in_features=2048, out_features=10, bias=True, prior mean=0.00, prior sd=1.00)\n", ")\n", "Epoch 1, last mini-batch nll=0.11387774348258972, acc=0.9700000286102295, kl=322786.84375\n", "Epoch 2, last mini-batch nll=0.13871386647224426, acc=0.9599999785423279, kl=261127.609375\n", "Epoch 3, last mini-batch nll=0.048318635672330856, acc=0.9900000095367432, kl=205317.1875\n", "Epoch 4, last mini-batch nll=0.0341961495578289, acc=1.0, kl=158691.265625\n", "Epoch 5, last mini-batch nll=0.06055455654859543, acc=0.9700000286102295, kl=122037.7109375\n" ] } ], "source": [ "# train an ensemble of MFVI networks (independently) and see what happens\n", "mfvi_bnn_list = [mfvi_bnn]\n", "N_ensemble = 5\n", "activation = 'ReLU'\n", "learning_rate = 1e-3\n", "N_epochs = 5\n", "beta = 1.0\n", "\n", "for i in range(1, N_ensemble):\n", " print(\"train a new MFVI CNN (network {})\".format(i+1))\n", " net = make_bayesian_cnn(in_channel=1, n_class=10, dropout_prob=0.0, \n", " activation=activation)\n", " bnn.bayesianize_(net, inference='ffg', init_sd=0.02)\n", " net.to(device)\n", " print(net)\n", " # start training\n", " opt = torch.optim.Adam(net.parameters(), lr=learning_rate)\n", " logs = train_bnn(net, opt, train_loader, device, N_epochs, beta)\n", " mfvi_bnn_list.append(net)" ] }, { "cell_type": "markdown", "metadata": { "id": "Uct-1Us2MuzT" }, "source": [ "Training is now finished, let's see how the ensemble performs on clean data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pg3t3s-Gjsu-", "outputId": "9cd5548b-3b8c-4eca-d36e-e32b640981e7" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Test Accuracy: 99.0199966430664%\n" ] } ], "source": [ "# test the ensemble on clean test data\n", "def predict_ensemble(x_test, K=5, reduce_mean=True):\n", " y_prob_samples = []\n", " for net in mfvi_bnn_list:\n", " y_prob_samples.append(predict(net, x_test, K=K, reduce_mean=False))\n", " y_prob_samples = torch.concat(y_prob_samples, axis=0)\n", " if reduce_mean:\n", " return y_prob_samples.mean(0)\n", " else:\n", " return y_prob_samples\n", "\n", "accuracy = 0\n", "K = 5\n", "for x, y in test_loader:\n", " x = x.to(device); y = y.to(device)\n", " y_pred = predict_ensemble(x, K)\n", " pred = y_pred.data.max(1, keepdim=True)[1] # get the index of the max probability\n", " accuracy += pred.eq(y.data.view_as(pred)).float().cpu().sum()\n", "accuracy = to_numpy(accuracy / len(test_loader.dataset) * 100) # accuracy in percentage\n", "print('Test Accuracy: {}%'.format(accuracy))" ] }, { "cell_type": "markdown", "metadata": { "id": "4vuukXBVMzfb" }, "source": [ "Then we perform PGD attacks on the ensemble BNN." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "m4_eVAjYjpxJ", "outputId": "9c73ad26-e767-4f05-cd9f-5607d889622c" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "entropy (clean) 0.10990928\n", "clean accruacy for this batch: 100.0%\n", "adv accruacy for this batch (%): [94. 77. 42. 6. 0. 0. 0. 0.]\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "total uncertainty (entropy): [0.34317023 0.70485646 0.9586889 0.98697364 0.86825347 0.80917144\n", " 0.7803755 0.73641235]\n", "aleatoric uncertainty (cond. entropy): [0.20037204 0.44248432 0.62013936 0.69085914 0.63022095 0.58786035\n", " 0.5421429 0.485346 ]\n", "epistemic uncertainty (mutual info): [0.14279819 0.26237214 0.33854955 0.2961145 0.23803252 0.22131109\n", " 0.23823255 0.25106636]\n" ] } ], "source": [ "# test for adversarial attacks\n", "x_adv = test_robustness(mfvi_bnn_list, predict_ensemble, x_test, y_test)\n", "# also compute epistemic uncertainty\n", "logs = compute_uncertainty(predict_ensemble, x_adv)\n", "print(\"total uncertainty (entropy):\", logs[:, 0])\n", "print(\"aleatoric uncertainty (cond. entropy):\", logs[:, 1])\n", "print(\"epistemic uncertainty (mutual info):\", logs[:, 2])" ] }, { "cell_type": "markdown", "metadata": { "id": "cAMnjnwM9dkH" }, "source": [ "# Quantitative metric for detection results\n", "\n", "The ensemble BNN above makes a bit of improvement on robustness but not too much, however, in terms of uncertainty measures, the ensemble becomes more uncertain on the adversarial inputs. But at the same time, the total entropy on clean inputs remains relatively close to the single MFVI-BNN case. This is helpful as it will be easier to separate clean/adversarial examples by looking at the uncertainty measures and picking appropriate thresholds for dectection.\n", "\n", "In below blocks, we will quantify the quality of uncertainty by considering its effectiveness as a tool for adversarial example detection. The idea is, for a given uncertainty measure (e.g., total entropy):\n", "\n", "* for a given threshold, predict the input as \"adversarial\" if uncertainty > threshold;\n", "* search for the best threshold so that it returns the best true positive rate (TPR) of detecting adversarial example, while maintaining the false positive rate (FPR) to be below 5%.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qXcybj_-Qpu1" }, "outputs": [], "source": [ "# below are helper functions for the adversarial example detection test\n", "\n", "def get_uncertainty_adv_examples(model, predict_func, x_test, y_test, epsilon_list, T=10):\n", " # first get uncertainties on clean examples, return tensor of shape (3, batch_size)\n", " uncertainty_clean = compute_uncertainty(predict_func, x_test.unsqueeze(0), reduce_mean=False)[0]\n", " # now run the attack\n", " acc_adv, pred_adv, prob_adv, x_adv = run_attack(model, x_test, y_test, predict_func, epsilon_list, T)\n", " print(\"adv accruacy for this batch (%): {}\".format(acc_adv * 100))\n", " # get uncertainties on adversarial examples, return tensor of shape (len(epsilon_list, 3, batch_size))\n", " uncertainty_adv = compute_uncertainty(predict_func, x_adv, reduce_mean=False)\n", " return uncertainty_clean, uncertainty_adv\n", "\n", "def compute_detection_error(thresholds, uncertainty, label):\n", " # compute the detection error\n", " # here if uncertainty > threshold then we say it is an OOD example\n", " # where for label, 0 means in-distribution, 1 means OOD\n", " # we assume the first half of the labels are 0 and the second half of the labels are 1\n", " tpr = []; fpr = []\n", " N = int(label.shape[0] / 2)\n", " for t in thresholds:\n", " pred = np.maximum(np.sign(uncertainty - t), 0)\n", " fpr.append(1 - np.mean(np.where(pred[:N] == label[:N], 1, 0)))\n", " tpr.append(np.mean(np.where(pred[N:] == label[N:], 1, 0)))\n", " return fpr, tpr\n", "\n", "def run_detect(model, x_test, y_test, predict_func, epsilon_list, min_fpr = 0.05):\n", " thresholds = np.arange(0.0, np.log(10), 0.05)\n", " fpr_total = 0.0; tpr_total = 0.0\n", " N_data = x_test.shape[0]; batch_size = 100; N_batches = int(N_data / batch_size)\n", " for n in range(N_batches):\n", " x = x_test[n*batch_size:(n+1)*batch_size].to(device)\n", " y = y_test[n*batch_size:(n+1)*batch_size].to(device)\n", " uncertainty_clean, uncertainty_adv = get_uncertainty_adv_examples(model, predict_func, x, y, epsilon_list)\n", " # we assume the first half of the labels are 0 and the second half of the labels are 1\n", " label = np.concatenate([np.zeros(x.shape[0]), np.ones(x.shape[0])])\n", " # len(uncertainty_adv) == len(epsilon_list)\n", " fpr = np.zeros((len(uncertainty_adv), 3, len(thresholds)))\n", " tpr = np.zeros((len(uncertainty_adv), 3, len(thresholds)))\n", " for i in range(len(uncertainty_adv)):\n", " for j in range(3):\n", " uncertainty = np.concatenate([uncertainty_clean[j], uncertainty_adv[i][j]])\n", " fpr[i, j], tpr[i, j] = compute_detection_error(thresholds, uncertainty, label)\n", " fpr_total = fpr_total + fpr * batch_size / N_data\n", " tpr_total = tpr_total + tpr * batch_size / N_data\n", " # now we compute the best tpr given that the corresponding fpr <= min_fpr\n", " mask = np.where(fpr_total <= min_fpr, 1, 0)\n", " tpr_best = np.max(tpr_total * mask, axis=-1)\n", " # plot the result\n", " plt.subplots(1, 1, figsize=(5, 4))\n", " plt.plot(epsilon_list, tpr_best[:, 0], 'k-', label='total entropy')\n", " plt.plot(epsilon_list, tpr_best[:, 1], 'b-', label='cond entropy')\n", " plt.plot(epsilon_list, tpr_best[:, 2], 'r-', label='mutual info')\n", " plt.legend()\n", " plt.xlabel('epsilon')\n", " plt.title('Best TPR (with FPR <= %.2f)' % min_fpr)\n", " plt.show()\n", " return tpr_best" ] }, { "cell_type": "markdown", "metadata": { "id": "dC0_tU4fmdJ9" }, "source": [ "Now we can test for adversarial detection. To do so, we randomly sample 1000 (10*100) datapoints from the test data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lB_VDIV6OuiK" }, "outputs": [], "source": [ "# get N_batch*100 test images to test detection\n", "count_batch = 0\n", "N_batch = 10\n", "x_clean = []; y_clean = []\n", "for x, y in test_loader:\n", " x_clean.append(x); y_clean.append(y)\n", " count_batch += 1\n", " if count_batch >= N_batch:\n", " break\n", "x_clean = torch.concat(x_clean, dim=0); y_clean = torch.concat(y_clean, dim=0)" ] }, { "cell_type": "markdown", "metadata": { "id": "iPx7CvK-OvdV" }, "source": [ "First we evaluate the MC-dropout method." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 575 }, "id": "u_7dewHElx12", "outputId": "04b5dda8-cd6f-4136-c24e-6a6ea21beeee" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "adv accruacy for this batch (%): [96. 82. 33. 2. 0. 0. 0. 0.]\n", "adv accruacy for this batch (%): [96. 76. 38. 2. 0. 0. 0. 0.]\n", "adv accruacy for this batch (%): [95. 71. 24. 1. 0. 0. 0. 0.]\n", "adv accruacy for this batch (%): [94. 75. 26.999998 1. 0. 0. 0.\n", " 0. ]\n", "adv accruacy for this batch (%): [90. 62. 18. 3. 0. 0. 0. 0.]\n", "adv accruacy for this batch (%): [95. 72. 22. 1. 0. 0. 0. 0.]\n", "adv accruacy for this batch (%): [91.99999 72. 34. 2. 0. 0. 0. 0. ]\n", "adv accruacy for this batch (%): [96. 79.99999 26. 2. 0. 0. 0. 0. ]\n", "adv accruacy for this batch (%): [89. 72. 26. 0. 0. 0. 0. 0.]\n", "adv accruacy for this batch (%): [91.99999 70. 22.999998 1. 0. 0. 0.\n", " 0. ]\n" ] }, { "data": { "image/png": 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zzEHIRvGVvp0ws/nz57Nt2zbCwsJcvs68BN4RwLmZUNXxnLM4YKGqXlHVg8BerAA08khVmTBhAq1atUK1GtWrx7F+fU3eeAOmT7e2z+XV0KHlgDA+++yA2+o1DE+Ul8BbD9QWkRoiUgLoAyzMNM8CrNYdIhKG1cU137Y8SkxMZMiQITz00EM0avQESUmrOX68FF9/Df/8J+R3UIgOHSAoKInduyP5/fff3VO0YXigXANPVVOAEcASYBcwW1V3iMgoEenimG0JcEpEdgIrgWdU9ZS7ivYmhw4d4tZbb2XSpEl07LiYTZve4Npr/Vi3Du68s2DLDA6GO++8DNzLzJmmW2sY6fK0DU9Vv1HVOqp6g6r+x/HcK6q60HFfVfUpVa2nqg1VdaY7i/YWS5YsITo6mn37DnPHHb+xZMlddO4s/Pwz1KlTuGUPGVIOuIaJE7O9Yp1h+BxzapkN0tLS+Pe//82dd95JxYqNqVnzd5YuvZ6XX4YFC6BcucKvo0MHCA5OYs+eRqZba2Rr8ODBGQf/FoYdo7QUhAm8Inb27Fm6du3Kyy+/TIcOL3PmzHfExgYzdy6MGgV+LvqNlCgBd911GejGzJnzXbNQw8hGToGXkpJSxNVkzwReEdq2bRsxMTF8++239O+/jJUrR1KypB8//QT33ef69Vnd2lA++8y08LzB559/nnF2xYABAwAraNq2bUtkZCTt2rXLaM0PHjyYxx9/nFtuuYWaNWtmtOJUlREjRnDjjTfSvn37bE8b279/P506dSI6OppWrVqxe/fuHJebeViqyZMn06VLF9q2bUu7du04ffo03bp1IzIykubNm7Nt2zYARo4cyYABA2jRogW1a9fmk08+AWDgwIEsWLAgo57+/fu7ZkTv7I5IdvfN1860mDZtmpYsWVKvuy5Ce/Y8oqDarp3qyZPuW+fly6olS15SmKKHDh1y34p8gPMR/U88odq6tWtvTzyR8/p//fVXrV27tsbHx6uq6qlTp1RV9e6779bJkyerqupnn32mXbt2VVXVQYMGaY8ePTQ1NVV37NihN9xwg6qqzp07V9u3b68pKSl65MgRDQkJ0Tlz5vxlfW3bttW9e/eqqurPP/+sbdq0yXG5K1eu1Lvuuivj9ZMmTdIqVapk1DlixAgdOXKkqqouX75cGzVqpKqqr776qkZGRuqlS5c0Pj5eq1atqkeOHNFVq1ZlvJezZ89q9erV9cqVKzn+XtKRw5kWpoXnZpcvX+bxxx93DJDYnhtuiGXOnMr8/e/w7bfgwoEg/iIwEO6++wrQjRkzTLfWk61YsYKePXtmHIx7zTXXAPDTTz9lDJ80YMCAjCGWALp164afnx/16tXj+PHjAKxevTpjWKnKlSvTtm3bv6wrISGBH3/8kZ49exIVFcVDDz3EsWPHclxuVu64446MOteuXZvRKm3bti2nTp3i/PnzAHTt2pWSJUsSFhZGmzZtWLduHa1btyY2Npb4+HhmzJhB9+7dCQgo/OBOZngoNzp69Ci9evXihx9+YMCAMaxZ8zTHjglTpsDAgUVTw9Ch5ZgzByZOPMJzWY5zY+SXp4wO5Tyck9XwyZu0tDRCQ0OzHeQ0r8vN6xBRma8+lv544MCBTJ06lZkzZzJp0qQ8LSs3poXnJmvWrCE6OpotW7bwxBM/MHfuP7hyRVizpujCDqBNGyhV6hKxsVH89ps5RMVTtW3bljlz5nDqlHV46+nTpwG45ZZbmDnTOgps2rRptGrVKsfl3HbbbRnDSh07doyVK1f+ZZ5y5cpRo0YN5syZA1ihtnXr1hyXm9MQUWANZzVt2jTAGrE7LCyMco7DEb766iuSkpI4deoUq1atomnTpoC1vTB97MF69eplveB8MoHnYqrKu+++S5s2bShTJoS+fQ8ybtwtREXBhg3g+F0WmcBA6NIlBejCjBkLcp3fKJ7q16/Piy++SOvWrWnUqBFPOUZ4ff/995k0aRKRkZF88cUXjBs3Lsfl3HvvvdSuXZt69eoxcOBAWrRokeV806ZN47PPPqNRo0bUr18/1x0GmYelymzkyJFs3LiRyMhInnvuOaZMmXLVa9u0aUPz5s15+eWXqVy5MgAVK1akbt26rh0TL7uNe+6+eeNOiwsXLmifPn0U0Lvu6qcdO15WUB02TDUpyb66li2zhoyqVeuf9hXh4czwUO7x6quv6pgxY7KcdvHiRa1Zs6aePXs229ebnRY22bt3L82bN2f27Nn8/e8fExs7leXLA/nvf2HChL8O1lmUWreG0qUvsm9f4xyvQ2AYxcWyZcuoW7cujz32GCEhIa5bcHZJ6O6bN7XwFixYoOXKldMKFSrov/+9ScuVUw0PV/3+e7sr+1O/fucUEvQ//3nH7lI8kmnhFU+mhVeEUlNTeeGFF+jWrRu1a9fhwQf38fLLjalVy9ped9ttdlf4p2HDygGlmTQp+8MIDMPbmcAroJMnT9KpUydef/11Bg9+lGrVfmbMmFD69oU1a6C4DejcqhWUKZPAvn1NTLfW8Fkm8AqoT58+rFmzhtdfn8mmTR+wYIE/Y8bA1Kn5G6yzqPj7w733pgF3MXWq2Vtr+CYTeAVw4sQJVqxYQa9eHzF2bG9+/x2++Qb+8Y/8D9ZZlKxubSkmTcr5snuG4a1M4BXAokWLUB3I9OmDqFgR1q+Hjh3trip3LVtC2bIJHDgQw8GDB+0uxyhCZ8+eZfz48YVaRnZDSb3yyissW7Ysx9cmJyfTvn17oqKimDVrVqHqKAwTeAUwf/5C/PzGcPPN8PPPUKuW3RXljZ8fdO+eCnRm6lQXjDxheAxXBF52Ro0aRfv27XOcZ/PmzQBs2bKF3r17u6WOvDCBl08JCQl8990F0tLCeewxoWxZuyvKn6FDQ4BgJk06aXcpRj4cOnSIm266icGDB1OnTh369+/PsmXLaNmyJbVr12bdunWAdUbD2LFjM17XoEEDDh06xHPPPcf+/fuJiorimWeeYdWqVdx9990Z840YMYLJkycDVoA1bdqUBg0aMHz48FzPw3Vu+VWvXp1XX32VJk2a0LBhQ3bv3s2JEye4//77Wb9+PVFRUezfv5/ly5fTuHFjGjZsyIMPPkhycrKLP7GsmcED8mnJkiVcuXIXgYFpdO7sef8vWrSAkJDzHDzYlIMHD1KjRg27S/I8Tz4J2ZxYX2BRUbmOSrBv3z7mzJnDxIkTadq0KdOnT2ft2rUsXLiQ//u//7tq/LjMRo8eza+//poxIMCqVauynXfEiBG88sorgDUCy+LFi7nnnnvy/FbCwsLYtGkT48ePZ+zYsXz66ad8+umnjB07lsWLF5OUlMTtt9/O8uXLqVOnDgMHDuS///0vTz75ZJ7XUVCe94212fz5C/Dz68Edd7hmKPai5ucHPXoo0InPPzfdWk9So0YNGjZsiJ+fH/Xr16ddu3aICA0bNnTpoUYrV67k5ptvpmHDhqxYsYIdO3bk6/X3OUazjY6OzrKuPXv2UKNGDeo4LtwyaNAgVq9eXei688K08PLhypUrfPXVb6SlVaN7d7urKbihQ0P47DOYMuU0r75qdzUeyKbxoZyHZfLz88t47OfnlzGMekBAAGlpaRnzJSUlZbms7OZLSkrikUceYcOGDURERDBy5Mhsl5Fbnf7+/sVqeHcwLbx8Wb16NQkJd+Dnp+SjhV/s3HwzhIZa3doDB8zlg71J9erV2bRpEwCbNm3K2BufefimatWqsXPnTpKTkzl79izLly8H/gy+sLAwEhISXHKBn8xuvPFGDh06xL59+wD44osvaN26tcvXkxUTePmwYMECRO7j1lvTCA+3u5qCE4GePRXoyOefL7K7HMOFunfvzunTp6lfvz4ffPBBRrexQoUKtGzZkgYNGvDMM88QERFBr169aNCgAb169aJx48YAhIaGMmzYMBo0aEDHjh0zxqZzpeDgYCZNmkTPnj0zuugPP/ywy9eTpexOsnW+AZ2APcA+4Lkspg8G4oEtjtvQ3JbpaYMHpKWl6XXXtVZQfe89u6spvHXrrCGjqlV71e5SPIIZPKB4cvngASLiD3wI3AnUA/qKSFbDj85S1SjH7dPCR3HxsnnzZv74ozkA3brZXIwLxMTANdec5bffmplureEz8tKlbQbsU9UDqnoZmAl0dW9ZxY+1y/9eGje+QkSE3dUUngj06iXAHUyebLq1hm/IS+BVAQ47PY5zPJdZdxHZJiJfikiWkSAiw0Vkg4hsiI+PL0C59pk9+yfgZnr1CrS7FJcZNiwECOTzz8/bXYpH0HxcCMdwv4L8Ply102IRUF1VI4GlwJSsZlLVCaoao6ox4R601X///v3s2XMT4J4LZtulcWOoUOGM6dbmQXBwMKdOnTKhV0yoKqdOnSI4ODhfr8vLcXhHAOcWW1XHc84rP+X08FPgzXxVUcxZFzC5j9q1L1OnTgm7y3EZEejd24/x49sxefJ4Ro163O6Siq2qVasSFxeHp/VMvFlwcDBVq1bN12vyEnjrgdoiUgMr6PoA/ZxnEJFKqpp+pd4uwK58VVHMzZmzEniC3r397S7F5YYPD2H8eJgy5TyjRtldTfEVGBhoTsPzArl2aVU1BRgBLMEKstmqukNERolIF8dsj4vIDhHZCjyOdZiKVzhx4gS//FIR8Peq7my6yEgIDz/N7783Z//+/XaXYxhuladteKr6jarWUdUbVPU/judeUdWFjvvPq2p9VW2kqm1Udbc7iy5KixcvRrUblSsnExVldzWuJwJ9+vgBbZg06Wu7yzEMtzJnWuRizpwlwB306lWiWI9mXBjDh4cC/nz++UW7SzEMtzKBl4OEhASWLQsEguje3UvTDqhfHypWPMXhw80zzm80DG9kAi8H3333HSkp91C+fDItWthdjfuIQN++AUBrJk78xu5yDMNtTODlYO7cr4G76NEjEH/v20F7FesgZD+++OKS3aUYhtuYwMuGNfbdJaAM3bt7/8dUrx5cd91J4uJuMd1aw2t5/ze5gNasWcPFi3dQqtRl2rSxu5qi0a9fAHArn376P7tLMQy3MIGXjXnzFgJd6dLFjxLec3JFjoYNCwX8mDo10e5SDMMtTOBlQVWZPfsPoAI9e/rOKPg33QSVKsVz5EhLYmNj7S7HMFzOBF4WNm/eTHx8S0qUSPGIC2y70oABgUBLPvnkW7tLMQyXM4GXhfnzvwLupX37VEqXtruaojV0aCgA06YVzXVCDaMomcDLwowZ+4Cq9O0blOu83qZ2bahS5QRHj5pureF9TOBlcuDAAfbvj8TPL5W77rK7GnsMHBgEtGDChCV2l2IYLmUCL5MFC6yx71q2TKZ8eburscfQoSEATJt22eZKDMO1TOBlMn36VqA2/fuXsrsU29SsCRERf3Ds2K3s3bvX7nIMw2VM4DmJj49n06ZqQBpdfe4yRVcbODAYaMbHHy+1uxTDcBkTeE6sse/uJSrqEtddZ3c19rIOQoYZM67YXIlhuI4JPCfTpv0ERHH//T52LEoWqlWDatWOcexYK9OtNbyGCTyHixcv8v331wBw333eO/ZdfgwcWBKI5qOPltldimG4hAk8h/Sx72rVuoC5Vovlz25tis2VGIZrmMBzmD59JdCS++/33b2zmUVEQI0aR/njj9vYs2eP3eUYRqGZwANSUlL45htrkIAePbx8pM98GjSoFBDF+PHL7S7FMArNBB7W2HeXLnWicuUL1KtndzXFi3VubRqzZqndpRhGoeUp8ESkk4jsEZF9IvJcDvN1FxEVkRjXleh+M2cuAdrQp0+Q116ZrKCqVIEbbjjG8eO3sXu311x90/BRuQaeiPgDHwJ3AvWAviLyl3aQiJQFngB+cXWR7qSqzJ2bBATSu7ePjPSZT4MHlwYa8uGHK+0uxTAKJS8tvGbAPlU9oKqXgZlAVuch/At4A0hyYX1ut2XLFk6dak358gnEeFS7tOgMGWJ1a2fPNt1aw7PlJfCqAIedHsc5nssgIk2ACFXN8dL1IjJcRDaIyIb4+Ph8F+sOs2d/DXSie3c//MwWzSxVqgS1ah3lxInb2bXLdGsNz1Xor7iI+AFvA0/nNq+qTlDVGFWNCQ8PL+yqXWLGjNNASfr1M4ej5OTBB8sA9fjww1V2l2IYBZaXwB4lXsQAAB/ESURBVDsCRDg9rup4Ll1ZoAGwSkQOAc2BhZ6w4+LAgQP89lsTSpVKpFUru6sp3h58MBRIZfZsuysxjILLS+CtB2qLSA0RKQH0ARamT1TVc6oapqrVVbU68DPQRVU3uKViF5o7dxFwD507XyHAd67VUyAVK0KdOkeJj2/Dzp277C7HMAok18BT1RRgBLAE2AXMVtUdIjJKRLq4u0B3+vzzOCCEwYPL2V2KRxgypCxwIx98sNruUgyjQETVnj1vMTExumGDfY3AkydPEh4+nxIlBnLuXBDBwbaV4jHi4+Haa1MID5/IiRPD7S7HMLIkIhtVNctNaj67X3Lhwq+BLtx++0UTdnkUHg433WS6tYbn8tnAmzRpD1CRBx/00QtXFNCQIeWA2rz33hq7SzGMfPPJwLt06RI//VQJf/8rdO5sziXLjwceCEUkhblzffJPx/BwPvlXu2TJd6SmdqFp03OULWt3NZ6lQgW46aYjnDzZlh07dtpdjmHki08G3sSJm4FqjlOmjPwaNiwEqMm4cWvtLsUw8sXnAi8lJYVly8ohkkq3bubgu4IYPDgUkSvMmxdodymGkS8+F3hr164lKelO6tc/RViY3dV4pvLloV69I5w6Zbq1hmfxucCbOPFHoB6DB4fYXYpHGz48FKjGu+/+aHcphpFnPhV4qsqiRVY3tnfvIJur8WyDBoUictl0aw2P4lOBt3XrVs6ebUPNmieoWtXuajxbSAg0aHCE06fbsn37DrvLMYw88anAmzJlJdDUDAXlIg89VB6I4N13f7a7FMPIE58KvNmzLwMwYEAZmyvxDgMGWN3aBQvM0PiGZ/CZwDt48CBHj97MddfFU6eO3dV4h3LlIDIyznRrDY/hM4E3dep3QCtz3VkXe/jha4AqvPWW6dYaxZ8PBd55wJ8hQ66xuxSvcv/9ofj5JbNwYTB2DTVmGHnlE4F38uRJ9u6tT2joaRo1srsa71KmjNWtPXOmLdu2mW6tUbz5RODNnr0EaMfdd182F9p2g7/9rQJQibffXm93KYaRI58IvIkT/wCCeOihinaX4pX69w/Fzy/RdGuNYs/rA+/SpUts3lyDUqXOccstpnnnDqVLQ1TUEc6ebcvWraZbaxRfXh94ixYtIy2tA23bXjAX2najRx4JAyry1lvF/mJ1hg/z+gj4+OMDQBkeeeQ6u0vxav36heLvn8jixSVNt9Yotrw68FJSUvjxx4qUKHGR9u3N2HfuVLIkNG4cx9mz7diy5Ve7yzGMLHl14H3//Y8kJ3ekefOTBJpBPdzu0UfDgTDGjt1kdymGkaU8BZ6IdBKRPSKyT0Sey2L6wyKyXUS2iMhaEann+lLzb/z4X4FrePjha+0uxSf06ROKv/9Fvv66lOnWGsVSroEnIv7Ah8CdQD2gbxaBNl1VG6pqFPAm8LbLK80nVWXp0rL4+yfRtWtJu8vxCcHBEB19hHPn2rF5s+nWGsVPXlp4zYB9qnpAVS8DM4GuzjOo6nmnh6UB2/+9b9myjQsX2tGo0TFKmdGgisyIEdcC15hurVEs5SXwqgCHnR7HOZ67iog8KiL7sVp4j2e1IBEZLiIbRGRDfHx8QerNsw8/XA9UNufOFrFevUIJCEjg66/Lmm6tUey4bKeFqn6oqjcAzwIvZTPPBFWNUdWY8PBwV606S4sWBSByhX79zLUrilJQEMTEHOH8+bZs2mS6tUbxkpfAOwJEOD2u6nguOzOBboUpqrAOHjzEiRO3UqdOHKHm0rNF7rHHKgKhjBmzxe5SDOMqeQm89UBtEakhIiWAPsBC5xlEpLbTw7uAWNeVmH8fffQDUIv77y9tZxk+q0ePUAICLvDNN6ZbaxQvuQaeqqYAI4AlwC5gtqruEJFRItLFMdsIEdkhIluAp4BBbqs4D6yh3NMYNswcjmKHEiWgWbMjXLjQhsWLl9ldjmFkELv+A8fExOiGDa4/7/LUqVOEhcURERHC779Xd/nyjbz55ptE7rqrJCVLjiM2tgdVqvxlP5dhuIWIbFTVmKymed2ZFp999j3QyAzlbrPOnUvSq9cZEhOf4I473uHKlSt2l2QY3hd4n39+AYDHHjMXnrXblCnlqVbtDLt2Pc8jj4y2uxzD8K7Au3TpErt23Uh4+O/UqGHGvrNbcDAsW1aewMBSfPppe2bNmm93SYaP86rAmzVrDWlpzbnrrst2l2I41KoFkycHAC0YODCO2Fhbd+AbPs6rAm/ChOMAPPlkNZsrMZz16xfIoEHnuXz5Me6440MSExPtLsnwUV4TeCkpKWzcWI1y5Y4SGWnGgipuPv64HLVrn+O330YycOBIu8sxfJTXBN7//vcLV660pF278+bKZMVQUBAsWRJCcHAQX37Zk48/nmx3SYYP8prA++CD34AAnngiItd5DXvUqAHTp5cAYnj00US2bDGnnhlFyysCT1VZsyac4OAT3HabOZ2sOLv3Xn/+9rdLpKb+jU6dJnLu3Dm7SzJ8iFcE3i+/7CAx8VZatjxhurMeYNy4UjRocJ7jx/9Nz54vmPNtjSLjFYH3zju7gJI8+mhlu0sx8iAwEL75phylSgWydOkw3nzzfbtLMnyEVwTe0qVlCAg4Q5cuZrBPTxERAXPmBANRPP98SdauXWt3SYYP8PjA27v3N86caUmTJofxN6fPepTOnYWnnkpCdRj33DOLEydO2F2S4eU8PvDefnsLUI6hQyvYXYpRAG+8EUx0dAJnz46ma9fnSE1Ntbskw4t5fOAtXBiAn18CAwea4Yc8UUAALFxYhrJl/fj556d44YX/2F2S4cU8OvBOnDjFsWPNuOmmAwQF2V2NUVCVK8O8eSWBerz55vX873//s7skw0t5dOC9++4GIJwBA8yxd56ufXt44YVUYDA9e37Db7/9ZndJhhfy6MCbPfsKkMSjj9a0uxTDBUaNCuSWWy5x8eKb3H33CyQnJ9tdkuFlPDbwLl68xIEDjahefS9ly5qjjb2Bvz/Mm1eK0FDh119f4fHHX7S7JMPLeGzgffTRelQj6NHDY9+CkYWKFWHBgmBEajNhQjQzZsy0uyTDi3hsWlhDuafw1FN17C7FcLHWreG11xToy+DBP7Fr1y67SzK8hEcGXkpKCjt21KFixd1UqlTC7nIMN3jxRX9uvz2Jy5ffpHPnl0hISLC7JMML5CnwRKSTiOwRkX0i8lwW058SkZ0isk1ElouIW4ccnj59M6mpdcxQ7l7Mz8869Sw8XDl06E0efPDvZpABo9ByDTwR8Qc+BO4E6gF9RaReptk2AzGqGgl8Cbzp6kKdffxxPAD//KfpznqzsDBre56fX3XmzOnIRx99bHdJhofLSwuvGbBPVQ+o6mVgJtDVeQZVXamqlxwPfwbcdo1EVWXDhghCQ3dx441l3LUao5i45RZ44w0BevDYY3txx8XbDd+Rl8CrAhx2ehzneC47Q4AsD5UXkeEiskFENsTHx+e9SidLluzm8uWGtGt3vkCvNzzP00/70anTZVJTR3PPPf/i9OnTdpdkeCiX7rQQkfuBGGBMVtNVdYKqxqhqTHh4eIHWMW7c7wA8/bQ52NhXiFhDw1eqpPzxxzj69HmEtLQ0u8syPFBeAu8I4HyhiKqO564iIu2BF4Euquq2Q+TXrAmnVKl9tGhRsMA0PFP58vDVV0H4+0ewdGk/Ro9+w+6SDA+Ul8BbD9QWkRoiUgLoAyx0nkFEGgMfY4Wd2wY127DhMBcvRnHLLcfdtQqjGGvaFN5+2w/owksvnWblypV2l2R4mFwDT1VTgBHAEmAXMFtVd4jIKBHp4phtDFAGmCMiW0RkYTaLK5S33ooF/Hj8cbftEzGKucceE7p1u4Lq69x331scPXrU7pIMDyJ2HdsUExOj+d3jVqHCOi5cuI7k5OvNxXp82Llz0LBhMnFxJ2nW7GHWrp1PQECA3WUZxYSIbFTVmKymecyZFocOneH06SiaNPnNhJ2PCwmxtucFBFzHL788yvPPm0EGjLzxmMB7441fgRIMG2aGcjegcWN4/31/oBNjxwawYMECu0syPIDHBJ41lPsfDBp0k92lGMXE8OHQu3cqMIr77/+U/fv3212SUcx5ROCdOpXI0aOR1K27l4AAjyjZKAIi8Omn/tSsmcalS5/StetwEhMT7S7LKMY8Ij0CA/14/PGtvPxyRbtLMYqZMmVgwYJAAgPD2bHjBR577Em7SzKKMY8IvHLlghg37hZ6977R7lKMYqhhQ/j4Y3+gHZ99VpkpU6bYXZJRTHlE4BlGbgYPhoED04CXGT58Dtu3b7e7JKMYMoFneI3//tePm25KJSVlMl27PsL582aACeNqJvAMr1GqFMyfH0hQUHkOHvw/HnhgmBk01LiKCTzDq9x0k7XnFloxb14U7733nt0lGcWICTzD6/TrB8OGKfA8Tz21nJ9++snukoxiwgSe4ZXee09o2DAFmMJ99z1JQQecNbyLCTzDKwUHw7x5AZQsWY7jx8fRt+8gUlNT7S7LsJkJPMNr1aoFkyb5o9qc5cvb8a9//cvukgybmcAzvFrPnjBihAJP89pr23jyySdZs2aNae35KBN4htcbO1aIjk4lMPALPvzwCLfd1plKlSoxdOhQFi9eTFJSkt0lGkXEBJ7h9YKCYM4cf8LCSpOSMgd//3PAKr74oir33PMKFSqE06NHD6ZOncqZM2fsLtdwI48a8dgwCuPyZfjxR1iyBL79FrZssZ4vWfI88B2JiQvw919Bmzb16datG127dqVqVXM5AU+T04jHJvAMn/XHH7B0qRWAS5YoJ09aQ2kHBf1KcvJCYAlNmlyme/d76NatG3Xr1kXMcNvFngk8w8hFWhps3pwefvDjj0pKiuDnd5G0tKXAEqpX30vPntF069aN5s2b4+dntgi5y6lTp9i6dStbt27l0UcfpUSJEnl+rQk8w8in8+dhxQor/L7+OoXDh9MvEhQLfEto6Druvbc8PXveSdu2bQkKCrKzXI+VkpJCbGxsRrht2rSTzZvTiI+PAKKBaNauDaRly/p5XqYJPMMoBFWIjbXCb9GiK6xeLSQnBwCXgbWUKLGS229PZtCgxtx1V2dCQkLsLrlYOnPmDNu2bcsIt82bd7FjRwCXLzfACrcYoB4QCEBIyGWaNFE+/TSImjXzvh4TeIbhQsnJsHat1fKbP/8Shw6Vc0w5hsgyGjY8woAB19GvXwcqV65sa612SE1NZd++fVeF25Ytu4mLK48VatH4+99MWtpNqFot5/LlU4iJ8aNpUz9iYiA6GiIiKNAVCgsdeCLSCRgH+AOfquroTNNvA94FIoE+qvplbss0gWd4i6NH4dtv05g+/TQ//FCSpKTSQBqwkcqVt9OlSwlGjGhK/freN2L3uXPnMoIt/ef27bEkJtYCYhCJISjoFpKTa2WE2zXXpNK0qR/R0VLocMtKoQJPRPyBvcAdQBywHuirqjud5qkOlAP+ASw0gWf4qtRU2LBBmTo1noULk/n998pY7YRzlCmzjltvvcBDD9WgS5dGHrXTIy0tjQMHDmS02NLD7dChY1jtnGhKlGhJiRLNuXSpBmlp/gCEhSnR0UJ0tBVsMTGuDbesFDbwWgAjVbWj4/HzAKr6ehbzTgYWe33gnTwJb74JR45AVJR1kdTGjaGCuWaucbWzZ2HWrJNMmXKcTZvCSU6+FoCAgH3UrXuYyEihdOlUSpdOo0wZpUyZNMqWhbJloVw5KFnSn4CAAAIDAwv0syCheuHCBbZv354RblarbTsXL6YAkYg0pVy5NojEcP58RLbhFh0N11/v3nDLSk6BF5DVk5lUAQ47PY4Dbi5gIcOB4QDXX399QRZhr6QkeP99+M9/4MIFqFwZpk//c3pExJ/hl35z978zo1gLDYWHHgrjoYfCUIVffjnH++/Hsny5P9u3N2f79pK5LCEZOA9ccNyyup/T9AQCA5MICLhEYGAagYE5h+SZM2c4cOAAEAREUrJkK0JCXqJkyUYkJVUmNdUPVQgM5Kpgs8JNiv2fel4Cz2VUdQIwAawWXlGuu1BUYdYseP55OHQIOne2Wnj161utvS1brIO40m+LFlmvAbjmmr+GYJ064O9v61syip4ING8eQvPmVuPj3LlkYmNPcPZsKufOKefOpXH+vHLunJKQAOfPCwkJ6beSXLpUmosXK3PpUgCXLvmTmBhAYmIgycmBOa73yhXrlpycSmBgoiMAkwgMvIS/fyL+/pfw97+In99FRMpSqVI9Tpy4ltRUPxITraHz07e12dlyc4W8BN4RIMLpcVXHc77hhx/gqadg3TqIjLQOzW/f/s/pYWHWY+fnLl6EbduuDsH33rPObQLrLygy8uoQbNDAGsTN8BkhIUHExFxb6OWkplp/cufPWx2P9J9/ve/PhQtluHChTJbznDljnXfcpMnVAeep4ZaVvATeeqC2iNTACro+QD+3VlUc7NsHzz0Hc+daXdeJE2HgwLy1zEqXhhYtrFu6K1dg166rQ3DaNPjvf63pAQFQt+7VIRgVBeaYLiMX/v7W9r5y5XKf19fl9bCUzliHnfgDE1X1PyIyCtigqgtFpCkwHygPJAF/qGqOh0YX250Wp0/Dv/4FH34IJUrAP/8JTz9thZirpaXBwYNXh+DmzdZJnulq1vxrl7hSJdfXYhhewhx4nBfJyVbI/etfVhv/wQdh1Ch7wuWPP/4agvv3/zm9YsW/hmDNmuBBhzkYhrsUdi+td1OFL7+0uq8HDkDHjjBmDDRsaF9N110Hd95p3dKdOwdbt14dgsuWQUqKNb1sWWjVCkaOhKZNbSnbMIo73w68n36yuqs//WQF3LffWoFXHIWEwG23Wbd0SUmwY8efAThnDjRrBr17w//9H/k6AdEwfIBv9oEOHLBC4ZZbrG1on3xiBUZxDbvsBAdbu9GGDrW64/v2wUsvwcKF1hWp//53OHXK7ioNo9jwrcA7cwb+8Q9rb+iiRfDKK9YwGEOHesdxceXKWdsg9+2DQYOsQ2FuuAHeeAMSE+2uzjBs5xuBd/kyjBtnXbfv7behf38r6F57DcqUsbs616tc2Wq1bt0Kt95qbZ+88Ub4/HNrz7Bh+CjvDjxVmDfPOiPiySetvZmbNlnH1FWpYnd17tegASxebI1kee21VquvSRPr4GnD8EHeG3jr1kHr1tC9u3U83ddfW1/0qCi7Kyt6bdpYn8f06dbe3g4doFMnqwVoGD7E+wLv0CHo1w9uvhn27IGPPrK+2J07e8/5MQXh5wd9+8Lu3fDWW1YANm4MgwfD4cO5vtwwvIH3BN65c/Dss9beyfnz4cUXrY33Dz1knbZlWIKCrHOD9++3duDMnGkNZvD889ZnaBhezPMD78oV+OADa4fEm29ah5vExsK//20djGtkrXx56/Paswd69IDRo609us6DHBiGl/HcwFOFr76yNsw/9pj1c+NGmDIFzMWT865aNfjiC+uzi4qCJ56wDtuZPfvPIa4Mw0t4ZuBt3GhtiO/Wzdout3ChtSeySRO7K/Nc6Xtv//c/a6CE3r2heXNYs8buygzDZTwr8A4fhgEDrMG6duywzi7Yvh3uuce3d0i4ioi193bzZuvQnSNHrFPZuna1dnYYhofzjMC7eBFeeMHauD5njnUg7b598Mgj1ljThmv5+8MDD8DevdY5uStXWpsMHn746qGrDMPDeEbggXWWQPfu1kb21183A2MWhVKlrL23+/db/1w++8zaOfTaa5CQYHd1hpFvnhF4pUtbXdipU62N7EbRCg+39t7u2mUNWTVypBV8H3/85/BUhuEBPCPwwLToioNataxNCj/9ZN1/+GFrWK2FC80eXcMjeE7gGcVH+t7b+fOtwQi6doXbb7fO3jCMYswEnlEwItZhQb/+al2IaPdu63S+3r2vHo7eMIoRE3hG4QQGWl3bffus8QUXL7YOXH7ySeuavYZRjJiL+BiudewYvPqqtUc3KMja1hcRYZ39EhFx9a1qVShZ0u6KDS9jrlpmFL2dO2HCBGsI/cOHIS4O4uP/Ol9Y2F+D0PlWpYo51tLIl0JftUxEOgHjsK5L+6mqjs40PQj4HIgGTgG9VfVQYYo2PFy9evDuu1c/l5RkBd/hw3+9HTwIq1fD2bNXv0bEuopbdq3EiAjrUpreMES/4Xa5Bp6I+AMfAncAccB6EVmoqjudZhsCnFHVWiLSB3gD6O2Ogg0PFhxsdXFr1cp+noSErAPx8GGr1bhkiXXmjTN/f2tY+5xaiuHhOV+3NyXFujZxcrI1WkxB7+f3NcVtyH0Razi1wMCsf+Y0zV2vqVHDGsTXBfLSwmsG7FPVA9bnITOBroBz4HUFRjrufwl8ICKidvWXDc9Vpoy106Nu3aynq1qtwPRucuZQ3LgRFiywwsRZiRJW99jPL+vwcWXwiFjbL4OCrPVmdT8oyDqgvri1TNPSrPBPSbEu/JSSYg3B5vwzq+fSf6amur6mvXuhdm2XLCovgVcFcB4SNw64Obt5VDVFRM4BFQCzm85wLRFrLL/y5SEyMut5VK09xJnD8MgR6wvtHDo5BVJ203KbLyDAdwezSEuzQi8/IZndz/T7113nsvKKdChgERkODAe4/vrri3LVhi8Rsbqw4eFmyLCi5udn3Yrpjqa8HId3BIhwelzV8VyW84hIABCCtfPiKqo6QVVjVDUmPDy8YBUbhmEUUF4Cbz1QW0RqiEgJoA+wMNM8C4FBjvs9gBVm+51hGMVNrl1axza5EcASrMNSJqrqDhEZBWxQ1YXAZ8AXIrIPOI0VioZhGMVKnrbhqeo3wDeZnnvF6X4S0NO1pRmGYbiWOZfWMAyfYQLPMAyfYQLPMAyfYQLPMAyfYdtoKSISD/yWz5eFUbzO3jD15Ky41QPFryZTT84KUk81Vc3yQF/bAq8gRGRDdsO+2MHUk7PiVg8Uv5pMPTlzdT2mS2sYhs8wgWcYhs/wtMCbYHcBmZh6clbc6oHiV5OpJ2curcejtuEZhmEUhqe18AzDMArMBJ5hGD6jWASeiHQSkT0isk9EnstiepCIzHJM/0VEqjuery4iiSKyxXH7qAhruk1ENolIioj0yDRtkIjEOm6DMr/WhnpSnT6jzEN7uauep0Rkp4hsE5HlIlLNaZodn09O9bj888ljTQ+LyHbHeteKSD2nac87XrdHRDraWY+7vme51eM0X3cRURGJcXquYJ+Pqtp6wxpyaj9QEygBbAXqZZrnEeAjx/0+wCzH/erArzbVVB2IxLpaWw+n568BDjh+lnfcL29XPY5pCTZ8Pm2AUo77f3P6ndn1+WRZjzs+n3zUVM7pfhfgW8f9eo75g4AajuX421iPy79neanHMV9ZYDXwMxBT2M+nOLTwMi4SpKqXgfSLBDnrCkxx3P8SaCfi1osG5FqTqh5S1W1A5qu/dASWquppVT0DLAU62ViPO+SlnpWqesnx8GeskbLBvs8nu3rcJS81nXd6WBpI34PYFZipqsmqehDY51ieXfW4Q16+9wD/wroKYpLTcwX+fIpD4GV1kaAq2c2jqilA+kWCAGqIyGYR+V5EWhVhTe54rbuWGSwiG0TkZxHpVshaClLPEOB/BXytu+sB138+ea5JRB4Vkf3Am8Dj+XltEdYDrv+e5VqPiDQBIlT16/y+NjtFehEfNzgGXK+qp0QkGlggIvUz/acyrHMLj4hITWCFiGxX1f1FsWIRuR+IAVoXxfpyk009tn0+qvoh8KGI9ANe4s9LJdgim3qK/HsmIn7A28BgVy63OLTwCnyRIEeT9hSAqm7E6svXKaKa3PFatyxTVY84fh4AVgGNi6IeEWkPvAh0UdXk/Ly2COtxx+eT55qczATSW5fF4W8oox43fc9yq6cs0ABYJSKHgObAQseOi4J/Pq7cEFnAjZcBWBuua/Dnxsv6meZ5lKt3Wsx23A/HsbESa+PnEeCaoqjJad7J/HWnxUGsDfLlHfcLVVMh6ykPBDnuhwGxZLFx2A2/s8ZYX4zamZ635fPJoR6Xfz75qKm20/17sK4RA1CfqzfKH6DwOy0KU4/Lv2f5+Zt2zL+KP3daFPjzKdQv1VU3oDOw1/EH+aLjuVFY/4kBgoE5WBsn1wE1Hc93B3YAW4BNwD1FWFNTrG0HF7EuSbnD6bUPOmrdBzxgZz3ALcB2xx/IdmBIEdWzDDju+N1sARba/PlkWY+7Pp881jTO6e93pfMXHqsluh/YA9xpZz3u+p7lVk+meVfhCLzCfD7m1DLDMHxGcdiGZxiGUSRM4BmG4TNM4BmG4TNM4BmG4TNM4BmG4TNM4BnFnoh0SR9NQ0RGisg/7K7J8EyefmqZ4QNUdSHgsmGbDN9lWniG24nI/SKyzjGW2sci4i8iCSLyjojscIxPF+6Y93GncetmOp4bLCIfZLHcKMcJ/9tEZL6IlHc8v0pE3nCsc68LB5UwPJwJPMOtRKQu0BtoqapRQCrQH2v4oQ2qWh/4HnjV8ZLngMaqGgk8nMviPweedcy73WkZAAGq2gx4MtPzhg8zXVrD3doB0cB6xxCGJYETWOP2zXLMMxWY57i/DZgmIguABdktVERCgFBV/d7x1BSs0w/TpS9vI9YAloZhWniG2wkwRVWjHLcbVXVkFvOln+N4F/Ah0AQrJAv6Tzl9NJRUzD92w8EEnuFuy4EeInItgIhc47iehB+Qfu2NfsBaxxhoEaq6EngWaxiwMlktVFXPAWects8NwOoaG0a2zH8+w61UdaeIvAR85wi0K1jDfV0EmjmmncDazucPTHV0VwV4T1XP5jCa/yDgIxEphTVE0APufTeGpzOjpRi2EJEEVc2y9WYY7mK6tIZh+AzTwjMMw2eYFp5hGD7DBJ5hGD7DBJ5hGD7DBJ5hGD7DBJ5hGD7j/wH29GVe095IDgAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# test detection for the MC-dropout net\n", "epsilon_list = np.arange(0.05, 0.41, 0.05)\n", "tpr_dropout = run_detect(dropout_bnn, x_clean, y_clean, predict_mcdropout, epsilon_list)" ] }, { "cell_type": "markdown", "metadata": { "id": "JWjFNv08mjp3" }, "source": [ "Then we evaluate the MFVI Bayesian CNN." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 558 }, "id": "bSNKODDHl09g", "outputId": "8b2e3058-98a6-4ec9-d3e9-72decaea39a7" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "adv accruacy for this batch (%): [91. 77. 39. 12. 0. 0. 0. 0.]\n", "adv accruacy for this batch (%): [95. 76. 48. 12. 3. 0. 0. 0.]\n", "adv accruacy for this batch (%): [93. 71. 37. 13. 2. 0. 0. 0.]\n", "adv accruacy for this batch (%): [94. 66.99999 38. 17. 0. 0. 0. 0. ]\n", "adv accruacy for this batch (%): [87. 66. 31. 4. 2. 0. 0. 0.]\n", "adv accruacy for this batch (%): [91.99999 72.99999 33. 9. 0. 0. 0. 0. ]\n", "adv accruacy for this batch (%): [93. 66. 37. 13. 1. 0. 0. 0.]\n", "adv accruacy for this batch (%): [95. 74. 42.999996 14. 0. 0. 0.\n", " 0. ]\n", "adv accruacy for this batch (%): [91.99999 76. 37. 11. 1. 0. 0. 0. ]\n", "adv accruacy for this batch (%): [91.99999 72.99999 37. 13. 0. 0. 0. 0. ]\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# test detection for the mfvi net\n", "tpr_mfvi = run_detect(mfvi_bnn, x_clean, y_clean, predict_mfvi, epsilon_list)" ] }, { "cell_type": "markdown", "metadata": { "id": "ZXgUsw6PmmkP" }, "source": [ "Finally we evaluate the ensemble MFVI Bayesian CNN." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 610 }, "id": "OnQzSgaamvML", "outputId": "b0b4aa0a-06aa-47cf-d9a1-9511b6ce57b3" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:780: UserWarning: Note that order of the arguments: ceil_mode and return_indices will changeto match the args list in nn.MaxPool2d in a future release.\n", " warnings.warn(\"Note that order of the arguments: ceil_mode and return_indices will change\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "adv accruacy for this batch (%): [91.99999 76. 44. 4.9999995 0. 0.\n", " 0. 0. ]\n", "adv accruacy for this batch (%): [97. 79.99999 45.999996 7. 0. 0. 0.\n", " 0. ]\n", "adv accruacy for this batch (%): [96. 78. 39.999996 11. 0. 0. 0.\n", " 0. ]\n", "adv accruacy for this batch (%): [95. 78. 39. 6. 0. 0. 0. 0.]\n", "adv accruacy for this batch (%): [91. 70. 29. 6. 0. 0. 0. 0.]\n", "adv accruacy for this batch (%): [96. 81. 28. 4. 0. 0. 0. 0.]\n", "adv accruacy for this batch (%): [93. 79. 42. 8. 0. 0. 0. 0.]\n", "adv accruacy for this batch (%): [93. 79.99999 48.999996 4.9999995 1. 0.\n", " 0. 0. ]\n", "adv accruacy for this batch (%): [94. 76. 38. 8. 0. 0. 0. 0.]\n", "adv accruacy for this batch (%): [95. 72.99999 38. 8. 0. 0. 0. 0. ]\n" ] }, { "data": { "image/png": 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================================================ { "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Kopia notatnika ProbAI 2022 BNN Tutorial - regression.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "source": [ "# A Hands-on Tutorial for Bayesian Neural Networks\n", "\n", "(Part 1: regression)\n", "\n", "[Yingzhen Li](http://yingzhenli.net/home/en/)\n", "\n", "(As part of the BNN lecture at [ProbAI 2022](https://probabilistic.ai/))\n", "\n", "In this tutorial, you will implement various Bayesian neural network methods based on variational inference.\n", "\n", "We will go through regression tasks to see the applications of uncertainty estimation in practice, including a case study on Bayesian optimisation.\n", "\n", "**How to use this tutorial notebook?**\n", "\n", "* Read the descriptions in the text;\n", "* Fill in the missing code whenever you see a block that looks like below:\n", "\n", "```\n", "### beginning of your code ###\n", "[insert your own code here]\n", "### end of your code ###\n", "```\n", "There will be hints provided in the code blocks as well to guide you through.\n", "\n", "Let us set up the required packages, and then, enjoy 😊" ], "metadata": { "id": "Z97rDy1VjK4I" } }, { "cell_type": "code", "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import math\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.distributions as dist\n", "from torch.utils.data import Dataset, DataLoader\n", "\n", "EPS = 1e-5 # define a small constant for numerical stability control" ], "metadata": { "id": "1Zt00SKnNgyR" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# What is a Bayesian neural network?\n", "\n", "In short, a Bayesian neural network (BNN) is a neural network that use (approximate) Bayesian inference for uncertainty estimation. For example, we can treat the NN parameters as random variables and infer them using (approximate) Bayesian posterior inference.\n", "\n", "For the mathematical foundation, see [lecture notes here](http://yingzhenli.net/home/pdf/ProbAI2022_lecture_note.pdf)." ], "metadata": { "id": "uJqCF8BXD7dO" } }, { "cell_type": "markdown", "source": [ "# Implementing the BNN with mean-field variational inference (MFVI)\n", "\n", "In this part, you will implement the Bayes-by-backprop algorithm by [Blundell et al. (2015)](https://arxiv.org/abs/1505.05424), which is doing mean-field variational inference (MFVI) in weight space for Bayesian neural networks.\n", "\n", "For the mathematical foundation, see [lecture notes here](http://yingzhenli.net/home/pdf/ProbAI2022_lecture_note.pdf).\n" ], "metadata": { "id": "-eYv-w6TNmW4" } }, { "cell_type": "markdown", "source": [ "## Implementing the ```MFVILinear``` class and construct a BNN\n", "\n", "We will implement the MFVI approach in a modular way, so we need a \"BNN layer\" module ```MFVILinear``` to start with. This will allow us to stack-up many network layers efficiently. Then we will implement the training & testing functions, and test the model's performance with a regression example.\n", "\n", "In below block you will implment a BNN layer with mean-field variational inference (MFVI), and more specifically:\n", "\n", "* the initialisation of the variational parameters\n", "* the forward pass with Monte Carlo\n", "* the $KL[q||p]$ regularisation term for one layer, i.e., $KL[q(W) || p(W)] + KL[q(b) || p(b)]$" ], "metadata": { "id": "kOQ75uGIkpW1" } }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dpydUZNVjCRV" }, "outputs": [], "source": [ "class MFVILinear(nn.Module):\n", " \"\"\"Applies a linear transformation to the incoming data: y = xW^T + b, where \n", " the weight W and bias b are sampled from the q distribution.\n", " \"\"\"\n", "\n", " def __init__(self, dim_in, dim_out, prior_weight_std=1.0, prior_bias_std=1.0, init_std=0.05,\n", " sqrt_width_scaling=False, device=None, dtype=None):\n", " factory_kwargs = {'device': device, 'dtype': dtype}\n", " super(MFVILinear, self).__init__()\n", " self.dim_in = dim_in # dimension of network layer input\n", " self.dim_out = dim_out # dimension of network layer output\n", "\n", " # define the trainable variational parameters for q distribtuion\n", " # first define and initialise the mean parameters\n", " self.weight_mean = nn.Parameter(torch.empty((dim_out, dim_in), **factory_kwargs))\n", " self.bias_mean = nn.Parameter(torch.empty(dim_out, **factory_kwargs))\n", " self._weight_std_param = nn.Parameter(torch.empty((dim_out, dim_in), **factory_kwargs))\n", " self._bias_std_param = nn.Parameter(torch.empty(dim_out, **factory_kwargs))\n", " self.reset_parameters(init_std)\n", "\n", " # define the prior parameters (for prior p, assume the mean is 0)\n", " prior_mean = 0.0\n", " if sqrt_width_scaling: # prior variance scales as 1/dim_in\n", " prior_weight_std /= self.dim_in ** 0.5\n", " # prior parameters are registered as constants\n", " self.register_buffer('prior_weight_mean', torch.full_like(self.weight_mean, prior_mean))\n", " self.register_buffer('prior_weight_std', torch.full_like(self._weight_std_param, prior_weight_std))\n", " self.register_buffer('prior_bias_mean', torch.full_like(self.bias_mean, prior_mean))\n", " self.register_buffer('prior_bias_std', torch.full_like(self._bias_std_param, prior_bias_std)) \n", "\n", " def extra_repr(self):\n", " s = \"dim_in={}, dim_in={}, bias=True\".format(self.dim_in, self.dim_out)\n", " weight_std = self.prior_weight_std.data.flatten()[0]\n", " if torch.allclose(weight_std, self.prior_weight_std):\n", " s += f\", weight prior std={weight_std.item():.2f}\"\n", " bias_std = self.prior_bias_std.flatten()[0]\n", " if torch.allclose(bias_std, self.prior_bias_std):\n", " s += f\", bias prior std={bias_std.item():.2f}\"\n", " return s\n", "\n", " def reset_parameters(self, init_std=0.05):\n", " nn.init.kaiming_uniform_(self.weight_mean, a=math.sqrt(5))\n", " bound = self.dim_in ** -0.5\n", " nn.init.uniform_(self.bias_mean, -bound, bound)\n", " ### begin of your code ###\n", " # hints: Notice that standard deviation > 0 always so we cannot directly treat \n", " # the standard deviation as a free parameter. Instead we parameterise a free-form\n", " # parameter self._weight_std_param and then transform it to the standard deviation \n", " # in the function \"weight_std(self)\" below. Note that we want to initialise these \n", " # parameters so that standard deviation of q(W) (and q(b)) is initialised as init_std.\n", "\n", " self._weight_std_param.data = \n", " self._bias_std_param.data = \n", "\n", " ### end of your code ###\n", "\n", " # define the q distribution standard deviations with property decorator\n", " @property\n", " def weight_std(self):\n", " ### begin of your code ###\n", " # hints: depending on how you define self._weight_std_param above, the way \n", " # you convert self._weight_std_param to weight_std (standard deviation of q(W))\n", " # will be different.\n", "\n", " weight_std = \n", "\n", " ### end of your code ###\n", " return weight_std\n", "\n", " @property\n", " def bias_std(self):\n", " ### begin of your code ###\n", "\n", " bias_std = \n", "\n", " ### end of your code ###\n", " return bias_std\n", "\n", " # KL divergence KL[q||p] between two Gaussians\n", " def kl_divergence(self):\n", " q_weight = dist.Normal(self.weight_mean, self.weight_std)\n", " p_weight = dist.Normal(self.prior_weight_mean, self.prior_weight_std)\n", " kl = dist.kl_divergence(q_weight, p_weight).sum()\n", " q_bias = dist.Normal(self.bias_mean, self.bias_std)\n", " p_bias = dist.Normal(self.prior_bias_mean, self.prior_bias_std)\n", " kl += dist.kl_divergence(q_bias, p_bias).sum()\n", " return kl\n", "\n", " # forward pass with Monte Carlo (MC) sampling\n", " def forward(self, input):\n", " weight = self._normal_sample(self.weight_mean, self.weight_std)\n", " bias = self._normal_sample(self.bias_mean, self.bias_std)\n", " return F.linear(input, weight, bias)\n", "\n", " def _normal_sample(self, mean, std):\n", " ### begin of your code ###\n", " # please implement the sampling process for a factorised Gaussian\n", "\n", " ### end of your code ###\n", "\n", "# construct a BNN\n", "def make_mfvi_bnn(layer_sizes, activation='LeakyReLU', **layer_kwargs):\n", " nonlinearity = getattr(nn, activation)() if isinstance(activation, str) else activation\n", " net = nn.Sequential()\n", " for i, (dim_in, dim_out) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):\n", " net.add_module(f'MFVILinear{i}', MFVILinear(dim_in, dim_out, **layer_kwargs))\n", " if i < len(layer_sizes) - 2:\n", " net.add_module(f'Nonlinarity{i}', nonlinearity)\n", " return net" ] }, { "cell_type": "markdown", "source": [ "We have just defined the ```MFVILinear``` class and ```make_mfvi_bnn``` for network construction.\n", "\n", "In below we define the training procedure of a BNN, mainly by defining the training objective. This also involves helper functions that collects the KL regularizers accross layers as well as a prediction function.\n", "\n", "In particular, as we work with regression data, we will use Gaussian likelihood, i.e., $p(y | x, \\theta) = N(y; f_{\\theta}(x), \\sigma^2)$ where $f_{\\theta}$ is the neural network and $\\sigma^2$ is the output variance parameter that we will also optimise." ], "metadata": { "id": "2Pg74n4fjKEV" } }, { "cell_type": "code", "source": [ "# collect the kl divergence for all MFVILinear layers\n", "def kl_divergence(bnn):\n", " kl = 0.0\n", " for module in bnn:\n", " if hasattr(module, 'kl_divergence'):\n", " kl = kl + module.kl_divergence()\n", " return kl\n", "\n", "# define the training function which minimises the negative ELBO\n", "# note that the (tempered) negative ELBO = negative log-likliehood of dataset + beta * KL\n", "# where the neg. log-likelihood *per datapoint* is computed with a function\n", "# nll = data_loss_func(y, y_pred)\n", "# this data_loss_func is to be defined later, depending on the learning task\n", "def train_step(net, opt, data_loss_func, dataloader, N_data, beta=1.0):\n", " for _, (x, y) in enumerate(dataloader):\n", " x = x.to(device); y = y.to(device)\n", " opt.zero_grad() # opt is the optimiser\n", " y_pred = net(x)\n", " ### begin of your code ###\n", " # notice we might use mini-batches, so be careful for the right data-count!\n", " \n", " nll = data_loss_func(y, y_pred).mean()\n", " kl = kl_divergence(net)\n", " loss = \n", "\n", " ### end of your code ###\n", "\n", " loss.backward()\n", " opt.step()\n", " return nll, kl\n", "\n", "# now define the data_loss_func that will be used later\n", "# in detail, we will define a Gaussian log-likelihood function below, and then define\n", "# data_loss_func = lambda y, y_pred: -gauss_loglik(y, y_pred, log_noise_var)\n", "# where log_noise_var is the output noise (log) variance which is a seperate model parameter\n", "# define gaussian log-likelihood\n", "def gauss_loglik(y, y_pred, log_noise_var):\n", " # y should have shape as (batch_size, y_dim)\n", " # y_pred should have shape as (batch_size, y_dim) or (K, batch_size, y_dim)\n", " # where K is the number of MC samples\n", " # this function should return per-data loss of shape (batch_size,) or (K, batch_size)\n", " ### begin of your code ###\n", " # hint: consult with your textbook or wikipedia for the Gaussian distribution form\n", "\n", " ll = \n", "\n", " ### end of your code ###\n", " return ll\n", "\n", "# define the prediction function with Monte Carlo sampling using K samples\n", "def predict(bnn, x_test, K=1):\n", " y_pred = []\n", " for _ in range(K):\n", " y_pred.append(bnn(x_test))\n", " # shape (K, batch_size, y_dim) or (batch_size, y_dim) if K = 1\n", " return torch.stack(y_pred, dim=0).squeeze(0)\n", "\n", "# define the error metrics: RMSE and test negative log-likelihood\n", "# in below functions y_pred should have shape (K, batch_size, y_dim) or (batch_size, y_dim)\n", "def rmse(y, y_pred):\n", " if len(y_pred.shape) > 2: # using K > 1 MC samples\n", " y_pred = y_pred.mean(0)\n", " return (y - y_pred).pow(2).sum(-1).mean().sqrt()\n", "\n", "def test_nll(y, y_pred, data_loss_func):\n", " nll = data_loss_func(y, y_pred) # with shape (batch_size) or (K, batch_size)\n", " if len(nll) == 2: # using K > 1 MC samples\n", " nll = -torch.logsumexp(-nll, dim=0) + math.log(nll.shape[0]) # Bayesian predictive average\n", " return nll.mean()" ], "metadata": { "id": "CgTaXt3NnKSi" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Testing: a simple regression task\n", "We will run a simple 1-D regression task to test the implemented BNN, in terms of its predictive performance and uncertainty estimation.\n", "\n", "Let's get some synthetic data:\n", "\n" ], "metadata": { "id": "StOG-wgEnv9t" } }, { "cell_type": "code", "source": [ "def ground_truth_func(x):\n", " return np.sin(x * math.pi / 2 + 0.8) * np.exp(-0.1 * np.abs(x)) + 0.1 * x \n", "\n", "def gen_data(N_data, ground_truth_func, noise_std=None):\n", " # generate the training dataset, note here we will make data into 2 clusters\n", " x1 = np.random.randn(int(N_data/2), 1) * 0.5 + 2.0\n", " x2 = np.random.randn(int(N_data/2), 1) * 0.5 - 2.0\n", " x = np.concatenate([x1, x2], axis=0)\n", " y = ground_truth_func(x)\n", " if noise_std is not None and noise_std > EPS: \n", " # assume homogeneous noise setting, i.e., \"homoscedasticity\"\n", " y += np.random.randn(y.shape[0], y.shape[1]) * noise_std\n", " return x, y\n", "\n", "def normalise_data(x, mean, std):\n", " return (x - mean) / std\n", "\n", "def unnormalise_data(x, mean, std):\n", " return x * std + mean\n", "\n", "class regression_data(Dataset):\n", " def __init__(self, x, y, normalise=True):\n", " super(regression_data, self).__init__()\n", " self.update_data(x, y, normalise)\n", " \n", " def __len__(self):\n", " return self.x.shape[0]\n", " \n", " def __getitem__(self, index):\n", " x = torch.tensor(self.x[index]).float()\n", " y = torch.tensor(self.y[index]).float()\n", " return x, y\n", "\n", " def update_data(self, x, y, normalise=True, update_stats=True):\n", " assert x.shape[0] == y.shape[0]\n", " self.x = x\n", " self.y = y\n", " # normalise data\n", " self.normalise = normalise\n", " if update_stats:\n", " self.x_mean = self.x.mean(0) if normalise else 0.0\n", " self.x_std = self.x.std(0) if normalise else 1.0\n", " self.y_mean = self.y.mean(0) if normalise else 0.0\n", " self.y_std = self.y.std(0) if normalise else 1.0\n", " if self.normalise:\n", " self.x = normalise_data(self.x, self.x_mean, self.x_std)\n", " self.y = normalise_data(self.y, self.y_mean, self.y_std)\n", " \n", "N_data = 100\n", "noise_std = 0.1\n", "x_train, y_train = gen_data(N_data, ground_truth_func, noise_std)\n", "dataset = regression_data(x_train, y_train)\n", "dataloader = DataLoader(dataset, batch_size=100, shuffle=True)\n", "\n", "# plot the training data and ground truth\n", "x_test = np.arange(np.min(x_train) - 1.0, np.max(x_train)+1.0, 0.01)[:, np.newaxis]\n", "y_test = ground_truth_func(x_test)\n", "plt.plot(x_train, y_train, 'ro', label='data')\n", "plt.plot(x_test, y_test, 'k-', label='ground-truth')\n", "plt.legend()\n", "plt.title('ground-truth function')\n", "plt.show()" ], "metadata": { "id": "QDjtmJQ8Lglu" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "We set-up the hyper-parameters and build a BNN to be trained.\n", "\n" ], "metadata": { "id": "2PqirrxvLXOs" } }, { "cell_type": "code", "source": [ "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n", "x_dim, y_dim = x_train.shape[1], y_train.shape[1]\n", "# build a BNN, with hidden layer width = h_dim\n", "h_dim = 50\n", "layer_sizes = [x_dim, h_dim, h_dim, y_dim]\n", "# you can change the activation function here or define your own customized activation\n", "activation=nn.GELU()\n", "# you can change the prior parameters as you wish\n", "layer_kwargs = {'prior_weight_std': 1.0,\n", " 'prior_bias_std': 1.0,\n", " 'sqrt_width_scaling': False,\n", " 'init_std': 0.05,\n", " 'device': device}\n", "mfvi_regression_net = make_mfvi_bnn(layer_sizes, activation=activation, **layer_kwargs)\n", "# we assume a Gaussian likelihood with homogeneuous noise\n", "log_noise_var = nn.Parameter(torch.ones(size=(), device=device)*-3.0)\n", "# print out the BNN settings\n", "print(\"BNN architecture: \\n\", mfvi_regression_net)\n", "\n", "# plot the BNN prior in function space\n", "K = 50 # number of Monte Carlos samples used in test time\n", "x_test_norm = normalise_data(x_test, dataset.x_mean, dataset.x_std)\n", "x_test_norm = torch.tensor(x_test_norm, ).float().to(device)\n", "\n", "def to_numpy(x):\n", " return x.detach().cpu().numpy() # convert a torch tensor to a numpy array\n", "\n", "def get_regression_results(net, x, K, log_noise_var=None):\n", " y_pred = predict(net, x, K=K) # shape (K, N_test, y_dim)\n", " y_pred_mean = y_pred.mean(0)\n", " if log_noise_var is not None:\n", " # note here the preditive std needs to count for output noise variance\n", " y_pred_std = (y_pred.var(0) + torch.exp(log_noise_var)).sqrt()\n", " else:\n", " y_pred_std = y_pred.std(0)\n", " # unnormalise\n", " y_pred_mean = unnormalise_data(to_numpy(y_pred_mean), dataset.y_mean, dataset.y_std)\n", " y_pred_std = unnormalise_data(to_numpy(y_pred_std), 0.0, dataset.y_std)\n", " return y_pred_mean, y_pred_std\n", "\n", "# plot the BNN prior and ground truth\n", "def plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std, title=''):\n", " plt.plot(x_train, y_train, 'ro', label='data')\n", " plt.plot(x_test, y_test, 'k-', label='ground-truth')\n", " plt.plot(x_test, y_pred_mean, 'b-', label='prediction mean')\n", " # plot the uncertainty as +- 2 * std\n", " # first for the total uncertainty (model/epistemic + data/aleatoric)\n", " plt.fill_between(x_test[:,0], y_pred_mean[:,0]-2*y_pred_std[:,0], \n", " y_pred_mean[:,0]+2*y_pred_std[:,0], \n", " color='c', alpha=0.3, label='total uncertainty')\n", " # then for the model/epistemic uncertainty only\n", " plt.fill_between(x_test[:,0], y_pred_mean[:,0]-2*y_pred_std_noiseless[:,0], \n", " y_pred_mean[:,0]+2*y_pred_std_noiseless[:,0], \n", " color='b', alpha=0.3, label='model uncertainty')\n", " plt.legend()\n", " plt.title(title)\n", " plt.show()\n", "\n", "y_pred_mean, y_pred_std_noiseless = get_regression_results(mfvi_regression_net, x_test_norm, K)\n", "model_noise_std = unnormalise_data(to_numpy(torch.exp(0.5*log_noise_var)), 0.0, dataset.y_std)\n", "y_pred_std = np.sqrt(y_pred_std_noiseless ** 2 + model_noise_std**2)\n", "plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std,\n", " title='BNN init (before training, MFVI)')\n", "print(model_noise_std, noise_std, y_pred_std_noiseless.mean())" ], "metadata": { "id": "SIIq7s3KVBbX" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now define the data loss (i.e., negative log-likelihood) and train the BNN." ], "metadata": { "id": "_iKDaUqiXuRi" } }, { "cell_type": "code", "source": [ "# define the training function\n", "def train_network(net, opt, dataloader, data_loss_func, learning_rate=1e-3, \n", " N_epochs=2000, beta=1.0, verbose=True):\n", " net.train()\n", " logs = []\n", " for i in range(N_epochs):\n", " nll, kl = train_step(net, opt, data_loss_func, dataloader, \n", " N_data=len(dataloader.dataset), beta=beta)\n", " logs.append([to_numpy(nll), to_numpy(kl)])\n", " if (i+1) % 100 == 0 and verbose:\n", " print(\"Epoch {}, nll={}, kl={}\".format(i+1, logs[-1][0], logs[-1][1]))\n", " return np.array(logs)\n", "\n", "# start training\n", "learning_rate = 1e-3\n", "params = list(mfvi_regression_net.parameters()) + [log_noise_var]\n", "opt = torch.optim.Adam(params, lr=learning_rate)\n", "# define the regression loss: negative gaussian log-likelihood\n", "data_loss_func = lambda y, y_pred: -gauss_loglik(y, y_pred, log_noise_var)\n", "# hyper-parameters of training\n", "beta = 1.0\n", "N_epochs = 2000\n", "# the training loop starts\n", "logs = train_network(mfvi_regression_net, opt, dataloader, data_loss_func, \n", " beta=beta, verbose=True, N_epochs=N_epochs)\n", "\n", "# plot the training curve\n", "def plot_training_loss(logs, beta):\n", " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n", " ax1.plot(np.arange(logs.shape[0]), logs[:, 0], 'r-', label='nll')\n", " ax2.plot(np.arange(logs.shape[0]), logs[:, 1], 'r-', label='KL')\n", " ax1.legend()\n", " ax2.legend()\n", " ax1.set_xlabel('epoch')\n", " ax2.set_xlabel('epoch')\n", " ax1.set_title('ELBO (beta={})'.format(beta))\n", " ax2.set_title('ELBO (beta={})'.format(beta))\n", " plt.show()\n", "\n", "plot_training_loss(logs, beta)" ], "metadata": { "id": "z-rD6aj-TvLZ" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Training is now finished, let's plot the predictions again, but now it shows the mean & std of Bayesian predictive inference." ], "metadata": { "id": "T2TDVt0-hPTi" } }, { "cell_type": "code", "source": [ "y_pred_mean, y_pred_std_noiseless = get_regression_results(mfvi_regression_net, x_test_norm, K)\n", "model_noise_std = unnormalise_data(to_numpy(torch.exp(0.5*log_noise_var)), 0.0, dataset.y_std)\n", "y_pred_std = np.sqrt(y_pred_std_noiseless ** 2 + model_noise_std**2)\n", "plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std,\n", " title='BNN approx. posterior (MFVI)')\n", "print(model_noise_std, noise_std, y_pred_std_noiseless.mean())" ], "metadata": { "id": "dDWVAvPYibn3" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "**playground**\n", "\n", "You can test various hyper-parameter settings and see how they would affect the regression results.\n", "\n", "In particular, you can go back to previous code blocks and change the settings e.g,:\n", "\n", "1. Change the depth of the network, by changing the list of ```layer_sizes```;\n", "2. Activation functions: try e.g., ```nn.Tanh()``` or ```nn.ReLU()``` or the below ```Sine``` activation (copy-paste the code below to the place when you define the activation function);\n", "3. Training epochs: try setting different values for ```N_epochs``` when calling ```train_network()```, e.g., ```N_epochs = 1000``` vs ```N_epochs = 10000```, and see what happens to the training curves;\n", "4. Tempered ELBO: try using different values of ```beta```, e.g., ```beta = 0.1``` vs ```beta = 10```;\n", "5. Prior settings: try ```sqrt_width_scaling=True```;\n", "6. Initialisation of the q standard deviation ```init_std```.\n", "\n", "Come up with you own conclusions: which settings work well?" ], "metadata": { "id": "7D07ALhSrpSu" } }, { "cell_type": "code", "source": [ "# copy-paste below code to the appropriate place, and call it by\n", "# activation = Sine()\n", "class Sine(nn.Module):\n", " def __init__(self):\n", " super(Sine, self).__init__()\n", " def forward(self, x):\n", " return torch.sin(x)" ], "metadata": { "id": "x_ZNoqFvkl2-" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Using other q distributions?" ], "metadata": { "id": "r4k3n8ihJ1uO" } }, { "cell_type": "markdown", "source": [ "Hopefully from above tests, you can see that MFVI works but it needs careful settings of the hyper-parameters (i.e., tricks that are mostly hidden in paper appendices). \n", "\n", "A natural question is to ask whether other design of the q distribution would return better results. Within the Gaussian family, Gaussians with full-rank covariance matrices are more expressive than factorised Gaussians. But at the same time, it requires much more variational parameters if we were to use full covariance Gaussians for every layer. For example, a hidden layer with ```dim_in = dim_out = 50``` would need $50 \\times 50 = 2500$ parameters for parameterising the mean, but $\\sum_{i=1}^{50\\times 50} i = 3126250$ parameters for parameterising the (symmetric) full covariance matrix! Therefore, when selecting the q distribution family, one needs to also consider the computational & memory costs for such approximation.\n", "\n", "In below we will implement 2 \"economic\" solutions:\n", "\n", "1. The so-called \"last-layer BNN\" approach: only apply full-covariance Gaussian posterior approximation to the last layer of the network, and use MLE/MAP solutions for the previous layers. \n", "2. Monte Carlo dropout (MC-dropout). \n", "\n", "For mathematical foundations, see [lecture notes here](http://yingzhenli.net/home/pdf/ProbAI2022_lecture_note.pdf).\n" ], "metadata": { "id": "f9GetssjlunI" } }, { "cell_type": "markdown", "source": [ "## Last-layer BNN with full-covariace Gaussian approximation" ], "metadata": { "id": "ZztkJAkyNZUV" } }, { "cell_type": "markdown", "source": [ "For the \"last-layer BNN\" method we need to use a full-covariance Gaussian posterior approximation for the output layer of the network.\n", "\n", "In below block you will implment a BNN layer with variational inference and Gaussian q with **full covariance matrix**, and more specifically:\n", "\n", "* the initialisation of the variational parameters\n", "* the forward pass with Monte Carlo\n", "* the $KL[q||p]$ regularisation term for one layer" ], "metadata": { "id": "zEmpAu4gWUGI" } }, { "cell_type": "code", "source": [ "class FullCovGaussianLinear(nn.Module):\n", " \"\"\"Applies a linear transformation to the incoming data: y = xW^T + b, where \n", " the weight W and bias b are sampled from the q distribution.\n", " \"\"\"\n", "\n", " def __init__(self, dim_in, dim_out, prior_weight_std=1.0, prior_bias_std=1.0, init_std=0.5,\n", " sqrt_width_scaling=False, device=None, dtype=None):\n", " factory_kwargs = {'device': device, 'dtype': dtype}\n", " super(FullCovGaussianLinear, self).__init__()\n", " self.dim_in = dim_in # dimension of network layer input\n", " self.dim_out = dim_out # dimension of network layer output\n", "\n", " # define the trainable variational parameters for q distribtuion\n", " # first define and initialise the mean parameters\n", " self.weight_mean = nn.Parameter(torch.empty((dim_out, dim_in), **factory_kwargs))\n", " self.bias_mean = nn.Parameter(torch.empty(dim_out, **factory_kwargs))\n", " # then define and initialise the parameters for covariance matrix\n", " # note here that we concatenate all the parameters in W and b and work with their covariances\n", " # in particular, we would like to parameterise the Cholesky factor L of the covariance \n", " # so that LL^T = covariance matrix of q(W, b)\n", " num_params = dim_out * dim_in + dim_out # total number of parameters\n", " self._cov_diag = nn.Parameter(torch.empty((num_params,), **factory_kwargs))\n", " self._cov_tril = nn.Parameter(torch.empty((num_params, num_params), **factory_kwargs))\n", " self.reset_parameters()\n", " \n", " # define the prior parameters (for prior p, assume the mean is 0)\n", " prior_mean = 0.0\n", " if sqrt_width_scaling: # prior variance scales as 1/dim_in\n", " prior_weight_std /= self.dim_in ** 0.5\n", " self.prior_weight_std = prior_weight_std\n", " self.prior_bias_std = prior_bias_std\n", " # prior parameters are registered as constants\n", " self.register_buffer(\"prior_mean\", torch.full((num_params,), prior_mean, **factory_kwargs))\n", " prior_weight_std = torch.full_like(self.weight_mean, prior_weight_std)\n", " prior_bias_std = torch.full_like(self.bias_mean, prior_bias_std)\n", " prior_std_diag = torch.concat((prior_weight_std.flatten(), prior_bias_std.flatten()))\n", " self.register_buffer('prior_scale_tril', prior_std_diag.diag_embed())\n", "\n", " def extra_repr(self):\n", " s = \"dim_in={}, dim_in={}, bias=True\".format(self.dim_in, self.dim_out)\n", " s += f\", weight prior std={self.prior_weight_std:.2f}\"\n", " s += f\", bias prior std={self.prior_bias_std:.2f}\"\n", " return s\n", "\n", " def reset_parameters(self, init_std=0.5):\n", " nn.init.kaiming_uniform_(self.weight_mean, a=math.sqrt(5))\n", " bound = self.dim_in ** -0.5\n", " nn.init.uniform_(self.bias_mean, -bound, bound)\n", " _init_std_param = math.log(math.expm1(init_std))\n", " self._cov_diag.data = torch.full_like(self._cov_diag.data, _init_std_param)\n", " self._cov_tril.data = torch.full_like(self._cov_tril.data, 0.0)\n", "\n", " # define the q distribution standard deviations with property decorator\n", " @property\n", " def mean(self):\n", " # flatten the weight matrix into a vector\n", " return torch.concat((self.weight_mean.flatten(), self.bias_mean.flatten()))\n", "\n", " @property\n", " def scale_tril(self):\n", " # this returns the cholesky decomposition L of the covariance: Cov = LL^T\n", " return F.softplus(self._cov_diag).diagflat() + torch.tril(self._cov_tril, diagonal=-1)\n", "\n", " # KL divergence KL[q||p] between two Gaussians\n", " def kl_divergence(self):\n", " q = dist.MultivariateNormal(self.mean, scale_tril=self.scale_tril)\n", " p = dist.MultivariateNormal(self.prior_mean, scale_tril=self.prior_scale_tril)\n", " kl = dist.kl_divergence(q, p).sum()\n", " return kl\n", "\n", " # forward pass with Monte Carlo (MC) sampling\n", " def forward(self, input):\n", " weight, bias = self._normal_sample(self.mean, self.scale_tril)\n", " return F.linear(input, weight, bias)\n", "\n", " def _normal_sample(self, mean, scale_tril):\n", " sample = mean + scale_tril @ torch.randn_like(mean)\n", " # chunk out the weight and bias\n", " weight_vec, bias = torch.split(sample, [self.dim_in * self.dim_out, self.dim_out])\n", " return weight_vec.reshape(self.dim_out, self.dim_in), bias\n", "\n", "# now also define a function to make a network with only last layer using variational inference\n", "def make_last_layer_bnn(layer_sizes, activation='LeakyReLU', **layer_kwargs):\n", " nonlinearity = getattr(nn, activation)() if isinstance(activation, str) else activation\n", " net = nn.Sequential()\n", " linear_layer_kwargs = {}\n", " if 'device' in layer_kwargs:\n", " linear_layer_kwargs['device'] = layer_kwargs['device']\n", " for i, (dim_in, dim_out) in enumerate(zip(layer_sizes[:-2], layer_sizes[1:-1])):\n", " net.add_module(f'Linear{i}', nn.Linear(dim_in, dim_out, **linear_layer_kwargs))\n", " if i < len(layer_sizes) - 2:\n", " net.add_module(f'Nonlinarity{i}', nonlinearity)\n", " # last layer using variational inference with full covariance matrix Gaussian\n", " net.add_module(f'FullCovGaussianLinear{i+1}', \n", " FullCovGaussianLinear(layer_sizes[-2], layer_sizes[-1], **layer_kwargs))\n", " return net" ], "metadata": { "id": "Hhi3VIdSNQ8r" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now we can run the same regression test again and see how the above full-covariance approximation method compare with the MFVI approach above." ], "metadata": { "id": "shfw6zNCXhj4" } }, { "cell_type": "code", "source": [ "# build a last-layer-BNN with 2-hidden-layers, with hidden layer width = h_dim\n", "h_dim = 50\n", "layer_sizes = [x_dim, h_dim, h_dim, y_dim]\n", "# you can change the activation function here\n", "activation=nn.GELU()\n", "# you can change the prior parameters as you wish\n", "layer_kwargs = {'prior_weight_std': 1.0,\n", " 'prior_bias_std': 1.0,\n", " 'sqrt_width_scaling': False,\n", " 'init_std': 0.5,\n", " 'device': device}\n", "last_layer_bnn_regression_net = make_last_layer_bnn(layer_sizes, activation=activation, **layer_kwargs)\n", "# we assume a Gaussian likelihood with homogeneuous noise\n", "last_layer_bnn_log_noise_var = nn.Parameter(torch.ones(size=(), device=device)*-3.0)\n", "# print out the BNN settings\n", "print(\"BNN architecture: \\n\", last_layer_bnn_regression_net)\n", "\n", "y_pred_mean, y_pred_std_noiseless = get_regression_results(last_layer_bnn_regression_net, x_test_norm, K)\n", "model_noise_std = unnormalise_data(to_numpy(torch.exp(0.5*last_layer_bnn_log_noise_var)), 0.0, dataset.y_std)\n", "y_pred_std = np.sqrt(y_pred_std_noiseless ** 2 + model_noise_std**2)\n", "plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std,\n", " title='BNN init (before training, last-layer BNN)')\n", "print(model_noise_std, noise_std, y_pred_std_noiseless.mean())" ], "metadata": { "id": "8AHiqu5eXpC_" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now start training for this \"last-layer-BNN\" network.\n" ], "metadata": { "id": "w2FjUc8XZKFh" } }, { "cell_type": "code", "source": [ "# start training\n", "learning_rate = 1e-3\n", "params = list(last_layer_bnn_regression_net.parameters()) + [last_layer_bnn_log_noise_var]\n", "last_layer_bnn_opt = torch.optim.Adam(params, lr=learning_rate)\n", "data_loss_func = lambda y, y_pred: -gauss_loglik(y, y_pred, last_layer_bnn_log_noise_var)\n", "\n", "beta = 1.0\n", "N_epochs = 2000\n", "logs = train_network(last_layer_bnn_regression_net, last_layer_bnn_opt, \n", " dataloader, data_loss_func, beta=beta, verbose=True, N_epochs=N_epochs)\n", "# plot the training curve\n", "plot_training_loss(logs, beta)" ], "metadata": { "id": "WPERGYPbZKl-" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Training is now finished.\n", "\n", "Let's plot the predictions again, and compare it with the MFVI results above." ], "metadata": { "id": "UWLGn6sxZmnC" } }, { "cell_type": "code", "source": [ "# plot the prediction results\n", "y_pred_mean, y_pred_std_noiseless = get_regression_results(last_layer_bnn_regression_net, x_test_norm, K)\n", "model_noise_std = unnormalise_data(to_numpy(torch.exp(0.5*last_layer_bnn_log_noise_var)), 0.0, dataset.y_std)\n", "y_pred_std = np.sqrt(y_pred_std_noiseless ** 2 + model_noise_std**2)\n", "plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std,\n", " title='BNN approx. posterior (last-layer BNN)')\n", "print(model_noise_std, noise_std, y_pred_std_noiseless.mean())" ], "metadata": { "id": "9C7CmwUfcpui" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Similar to the MFVI tests, you can also test various hyper-parameter settings and see how they would affect the regression results using \"last-layer BNN\"." ], "metadata": { "id": "smpFt88Gi0FS" } }, { "cell_type": "markdown", "source": [ "## Monte Carlo dropout (MC-dropout)" ], "metadata": { "id": "9YwGM-0WQhTx" } }, { "cell_type": "markdown", "source": [ "For the mathematical foundation, see [lecture notes here](http://yingzhenli.net/home/pdf/ProbAI2022_lecture_note.pdf).\n", "\n", "For the MC-dropout method with dropout probability $p$, the q distribution is a mixture of delta measures. Specifically, for a layer with parameters $\\{W, b \\}, W \\in \\mathbb{R}^{d_{out} \\times d_{in}}, b \\in \\mathbb{R}^{d_{out}}$, the q distribution is\n", "\n", "$q(W, b) = \\prod_{i=1}^{d_{out}}q(W_i, b_i),$\n", "\n", "$q(W_i, b_i) = p \\delta(W_i = 0, b_i = 0) + (1 - p) \\delta(W_i = M_i, b_i = m_i),$\n", "\n", "with variational parameter for the layer as $\\{M, m \\}, M \\in \\mathbb{R}^{d_{out} \\times d_{in}}, m \\in \\mathbb{R}^{d_{out}}$. This means there are two equivalent way to compute $\\mathbb{E}_q[\\log p(y|x, \\theta)]$ with Monte Carlo: for the forward pass of a layer, below computations are equivalent:\n", "\n", "1. **drop weights**: sample $W, b \\sim q(W, b)$, then compute $y = xW^T + b$;\n", "2. **drop units**: compute $\\hat{y} = x M^T + m$, then apply dropout $y = \\text{dropout}(\\hat{y}; p)$.\n", "\n", "The $KL[q||p]$ regularizer for MC-dropout is ill-defined for Gaussian prior $p(\\theta)$. In practice this is replaced by an $\\ell_2$ regularizer, i.e., $\\frac{1-p}{2\\sigma^2_{\\text{prior}}}|| M ||_2^2$ for the weight variational parameter $M$ (and similarly for $m$).\n", "\n", "In below block you will implment a BNN layer with MC-dropout, and more specifically:\n", "\n", "* the forward pass with Monte Carlo: you are asked to implement both \"drop weight\" and the \"drop unit\" approach.\n", "* the $KL[q||p]$ regularisation term for one layer: use the $\\ell_2$ regularizer" ], "metadata": { "id": "Vfg88KutVyPb" } }, { "cell_type": "code", "source": [ "class MCDropoutLinear(nn.Module):\n", " \"\"\"Applies a linear transformation to the incoming data: y = xW^T + b, where \n", " the weight W and bias b are sampled from the q distribution.\n", " \"\"\"\n", "\n", " def __init__(self, dim_in, dim_out, prior_weight_std=1.0, prior_bias_std=1.0, dropout_prob=0.1,\n", " sqrt_width_scaling=False, device=None, dtype=None):\n", " factory_kwargs = {'device': device, 'dtype': dtype}\n", " super(MCDropoutLinear, self).__init__()\n", " self.dim_in = dim_in # dimension of network layer input\n", " self.dim_out = dim_out # dimension of network layer output\n", " self.dropout_prob = dropout_prob\n", " # the boolean indicator for the dropout implementation\n", " # if true then run the \"drop weight\" forward pass, else run \"drop unit\"\n", " self.drop_weight = False\n", "\n", " # define the trainable variational parameters for q distribtuion\n", " # first define and initialise the mean parameters\n", " self.weight = nn.Parameter(torch.empty((dim_out, dim_in), **factory_kwargs))\n", " self.bias = nn.Parameter(torch.empty(dim_out, **factory_kwargs))\n", " self.reset_parameters()\n", " \n", " # define the prior parameters (for prior p, assume the mean is 0)\n", " prior_mean = 0.0\n", " if sqrt_width_scaling: # prior variance scales as 1/dim_in\n", " prior_weight_std /= self.dim_in ** 0.5\n", " # prior parameters are registered as constants\n", " self.register_buffer('prior_weight_mean', torch.full_like(self.weight, prior_mean))\n", " self.register_buffer('prior_weight_std', torch.full_like(self.weight, prior_weight_std))\n", " self.register_buffer('prior_bias_mean', torch.full_like(self.bias, prior_mean))\n", " self.register_buffer('prior_bias_std', torch.full_like(self.bias, prior_bias_std)) \n", "\n", " def extra_repr(self):\n", " s = \"dim_in={}, dim_in={}, bias=True\".format(self.dim_in, self.dim_out)\n", " weight_std = self.prior_weight_std.data.flatten()[0]\n", " if torch.allclose(weight_std, self.prior_weight_std):\n", " s += f\", weight prior std={weight_std.item():.2f}\"\n", " bias_std = self.prior_bias_std.flatten()[0]\n", " if torch.allclose(bias_std, self.prior_bias_std):\n", " s += f\", bias prior std={bias_std.item():.2f}\"\n", " return s\n", "\n", " def reset_parameters(self, init_std=0.5):\n", " nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n", " bound = self.dim_in ** -0.5\n", " nn.init.uniform_(self.bias, -bound, bound)\n", "\n", " def set_dropout_mode(self, drop_weight=False):\n", " self.drop_weight = drop_weight\n", "\n", " # KL divergence KL[q||p]: for MCDropout this returns an l2 regulariser\n", " def kl_divergence(self):\n", " ### begin of your code ###\n", " # hints: using an appropriate l2 regulariser as the kl\n", "\n", " kl_weight = \n", " kl_bias = \n", "\n", " ### end of your code ###\n", " return kl_weight + kl_bias\n", "\n", " # forward pass with Monte Carlo (MC) sampling\n", " def forward(self, input):\n", " ### begin of your code ###\n", " # hints: your can implement the dropout method using an appropriate masking matrix\n", " if self.drop_weight: # drop weight method\n", " out = \n", "\n", " else: # drop unit method \n", " out = \n", "\n", " ### end of your code ###\n", " return out\n", "\n", "# construct a BNN\n", "def make_mcdropout_bnn(layer_sizes, activation='LeakyReLU', **layer_kwargs):\n", " nonlinearity = getattr(nn, activation)() if isinstance(activation, str) else activation\n", " net = nn.Sequential()\n", " for i, (dim_in, dim_out) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):\n", " net.add_module(f'MCDropoutLinear{i}', MCDropoutLinear(dim_in, dim_out, **layer_kwargs))\n", " if i < len(layer_sizes) - 2:\n", " net.add_module(f'Nonlinarity{i}', nonlinearity)\n", " return net" ], "metadata": { "id": "SeuiRYwNn2Or" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now we can run the same regression test again and see how the MC-dropout method compare with the other approaches above." ], "metadata": { "id": "jgKHmufGU4-g" } }, { "cell_type": "code", "source": [ "# build a BNN with 2-hidden-layers, with hidden layer width = h_dim\n", "h_dim = 50\n", "layer_sizes = [x_dim, h_dim, h_dim, y_dim]\n", "# you can change the activation function here\n", "activation=nn.GELU()\n", "# you can change the prior parameters as you wish\n", "layer_kwargs = {'prior_weight_std': 1.0,\n", " 'prior_bias_std': 1.0,\n", " 'sqrt_width_scaling': False,\n", " 'dropout_prob': 0.1,\n", " 'device': device}\n", "mcdropout_bnn_regression_net = make_mcdropout_bnn(layer_sizes, activation=activation, **layer_kwargs)\n", "# we assume a Gaussian likelihood with homogeneuous noise\n", "mcdropout_bnn_log_noise_var = nn.Parameter(torch.ones(size=(), device=device)*-3.0)\n", "# print out the BNN settings\n", "print(\"BNN architecture: \\n\", mcdropout_bnn_regression_net)\n", "\n", "def set_dropout_mode(bnn, drop_weight=False):\n", " print(\"set the dropout mode: drop_weight={}\".format(drop_weight))\n", " for module in bnn:\n", " if hasattr(module, 'set_dropout_mode'):\n", " module.set_dropout_mode(drop_weight)\n", "\n", "set_dropout_mode(mcdropout_bnn_regression_net, drop_weight=True)\n", "y_pred_mean, y_pred_std_noiseless = get_regression_results(mcdropout_bnn_regression_net, x_test_norm, K)\n", "model_noise_std = unnormalise_data(to_numpy(torch.exp(0.5*mcdropout_bnn_log_noise_var)), 0.0, dataset.y_std)\n", "y_pred_std = np.sqrt(y_pred_std_noiseless ** 2 + model_noise_std**2)\n", "plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std,\n", " title='BNN init (before training, MC-dropout)')\n", "print(model_noise_std, noise_std, y_pred_std_noiseless.mean())" ], "metadata": { "id": "ulX_qTfjm-JU" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now start training for this \"MC-dropout BNN\" network." ], "metadata": { "id": "s13GEP1XVIkq" } }, { "cell_type": "code", "source": [ "# start training\n", "learning_rate = 1e-3\n", "params = list(mcdropout_bnn_regression_net.parameters()) + [mcdropout_bnn_log_noise_var]\n", "mcdropout_bnn_opt = torch.optim.Adam(params, lr=learning_rate)\n", "data_loss_func = lambda y, y_pred: -gauss_loglik(y, y_pred, mcdropout_bnn_log_noise_var)\n", "set_dropout_mode(mcdropout_bnn_regression_net, drop_weight=False)\n", "\n", "beta = 1.0\n", "N_epochs = 2000\n", "logs = train_network(mcdropout_bnn_regression_net, mcdropout_bnn_opt, \n", " dataloader, data_loss_func, beta=beta, verbose=True, N_epochs=N_epochs)\n", "# plot the training curve\n", "plot_training_loss(logs, beta)" ], "metadata": { "id": "W-j_7rDhnGdL" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Training is now finished.\n", "\n", "Let's plot the predictions again, and compare it with the results above." ], "metadata": { "id": "oCFsxSHsVTeA" } }, { "cell_type": "code", "source": [ "# plot the prediction results\n", "set_dropout_mode(mcdropout_bnn_regression_net, drop_weight=True)\n", "y_pred_mean, y_pred_std_noiseless = get_regression_results(mcdropout_bnn_regression_net, x_test_norm, K)\n", "model_noise_std = unnormalise_data(to_numpy(torch.exp(0.5*mcdropout_bnn_log_noise_var)), 0.0, dataset.y_std)\n", "y_pred_std = np.sqrt(y_pred_std_noiseless ** 2 + model_noise_std**2)\n", "plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std,\n", " title='BNN approx. posterior (MC-dropout)')\n", "print(model_noise_std, noise_std, y_pred_std_noiseless.mean())" ], "metadata": { "id": "sFThZlR6nNKG" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Similar to previous tests, you can also test various hyper-parameter settings and see how they would affect the regression results using MC-dropout.\n", "\n", "In particular, try setting different value for ```dropout_prob``` in ```layer_kwargs```.\n", "\n", "Also you can try using \"drop unit\" forward pass when visualising the results, by using\n", "\n", "```set_dropout_mode(mcdropout_bnn_regression_net, drop_weight=False)```\n", "\n", "You can also try using \"drop weight\" forward pass when training:\n", "\n", "```set_dropout_mode(mcdropout_bnn_regression_net, drop_weight=True)```\n", "\n", "By doing so you'll see some visual differences in terms of learning curves and posterior predictive mean/std visualisations. Can you explain that?" ], "metadata": { "id": "MgqfF_nqVZcw" } }, { "cell_type": "markdown", "source": [ "# Case study 1: Bayesian optimisation" ], "metadata": { "id": "4gyMwQLznjrr" } }, { "cell_type": "markdown", "source": [ "### What is Bayesian optimisation (BO)?\n", "\n", "(Feel free to skip this part if you already understand the idea.)\n", "\n", "Assume you are interested in optimising a function $x^* = \\arg\\max f_0(x)$, but you don't actually know the analytic form of $f(x)$. Rather, you only have access of it as a \"black-box\", where you provide an input $x$ to this \"black-box\", and it will return you a (noisy version of) output $y = f_0(x) + \\text{noise}$.\n", "\n", "Bayesian optimisation (BO) is a class of methods that tackle this challenge. \n", "\n", "To motivate BO, let's imagine you already have a set of datapoints $D = \\{ (x_n, y_n) \\}_{n=1}^N$ collected by sending the queries $x_1, ..., x_N$ to the above mentioned \"black-box\". Then we can fit a *surrogate function* $f_{\\theta}(x)$ to data. In large data limit $N \\rightarrow +\\infty$, with flexible enough model, we expect $f_{\\theta}(x)$ to be very close, if not identical, to the ground-truth function $f_0(x)$. In such case we can solve the optimisation problem by finding $x^* = \\arg\\max f_{\\theta}(x)$ instead, which is tractable.\n", "\n", "However, in practice we don't have a big dataset to train the surrogate model. This is expecially the case if the \"black-box\" corresponds to an expensive experiment (e.g., training a Transformer network where $x$ represents the hyper-parameter settings). In such scenario $f_{\\theta}(x)$ will be quite different from $f(x)$ in most of the unseen input locations. \n", "\n", "The key idea of BO is to optimise $f_0$ using \"helps\" from this surrogate by taking the uncertainty of model fitting into account, and it aims to find the optimum of $f_0$ with least amount of queries to the expensive \"black-box\". Specifically, there are 3 ingredients of an BO method:\n", "\n", "1. Acquisition function: using the current estimated surrogate function $f_{\\theta}(x)$ and its uncertainty estimate to compute an acquisition function;\n", "2. Query the \"black-box\": find the next input to query by maximising the acquisition function;\n", "3. Surrogate model update: given new queried result and historical data, update the surrogate model and its uncertainty estimate.\n", "\n", "At the beginning since the model is uncertain, a good acquisition function will take uncertainty into account and encourage \"exploration\", i.e., querying inputs at different locations.\n", "\n", "As we collect more data, with proper Bayesian posterior updates, the surrogate model $f_{\\theta}$ will become closer and closer to the ground-truth function $f_0$ and the uncertainty will be reduced.\n", "\n", "So at some point, the model will become certain about its output, and a good acquisition function will also enable \"exploitation\" at this stage, to seek for the optimum of the surrogate function $f_{\\theta}$ as to solve the original optimization problem." ], "metadata": { "id": "3kDCCbrqiMEJ" } }, { "cell_type": "markdown", "source": [ "### The Upper Confidence Bound (UCB) method\n", "\n", "The UCB is an acquisition function that uses both mean prediction and uncertainty. Specifically, assume the surrogate model provides both mean $m(x)$ and standard deviation $\\sigma(x)$ for a given input $x$, then the UCB acquisition function is the following:\n", "\n", "$$UCB(x) = m(x) + \\beta \\sigma(x).$$\n", "\n", "And the query procedure will pick the next query input as $x = \\arg\\max UCB(x)$.\n", "\n", "Initially, $\\sigma(x)$ can be quite large for many regions, meaning that UCB will mainly explore. As we collect more data, $\\sigma(x)$ will decrease around the regions that have been searched, and this allows the algorithm to \n", "\n", "1. ignore some searched regions where the model confidently thinks their function value is small;\n", "2. exploit some other searched regions where the model confidently believes the optimum might be there;\n", "3. explore some other promising regions that has not been searched before.\n", "\n", "Here $\\beta$ a hyper-parameter specified by the user at a time, to achieve a desired balance between exploration ($\\sigma(x)$) and exploitation ($m(x)$). When $\\beta = 0$, it means we trust the surrogate model and exploit on that. When $\\beta$ is large, we allow the query process to focus on regions that have large $\\sigma(x)$ value (where the model is most uncertainty about). For optimal BO, this $\\beta$ coefficient will decrease during time; for simplicity, in this demo we only consider a fixed value of $\\beta$." ], "metadata": { "id": "bVVZAjrRiQAw" } }, { "cell_type": "markdown", "source": [ "First we implement the ```ucb``` acquisition function as mentioned above." ], "metadata": { "id": "jIVBSDOViWXa" } }, { "cell_type": "code", "source": [ "# the UCB acquisition function\n", "def ucb(net, x, beta, K):\n", " ### begin of your code ###\n", " # hint: do K times of forward passes and then compute the mean and std\n", " # final results should have shape (N, 1) with x having shape (N, dim_x)\n", "\n", " y_pred_mean = \n", " y_pred_std =\n", "\n", " ### end of your code ###\n", " return y_pred_mean + math.sqrt(beta) * y_pred_std, y_pred_mean, y_pred_std\n", "\n", "# some helper function\n", "def get_next_query(net, x, acquisition_func, **kwargs):\n", " # search for the next query point in x using the acquisition function\n", " # and here we also assume that x_dim = 1\n", " f, y_pred_mean, y_pred_std = acquisition_func(net, x, **kwargs) # shape (N, 1)\n", " next_query = x[torch.argmax(f[:, 0])].unsqueeze(0)\n", " return next_query, f, y_pred_mean, y_pred_std\n", "\n", "def query_oracle(x, oracle_func, noise_std=None):\n", " # given input, query the oracle function (potentially with noise)\n", " y = oracle_func(x)\n", " if noise_std is not None and noise_std > EPS: \n", " y += np.random.randn(y.shape[0], y.shape[1]) * noise_std\n", " return y\n", "\n", "def reset_parameters(bnn, init_std):\n", " print(\"re-initialise the parameters with init_std={}\".format(init_std))\n", " for module in bnn:\n", " if hasattr(module, 'reset_parameters'):\n", " module.reset_parameters(init_std)" ], "metadata": { "id": "uEZGG31Gnoak" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Now let's implement the main loop of BO." ], "metadata": { "id": "DI1pQKESiahp" } }, { "cell_type": "code", "source": [ "# now define the Bayesian optimisation search\n", "def bayesian_optimisation(net, log_noise_var, oracle_func, acquisition_func, x, x_init, T,\n", " noise_std = None, init_std = 0.1, N_epochs = 1000, **kwargs):\n", " # we start from a number of observations to initialise the dataset\n", " y_init = query_oracle(x_init, oracle_func, noise_std)\n", " observations = regression_data(x_init, y_init)\n", " # preparing the query candidates\n", " x_norm = normalise_data(x, observations.x_mean, observations.x_std)\n", " x_tensor = torch.tensor(x_norm).float().to(device)\n", " # now start the loop of BO for T steps\n", " acquisition_func_list = []\n", " mean_func_list = []; std_func_list = []\n", " for t in range(T):\n", " dataloader = DataLoader(observations, batch_size=min(100, len(observations)), shuffle=True)\n", " # train the network\n", " print(\"update BNN posterior...\")\n", " params = list(net.parameters()) + [log_noise_var]\n", " opt = torch.optim.Adam(params, lr=1e-3)\n", " data_loss_func = lambda y, y_pred: -gauss_loglik(y, y_pred, log_noise_var)\n", " # if using MC-dropout, use \"drop unit\" mode for training\n", " set_dropout_mode(net, drop_weight=False) # won't do anything if not usig MC-dropout\n", " train_network(net, opt, dataloader, data_loss_func, N_epochs=N_epochs, verbose=True)\n", " # now get next acquisition point and query\n", " print(\"query next point...\")\n", " # if using MC-dropout, use \"drop weight\" mode for computing the UCB acquisition function\n", " set_dropout_mode(net, drop_weight=True) # won't do anything if not usig MC-dropout\n", " x_next, f, y_pred_mean, y_pred_std = get_next_query(net, x_tensor, acquisition_func, **kwargs)\n", " x_next = unnormalise_data(to_numpy(x_next), observations.x_mean, observations.x_std)\n", " y_next = query_oracle(x_next, oracle_func, noise_std)\n", " # record the selections to be visualised\n", " acquisition_func_list.append(unnormalise_data(to_numpy(f), observations.y_mean, observations.y_std))\n", " mean_func_list.append(unnormalise_data(to_numpy(y_pred_mean), observations.y_mean, observations.y_std))\n", " std_func_list.append(unnormalise_data(to_numpy(y_pred_std), 0.0, observations.y_std))\n", " # add new observation to the dataset\n", " x_init = np.concatenate((x_init, x_next), axis=0)\n", " y_init = np.concatenate((y_init, y_next), axis=0)\n", " observations.update_data(x_init, y_init, update_stats=False)\n", " # reset the network for next round's training\n", " init_std_ = init_std * 0.9 ** (t+1) if init_std is not None else None\n", " reset_parameters(net, init_std=init_std_)\n", " log_noise_var.data = torch.full_like(log_noise_var.data, -3.0)\n", "\n", " return acquisition_func_list, mean_func_list, std_func_list, observations\n", "\n", "# define plot function\n", "def plot_bo_one_step(x_init, y_init, x, y_true, x_query, y_query, acquisition_value, \n", " mean_value, std_value, beta, title):\n", " plt.figure()\n", " plt.plot(x, y_true, 'k-', label='ground truth')\n", " plt.plot(x, mean_value, 'b-', label='prediction mean')\n", " plt.fill_between(x[:,0], mean_value[:,0]-math.sqrt(beta)*std_value[:,0], \n", " mean_value[:,0]+math.sqrt(beta)*std_value[:,0], \n", " color='b', alpha=0.3, label='epistemic uncertainty')\n", " plt.plot(x, acquisition_value, 'g-', label='acquisition func.')\n", " plt.plot(x_init, y_init, 'ro', label='observed data')\n", " plt.plot(x_query, y_query, 'go', label='query')\n", " plt.axvline(x_query, ymax=y_query, color='g', linestyle='--')\n", " plt.legend()\n", " plt.title(title)\n", " plt.show()" ], "metadata": { "id": "ne3pSg4TTrSm" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "With all the helper functions set, let's test BO with MFVI-BNN on optimising a ground-truth function defined below." ], "metadata": { "id": "HRU1vDmnidXN" } }, { "cell_type": "code", "source": [ "# now start the test\n", "# first define the ground-truth/oracle function that we want to optimize\n", "def ground_truth_func_func(x):\n", " return np.sin(x * math.pi / 2 + 0.8) * np.exp(-0.1 * np.abs(x)) + 0.1 * x\n", "oracle_func = ground_truth_func\n", "\n", "# set-up BO hyper-parameters\n", "acquisition_func = ucb\n", "ucb_params = {'beta': 1.0, 'K': 50}\n", "noise_std = 0.1\n", "x_min = -3; x_max = 3; dx = 0.01\n", "x = np.arange(x_min, x_max+EPS, dx)[:, np.newaxis]\n", "y_true = query_oracle(x, oracle_func, noise_std=None)\n", "# choose some random query locations to start with\n", "N_init = 5\n", "x_init = np.copy(x); np.random.shuffle(x_init); x_init = x_init[:N_init]\n", "T = 10 # run T steps of BO search\n", "\n", "# then set-up the BNN\n", "x_dim, y_dim = x.shape[1], y_true.shape[1]\n", "h_dim = 50\n", "layer_sizes = [x_dim, h_dim, h_dim, y_dim]\n", "activation=nn.GELU()\n", "# MFVI-BNN hyper-parameters\n", "layer_kwargs = {'prior_weight_std': 1.0,\n", " 'prior_bias_std': 1.0,\n", " 'sqrt_width_scaling': False,\n", " 'init_std': 0.1,\n", " 'device': device}\n", "bo_net = make_mfvi_bnn(layer_sizes, activation=activation, **layer_kwargs)\n", "# we assume a Gaussian likelihood with homogeneuous noise\n", "bo_log_noise_var = nn.Parameter(torch.ones(size=(), device=device)*-3.0)\n", "# print out the BNN settings\n", "print(\"BNN architecture: \\n\", bo_net)\n", "\n", "# start the BO process!\n", "acquisition_func_list, mean_func_list, std_func_list, observations = \\\n", " bayesian_optimisation(bo_net, bo_log_noise_var, oracle_func, acquisition_func, x, x_init, T,\n", " noise_std=noise_std, init_std=layer_kwargs['init_std'], N_epochs=1000,\n", " **ucb_params)\n", " \n", "# plot results\n", "def plot_bo_results(acquisition_func_list, mean_func_list, std_func_list, observations, T, x, y_true):\n", " for t in range(T):\n", " x_init = unnormalise_data(observations.x[:N_init+t], observations.x_mean, observations.x_std)\n", " y_init = unnormalise_data(observations.y[:N_init+t], observations.y_mean, observations.y_std)\n", " x_query = unnormalise_data(observations.x[N_init+t], observations.x_mean, observations.x_std)\n", " y_query = unnormalise_data(observations.y[N_init+t], observations.y_mean, observations.y_std)\n", " acquisition_value = acquisition_func_list[t]\n", " mean_value = mean_func_list[t]; std_value = std_func_list[t]\n", " title = 'BO with UCB, t={}, beta={}'.format(t+1, ucb_params['beta'])\n", " plot_bo_one_step(x_init, y_init, x, y_true, x_query, y_query, acquisition_value, \n", " mean_value, std_value, beta = ucb_params['beta'], title=title)\n", "plot_bo_results(acquisition_func_list, mean_func_list, std_func_list, observations, T, x, y_true)" ], "metadata": { "id": "ZTy4QJCAkze5" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Let's test again but with some other settings of your choice. \n", "\n", "First, you can try using a BNN with MC-dropout, by e.g., copy-paste the code in below block to appropriate place.\n", "\n", "You can also try other hyper-parameters for the BNN, e.g., changing the activation function, number of layers, ```N_epochs```, initialisation of the q standard deviation ```init_std``` if using Gaussian approximations, ```dropout_prob``` if using MC-dropout, etc." ], "metadata": { "id": "vCsJROl2pM7o" } }, { "cell_type": "code", "source": [ "# MC-dropout BNN hyper-parameters\n", "layer_kwargs = {'prior_weight_std': 1.0,\n", " 'prior_bias_std': 1.0,\n", " 'sqrt_width_scaling': False,\n", " 'dropout_prob': 0.1,\n", " 'device': device}\n", "bo_net = make_mcdropout_bnn(layer_sizes, activation=activation, **layer_kwargs)\n", "layer_kwargs['init_std'] = None" ], "metadata": { "id": "nReyeRH8aFZt" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "Second, you can try BO for a different ground-truth function. You can define a new ground-truth function ```new_ground_truth(x)``` and set ```oracle_func = new_ground_truth``` to define the oracle to query. An example is provided below." ], "metadata": { "id": "KqnMnyfGZ3Rw" } }, { "cell_type": "code", "source": [ "# copy-paste some of the below code to the appropriate place\n", "# example: quadratic function\n", "def quadratic_func(x):\n", " return -2 * x ** 2 + 3 * x + 0.3\n", "oracle_func = quadratic_func" ], "metadata": { "id": "gb5NpdDUpSWn" }, "execution_count": null, "outputs": [] } ] } ================================================ FILE: day_5/5_yingzhen/regression_solution.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "Z97rDy1VjK4I" }, "source": [ "# A Hands-on Tutorial for Bayesian Neural Networks\n", "\n", "(Part 1: regression)\n", "\n", "[Yingzhen Li](http://yingzhenli.net/home/en/)\n", "\n", "(As part of the BNN lecture at [ProbAI 2022](https://probabilistic.ai/))\n", "\n", "In this tutorial, you will implement various Bayesian neural network methods based on variational inference.\n", "\n", "We will go through regression tasks to see the applications of uncertainty estimation in practice, including a case study on Bayesian optimisation.\n", "\n", "**How to use this tutorial notebook?**\n", "\n", "* Read the descriptions in the text;\n", "* Fill in the missing code whenever you see a block that looks like below:\n", "\n", "```\n", "### beginning of your code ###\n", "[insert your own code here]\n", "### end of your code ###\n", "```\n", "There will be hints provided in the code blocks as well to guide you through.\n", "\n", "Let us set up the required packages, and then, enjoy 😊" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1Zt00SKnNgyR" }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import math\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.distributions as dist\n", "from torch.utils.data import Dataset, DataLoader\n", "\n", "EPS = 1e-5 # define a small constant for numerical stability control" ] }, { "cell_type": "markdown", "metadata": { "id": "uJqCF8BXD7dO" }, "source": [ "# What is a Bayesian neural network?\n", "\n", "In short, a Bayesian neural network (BNN) is a neural network that use (approximate) Bayesian inference for uncertainty estimation. For example, we can treat the NN parameters as random variables and infer them using (approximate) Bayesian posterior inference.\n", "\n", "For the mathematical foundation, see [lecture notes here](http://yingzhenli.net/home/pdf/ProbAI2022_lecture_note.pdf)." ] }, { "cell_type": "markdown", "metadata": { "id": "-eYv-w6TNmW4" }, "source": [ "# Implementing the BNN with mean-field variational inference (MFVI)\n", "\n", "In this part, you will implement the Bayes-by-backprop algorithm by [Blundell et al. (2015)](https://arxiv.org/abs/1505.05424), which is doing mean-field variational inference (MFVI) in weight space for Bayesian neural networks.\n", "\n", "For the mathematical foundation, see [lecture notes here](http://yingzhenli.net/home/pdf/ProbAI2022_lecture_note.pdf).\n" ] }, { "cell_type": "markdown", "metadata": { "id": "kOQ75uGIkpW1" }, "source": [ "## Implementing the ```MFVILinear``` class and construct a BNN\n", "\n", "We will implement the MFVI approach in a modular way, so we need a \"BNN layer\" module ```MFVILinear``` to start with. This will allow us to stack-up many network layers efficiently. Then we will implement the training & testing functions, and test the model's performance with a regression example.\n", "\n", "In below block you will implment a BNN layer with mean-field variational inference (MFVI), and more specifically:\n", "\n", "* the initialisation of the variational parameters\n", "* the forward pass with Monte Carlo\n", "* the $KL[q||p]$ regularisation term for one layer, i.e., $KL[q(W) || p(W)] + KL[q(b) || p(b)]$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "dpydUZNVjCRV" }, "outputs": [], "source": [ "class MFVILinear(nn.Module):\n", " \"\"\"Applies a linear transformation to the incoming data: y = xW^T + b, where \n", " the weight W and bias b are sampled from the q distribution.\n", " \"\"\"\n", "\n", " def __init__(self, dim_in, dim_out, prior_weight_std=1.0, prior_bias_std=1.0, init_std=0.05,\n", " sqrt_width_scaling=False, device=None, dtype=None):\n", " factory_kwargs = {'device': device, 'dtype': dtype}\n", " super(MFVILinear, self).__init__()\n", " self.dim_in = dim_in # dimension of network layer input\n", " self.dim_out = dim_out # dimension of network layer output\n", "\n", " # define the trainable variational parameters for q distribtuion\n", " # first define and initialise the mean parameters\n", " self.weight_mean = nn.Parameter(torch.empty((dim_out, dim_in), **factory_kwargs))\n", " self.bias_mean = nn.Parameter(torch.empty(dim_out, **factory_kwargs))\n", " self._weight_std_param = nn.Parameter(torch.empty((dim_out, dim_in), **factory_kwargs))\n", " self._bias_std_param = nn.Parameter(torch.empty(dim_out, **factory_kwargs))\n", " self.reset_parameters(init_std)\n", "\n", " # define the prior parameters (for prior p, assume the mean is 0)\n", " prior_mean = 0.0\n", " if sqrt_width_scaling: # prior variance scales as 1/dim_in\n", " prior_weight_std /= self.dim_in ** 0.5\n", " # prior parameters are registered as constants\n", " self.register_buffer('prior_weight_mean', torch.full_like(self.weight_mean, prior_mean))\n", " self.register_buffer('prior_weight_std', torch.full_like(self._weight_std_param, prior_weight_std))\n", " self.register_buffer('prior_bias_mean', torch.full_like(self.bias_mean, prior_mean))\n", " self.register_buffer('prior_bias_std', torch.full_like(self._bias_std_param, prior_bias_std)) \n", "\n", " def extra_repr(self):\n", " s = \"dim_in={}, dim_in={}, bias=True\".format(self.dim_in, self.dim_out)\n", " weight_std = self.prior_weight_std.data.flatten()[0]\n", " if torch.allclose(weight_std, self.prior_weight_std):\n", " s += f\", weight prior std={weight_std.item():.2f}\"\n", " bias_std = self.prior_bias_std.flatten()[0]\n", " if torch.allclose(bias_std, self.prior_bias_std):\n", " s += f\", bias prior std={bias_std.item():.2f}\"\n", " return s\n", "\n", " def reset_parameters(self, init_std=0.05):\n", " nn.init.kaiming_uniform_(self.weight_mean, a=math.sqrt(5))\n", " bound = self.dim_in ** -0.5\n", " nn.init.uniform_(self.bias_mean, -bound, bound)\n", " _init_std_param = np.log(init_std)\n", " self._weight_std_param.data = torch.full_like(self.weight_mean, _init_std_param)\n", " self._bias_std_param.data = torch.full_like(self.bias_mean, _init_std_param)\n", "\n", " # define the q distribution standard deviations with property decorator\n", " @property\n", " def weight_std(self):\n", " return torch.clamp(torch.exp(self._weight_std_param), min=EPS)\n", "\n", " @property\n", " def bias_std(self):\n", " return torch.clamp(torch.exp(self._bias_std_param), min=EPS)\n", "\n", " # KL divergence KL[q||p] between two Gaussians\n", " def kl_divergence(self):\n", " q_weight = dist.Normal(self.weight_mean, self.weight_std)\n", " p_weight = dist.Normal(self.prior_weight_mean, self.prior_weight_std)\n", " kl = dist.kl_divergence(q_weight, p_weight).sum()\n", " q_bias = dist.Normal(self.bias_mean, self.bias_std)\n", " p_bias = dist.Normal(self.prior_bias_mean, self.prior_bias_std)\n", " kl += dist.kl_divergence(q_bias, p_bias).sum()\n", " return kl\n", "\n", " # forward pass with Monte Carlo (MC) sampling\n", " def forward(self, input):\n", " weight = self._normal_sample(self.weight_mean, self.weight_std)\n", " bias = self._normal_sample(self.bias_mean, self.bias_std)\n", " return F.linear(input, weight, bias)\n", "\n", " def _normal_sample(self, mean, std):\n", " return mean + torch.randn_like(mean) * std\n", "\n", "# construct a BNN\n", "def make_mfvi_bnn(layer_sizes, activation='LeakyReLU', **layer_kwargs):\n", " nonlinearity = getattr(nn, activation)() if isinstance(activation, str) else activation\n", " net = nn.Sequential()\n", " for i, (dim_in, dim_out) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):\n", " net.add_module(f'MFVILinear{i}', MFVILinear(dim_in, dim_out, **layer_kwargs))\n", " if i < len(layer_sizes) - 2:\n", " net.add_module(f'Nonlinarity{i}', nonlinearity)\n", " return net" ] }, { "cell_type": "markdown", "metadata": { "id": "2Pg74n4fjKEV" }, "source": [ "We have just defined the ```MFVILinear``` class and ```make_mfvi_bnn``` for network construction.\n", "\n", "In below we define the training procedure of a BNN, mainly by defining the training objective. This also involves helper functions that collects the KL regularizers accross layers as well as a prediction function.\n", "\n", "In particular, as we work with regression data, we will use Gaussian likelihood, i.e., $p(y | x, \\theta) = N(y; f_{\\theta}(x), \\sigma^2)$ where $f_{\\theta}$ is the neural network and $\\sigma^2$ is the output variance parameter that we will also optimise." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "CgTaXt3NnKSi" }, "outputs": [], "source": [ "# collect the kl divergence for all MFVILinear layers\n", "def kl_divergence(bnn):\n", " kl = 0.0\n", " for module in bnn:\n", " if hasattr(module, 'kl_divergence'):\n", " kl = kl + module.kl_divergence()\n", " return kl\n", "\n", "# define the training function which minimises the negative ELBO\n", "# note that the (tempered) negative ELBO = negative log-likliehood of dataset + beta * KL\n", "# where the neg. log-likelihood *per datapoint* is computed with a function\n", "# nll = data_loss_func(y, y_pred)\n", "# this data_loss_func is to be defined later, depending on the learning task\n", "def train_step(net, opt, data_loss_func, dataloader, N_data, beta=1.0):\n", " for _, (x, y) in enumerate(dataloader):\n", " x = x.to(device); y = y.to(device)\n", " opt.zero_grad() # opt is the optimiser\n", " y_pred = net(x)\n", " # use mini-batches, so be careful for the right data-count!\n", " nll = data_loss_func(y, y_pred).mean()\n", " kl = kl_divergence(net)\n", " loss = N_data * nll + beta * kl\n", " loss.backward()\n", " opt.step()\n", " return nll, kl\n", "\n", "# now define the data_loss_func that will be used later\n", "# in detail, we will define a Gaussian log-likelihood function below, and then define\n", "# data_loss_func = lambda y, y_pred: -gauss_loglik(y, y_pred, log_noise_var)\n", "# where log_noise_var is the output noise (log) variance which is a seperate model parameter\n", "# define gaussian log-likelihood\n", "def gauss_loglik(y, y_pred, log_noise_var):\n", " # y should have shape as (batch_size, y_dim)\n", " # y_pred should have shape as (batch_size, y_dim) or (K, batch_size, y_dim)\n", " # where K is the number of MC samples\n", " # this function should return per-data loss of shape (batch_size,) or (K, batch_size)\n", " l2_dist = (y - y_pred).pow(2).sum(-1)\n", " return -0.5 * (log_noise_var + math.log(2*math.pi) + l2_dist * torch.exp(-log_noise_var))\n", "\n", "# define the prediction function with Monte Carlo sampling using K samples\n", "def predict(bnn, x_test, K=1):\n", " y_pred = []\n", " for _ in range(K):\n", " y_pred.append(bnn(x_test))\n", " # shape (K, batch_size, y_dim) or (batch_size, y_dim) if K = 1\n", " return torch.stack(y_pred, dim=0).squeeze(0)\n", "\n", "# define the error metrics: RMSE and test negative log-likelihood\n", "# in below functions y_pred should have shape (K, batch_size, y_dim) or (batch_size, y_dim)\n", "def rmse(y, y_pred):\n", " if len(y_pred.shape) > 2: # using K > 1 MC samples\n", " y_pred = y_pred.mean(0)\n", " return (y - y_pred).pow(2).sum(-1).mean().sqrt()\n", "\n", "def test_nll(y, y_pred, data_loss_func):\n", " nll = data_loss_func(y, y_pred) # with shape (batch_size) or (K, batch_size)\n", " if len(nll) == 2: # using K > 1 MC samples\n", " nll = -torch.logsumexp(-nll, dim=0) + math.log(nll.shape[0]) # Bayesian predictive average\n", " return nll.mean()" ] }, { "cell_type": "markdown", "metadata": { "id": "StOG-wgEnv9t" }, "source": [ "## Testing: a simple regression task\n", "We will run a simple 1-D regression task to test the implemented BNN, in terms of its predictive performance and uncertainty estimation.\n", "\n", "Let's get some synthetic data:\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 281 }, "id": "QDjtmJQ8Lglu", "outputId": "527fd35d-ee7f-4880-f43d-4beee4f34cda" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def ground_truth_func(x):\n", " return np.sin(x * math.pi / 2 + 0.8) * np.exp(-0.1 * np.abs(x)) + 0.1 * x \n", "\n", "def gen_data(N_data, ground_truth_func, noise_std=None):\n", " # generate the training dataset, note here we will make data into 2 clusters\n", " x1 = np.random.randn(int(N_data/2), 1) * 0.5 + 2.0\n", " x2 = np.random.randn(int(N_data/2), 1) * 0.5 - 2.0\n", " x = np.concatenate([x1, x2], axis=0)\n", " y = ground_truth_func(x)\n", " if noise_std is not None and noise_std > EPS: \n", " # assume homogeneous noise setting, i.e., \"homoscedasticity\"\n", " y += np.random.randn(y.shape[0], y.shape[1]) * noise_std\n", " return x, y\n", "\n", "def normalise_data(x, mean, std):\n", " return (x - mean) / std\n", "\n", "def unnormalise_data(x, mean, std):\n", " return x * std + mean\n", "\n", "class regression_data(Dataset):\n", " def __init__(self, x, y, normalise=True):\n", " super(regression_data, self).__init__()\n", " self.update_data(x, y, normalise)\n", " \n", " def __len__(self):\n", " return self.x.shape[0]\n", " \n", " def __getitem__(self, index):\n", " x = torch.tensor(self.x[index]).float()\n", " y = torch.tensor(self.y[index]).float()\n", " return x, y\n", "\n", " def update_data(self, x, y, normalise=True, update_stats=True):\n", " assert x.shape[0] == y.shape[0]\n", " self.x = x\n", " self.y = y\n", " # normalise data\n", " self.normalise = normalise\n", " if update_stats:\n", " self.x_mean = self.x.mean(0) if normalise else 0.0\n", " self.x_std = self.x.std(0) if normalise else 1.0\n", " self.y_mean = self.y.mean(0) if normalise else 0.0\n", " self.y_std = self.y.std(0) if normalise else 1.0\n", " if self.normalise:\n", " self.x = normalise_data(self.x, self.x_mean, self.x_std)\n", " self.y = normalise_data(self.y, self.y_mean, self.y_std)\n", " \n", "N_data = 100\n", "noise_std = 0.1\n", "x_train, y_train = gen_data(N_data, ground_truth_func, noise_std)\n", "dataset = regression_data(x_train, y_train)\n", "dataloader = DataLoader(dataset, batch_size=100, shuffle=True)\n", "\n", "# plot the training data and ground truth\n", "x_test = np.arange(np.min(x_train) - 1.0, np.max(x_train)+1.0, 0.01)[:, np.newaxis]\n", "y_test = ground_truth_func(x_test)\n", "plt.plot(x_train, y_train, 'ro', label='data')\n", "plt.plot(x_test, y_test, 'k-', label='ground-truth')\n", "plt.legend()\n", "plt.title('ground-truth function')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "2PqirrxvLXOs" }, "source": [ "We set-up the hyper-parameters and build a BNN to be trained.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 437 }, "id": "SIIq7s3KVBbX", "outputId": "b3f8101b-46a1-4287-82dc-8618fff2049a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BNN architecture: \n", " Sequential(\n", " (MFVILinear0): MFVILinear(dim_in=1, dim_in=50, bias=True, weight prior std=1.00, bias prior std=1.00)\n", " (Nonlinarity0): GELU()\n", " (MFVILinear1): MFVILinear(dim_in=50, dim_in=50, bias=True, weight prior std=1.00, bias prior std=1.00)\n", " (Nonlinarity1): GELU()\n", " (MFVILinear2): MFVILinear(dim_in=50, dim_in=1, bias=True, weight prior std=1.00, bias prior std=1.00)\n", ")\n" ] }, { "data": { "image/png": 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9Bs2KE/yS7Cko+Cq2ZeXKlWRmZvL8889Xel1CCF5++WXOnTvHpk2bKr0+Fdvx3nvv4enpyaOPPlppdaiCXw7efPNNOnToQGhoKBMmTGD58uUMGDCA2bNnExISwvvvv893331H9+7dCQgIYMaMGWafqmXv+dChQwwYMABQeu4zZsxgwIABtGnThg8++MBcX1HhmAvy0ksvcf78eYKCgpg7dy7R0dH069ePsLAwOnfuTHx8PF27djXnX758OQsXLmTTpk0cOnSISZMmERQURFZWFgAffvghwcHBBAQEcObMmcr4GWsFBoOBlStXcu+99+b7/SuTESNG0LFjR/75z3+q8/KrCdeuXWPdunVMmzYNHx+fSqunqla8sjmzZ88mLi7OpmUGBQXx3nvvFZvn119/ZfPmzRw9epS8vDyCg4Pp0aMHoKxYdOjQIbKzs2nXrh3fffcd7du3Z8qUKfz73/9m9uzZxZZ95swZ9u/fT1paGh06dOCJJ57g2LFjfPXVV8TFxaHT6fLVZ8nSpUs5ceKE+TeJjo7myJEjnDhxgtatWxMfH19knWPGjOGjjz5i+fLlhITceTjPz8+PI0eO8Mknn7B8+XI+/fTTYm1XKZq9e/cSHx/P0qVLq6xOjUbDiy++yPTp09m7dy+DBw+usrpVyscnn3xCXl5epY7xgNrDLzM//PAD4eHhuLm54e3tzUMPPWTe9vDDDwPw+++/07p1a9q3bw/A1KlTiY2NLbHs4cOH4+rqip+fHw0bNuT69ev5wjHXqVPHHI65NPTq1YvWrVuX8QgVRo0aBUCPHj2sNhYqJbNy5Ur8/PyqPITxhAkTaNCgAR999FGV1qtSdnJyclixYgUPPvhgpYfGqLY9/JJ64vbA09OzxDxOTk4YDAZAWazaEldXV/NnrVZbrO/90qVL5sZm1qxZDB06tFh7LOstqu6CmGwpyQ4V61y7do1t27Yxe/bsfP9tVeDq6srMmTNZsmQJ8fHx6jq4DsyWLVu4ceMGTz/9dKXXpfbwy0jfvn3ZsWMH2dnZpKen8/XXXxfK06FDB+Lj4zl37hwAX3zxhTluir+/P4cPHwZg8+bNJdZnLRxzixYtiIuLIy4ujlmzZhUKw1yQRo0a8ddff5GUlEROTk4+u0vaV6V8REREoNPpeOyxx+xS/6xZs9BoNPz73/9WEhxgjWeVwvznP/+hdevW3HfffZVelyr4ZaRnz56EhYXRrVs3hg0bRkBAAHXr1s2Xx83NjVWrVjF27FgCAgLQaDTMmjULgAULFvD3v/+dkJAQtFptifVZC8dcEF9fX/r27UvXrl2ZO3duoe3Ozs689tpr9OrVi8GDB+cLzTxt2jRmzZqVb9BWpWJIKVm9ejX9+vUzu/aqmubNmzNy5Eg+/fRTslatqrQ1nlXKzx9//EF0dDSPPfYYGk0VyLG1tQ/t/arwmraVSFpampRSyoyMDNmjRw95+PBhO1tUO3CU/780HDlyRAJyxYoVdrUjOjpaAnKVr2+lrPGsUjGef/556eTkJK9evWqzMilmTVu1h18OZs6cSVBQEMHBwYwePZrg4GB7m6TiYERGRuLs7MzYsWPtakf//v3p0KEDnyUlFZ2hgms8q5Sf7OxsIiIiCA8Pp3HjxlVSZ7UdtLUna9eutbcJKg6MXq9n3bp1DBs2jPr169vVFiEEM2bM4MUXX+R3oEPBDJW0xrNKyURFRZGUlMTjjz9eZXWqPXwVFRsTGxvLlStXmFTB5TRtxZQpU9BqNKxyKtC/s8EazyrlZ+XKlbRp04ZBgwZVWZ2q4Kuo2JgtW7bg7u7Ogw8+aG9TAGjcuDHDH3yQ1Z6e6Fq2tOkazyrl488//yQmJobp06dXzWCtEVXwVVRsiJSSnTt3MmjQIDw8POxtjpkZM2ZwLTWVbz76CAwGiI9Xxd6OfPnllwA88sgjVVqvKvgqKjbk7NmzXLhwgQceeMDepuTjgQceoFGjRnz22Wf2NqXWI6VkzZo13HvvvbRq1apK61YF345ER0ebb/u3b99ebLyVgtEwr1y5wpgxYyrdRpWysWvXLgCGDRtmZ0vy4+zszOTJk9m5cydJ1mbsqFQJP/74I+fOnWPKlClVXrcq+JVAeeKQh4WF8dJLL1ndXlDwmzZtqoa/dUB27dpF586dHTKUwaRJk9DpdGzcuNHeptRq1qxZg7u7O6NHj67yulXBLwPx8fF07NiRSZMm0alTJ8aMGUNmZiaghEx48cUXCQ4OZuPGjezevZu77rqL4OBgxo4dS3p6OgDffvstHTt2JDg4mKioKHPZERER5lga169fZ+TIkQQGBhIYGMjBgwcLhT+2DHecnZ3N9OnTCQgIoHv37uzfv99c5qhRoxg6dCjt2rVj3rx5RR6Xv78/L7/8MkFBQYSEhHDkyBGGDBlC27ZtWbFihTnfsmXL6NmzJ926dWPBggXm9BEjRtCjRw+6dOnCypUrzeleXl7Mnz+fwMBA+vTpw/Xr123xNzgs6enpxMTEOJw7x0RQUBCdOnVSpxXbkezsbNavX8+oUaPw9vau8vqr7Tz82bPBxtGRCQqCkmKy/f7773z22Wf07duXGTNm8Mknn/DCCy8ASniDI0eOcPPmTUaNGsXevXvx9PTkrbfe4l//+hfz5s3jscceY9++ffztb38zR9csyLPPPss999zDli1b0Ov1pKenFwp/bBnB8uOPP0YIwfHjxzlz5gz3338/f/zxB6CsovPbb7/h6upKhw4deOaZZ2jRokWhOlu2bElcXBxz5sxh2rRp/PDDD2RnZ9O1a1dmzZrF7t27OXv2LL/88gtSSsLCwoiNjaV///58/vnn1K9fn6ysLHr27Mno0aPx9fUlIyODPn36sHjxYubNm8d///tfXn311XL8M9WDffv2kZub67CCL4Rg4sSJ/OMf/+DixYu0VOfgVzlff/01KSkpTJ061S71qz38MtKiRQv69u0LwOTJkzlw4IB5m0nAf/rpJ06dOkXfvn0JCgpi9erVJCQkcObMGVq3bk27du0QQjB58uQi69i3bx9PPPEEoESrLBirpyAHDhwwl9WxY0datWplFvxBgwZRt25d3Nzc6Ny5MwkJCUWWYQq7HBAQQO/evfH29qZBgwa4urqSkpLC7t272b17N927dyc4OJgzZ85w9uxZAD744ANzL/7SpUvmdBcXF/MYRW0Is7xr1y68vb3N54cjMnHiRADWrVtnZ0tqJ2vWrKFp06YMHDjQLvVX2x6+vaIjF1xJ3vK7KRyxlJLBgwcXuqhsvWBLaShtyGVTPo1Gk28fjUaDTqdDSsnLL79c6KnA6Oho9u7dy48//oiHhwcDBgwwh152dnY2/z41PcyylJJdu3YxePBgXFxc7G2OVdq0aUOfPn1Yu3YtL774or3NqVXcuHGDb775hjlz5pQqcGJloPbwy8jFixf58ccfASXEQmhoaKE8ffr04YcffjCHR87IyOCPP/6gY8eOxMfHc/78ecB6L2vQoEHmkLZ6vZ7U1NRiQxj369ePSGPUwz/++IOLFy/SoUOhh+grxJAhQ/j888/NYxGXL1/mr7/+IjU1FR8fHzw8PDhz5gw//fSTTeutLpw8eZJLly45rDvHkokTJ3Ls2DFOnDhhb1NqFZs3b0an01m9s68KVMEvIx06dODjjz+mU6dOJCcnm10vljRo0ICIiAgmTJhAt27duOuuuzhz5gxubm6sXLmS4cOHExwcTMOGDYus4/3332f//v0EBATQo0cPTp06VWz44yeffBKDwUBAQAAPP/wwERERNl9w4/7772fixIncddddBAQEMGbMGNLS0hg6dCg6nY5OnTrx0ksv0adPH5vWW1343//+BygNo6Mzbtw4tFqtOnhbxaxfv56OHTsSEBBgNxuEdNBFjkNCQuShQ4fypZ0+fZpOnTrZySJloPTBBx9Ue0Z2wt7/f3EMHTqUixcvcurUKXubUiqGDBnC2bNnOX/+fCE3pYrtuXLlCs2bN2fBggX5ZrhVBkKIw1LKkKK2qT18FZUKkp2dTWxsbJWsWGQrxo0bx59//slvv/1mb1NqBRs3bkRKaXVmXlWhCn4Z8Pf3V3v3KoX48ccfycrKYvDgwfY2pdSEh4ej1WrVh/eqiPXr1xMYGJhvpTl7oAq+ikoF2bNnD1qtlgEDBtjblFLj5+fHvffea+55qlQeCQkJ/Pjjj3bv3YMq+CoqFWbv3r306dPHLk9OVoQxY8Zw7tw5jh8/bm9TajQbNmwAUAVfRaW6c+vWLQ4dOlSt3DkmRo4ciUajUWPrVDJfffUVPXv2pE2bNvY2xTaCL4QYKoT4XQhxTghRKAKYEGKaEOKGECLO+HrUFvWqqNib/fv3I6WsVgO2Jho2bMg999yjunUqkbNnz3LkyBHGjx9vb1MAGzxpK4TQAh8Dg4FE4FchxHYpZcH5aeullE9XtD5Lom7c4Hpurs3Ka+TiwqgGDaxuT0lJYe3atTz55JPFlhMfH8/BgwfNj7EXl89RpnlGRERw//3307Rp02Lzvfbaa/Tv379YgYuOjsbFxYW7777b1mY6HHv27MHb25tevXrZ25RyMWbMGJ566ilOnTpFly5d7myIjIT585VFzlu2VJZCVBdMKTPr168HsPti9iZs0cPvBZyTUl6QUuYCXwHhNii3RK7n5tLc1dVmr5Iaj4Ihiq0RHx9frR5q0ev1REREcOXKlRLzvvHGGyX2ZqOjozl48KCtzHNo9uzZw4ABA3B2dra3KeVi5MiRCCHyz9aJjISZMyEhAaRU3mfOVNJVysRXX31FaGhokQEL7YEtBL8ZcMnie6IxrSCjhRDHhBCbhBBFHr0QYqYQ4pAQ4tCNGzdsYJptKRiiWErJ3Llz6dq1KwEBAebW/KWXXuL7778nKCiId999l/j4ePr160dwcDDBwcEliqHlwigATz/9NBEREYAyNXTBggUEBwcTEBDAmTNnACU0rylEcrdu3di8eTOA1TDNluGc161bx6FDh5g0aRJBQUFkZWXxxhtv0LNnT7p27crMmTPNt/zTpk0zi0NRtsTHx7NixQreffddgoKC+P7772ndujV5eXkA3L59O9/36syFCxe4cOFCtfTfm2jSpAmhoaH5/fjz54Mx7LeZzEwlXaXUnD59mpMnTzJu3Dh7m2KmqgZtdwD+UspuwB5gdVGZpJQrpZQhUsqQBsW4VuzF0qVLadu2LXFxcSxbtoyoqCji4uI4evQoe/fuZe7cuVy9epWlS5fSr18/JdzwlCk0vH6dPUuXciQigvUrVvDss89WyA4/Pz+OHDnCE088wfLlywF48803qVu3LsePH+fYsWMMHDiQmzdvsmjRIvbu3cuRI0cICQnhX//6l7kcUzjnyZMnExISQmRkJHFxcbi7u/P000/z66+/cuLECbKysvj6669LZYu/vz+zZs1izpw5xMXF0a9fPwYMGMDOnTsBpcczatSoatsjtmTv3r0A1VrwQXHrnDx50hxhlYsXi85oLV2lSEzrXYwaNcrOltzBFoJ/GbDssTc3ppmRUiZJKXOMXz8FetigXrtz4MABJkyYgFarpVGjRtxzzz38+uuvdzIkJUFCAnlZWTy2eDEBo0Yx9pFHKvz4vekEsgw5vHfvXp566ilzHh8fH6thmk0UN01s//799O7dm4CAAPbt28fJkydLbUtBHn30UVatWgXAqlWrmD59eqmP1ZHZv38/TZs2tXmguqomPFzxwG7btk1JsBYnX42fXyY2b95Mnz59aNasKIeHfbCF4P8KtBNCtBZCuADjge2WGYQQTSy+hgGnbVCv43P5MhgMvLt2LY3q1+fo2rUcWr2a3BLGCpycnDAYDObvpnDDJkyB0UoKOWwK0xwXF0dcXBynTp3Kt4i1KZxzQbKzs3nyySfZtGkTx48f57HHHitkQ1ls6du3L/Hx8URHR6PX680rdVVnpJTExMRwzz33VPtYNK1atSIoKOiO4C9eDB4e+TN5eCjpKqXCFLbCHssYFkeFBV9KqQOeBv6HIuQbpJQnhRBvCCHCjNmeFUKcFEIcBZ4FplW0XntQMERxv379WL9+PXq9nhs3bhAbG0uvXr3u5DMKe2p6Ok38/NBoNHyxa1eJa962atWKU6dOkZOTQ0pKCts7B9kAACAASURBVN99912Jtg0ePJiPP/7Y/D05OdlqmOaSjs0k7n5+fqSnp5f58fuiQjlPmTKFiRMn1pje/fnz57l69Sr33HOPvU2xCSNGjODgwYPKMpSTJsHKldCqFQihvK9cqc7SKQNbtmwBHMudAzby4Uspd0kp20sp20opFxvTXpNSbjd+fllK2UVKGSilvFdKecYW9TZycSExJ8dmr0YlLFxRMETxyJEj6datG4GBgQwcOJC3336bxo0b061bN7RaLYGTJvHu2rU8OWYMq3fuJHDiRM4kJODp7l5sPS1atGDcuHF07dqVcePG0b179xJ/i1dffZXk5GS6du1KYGAg+/fvtxqmuSimTZvGrFmzCAoKwtXVlccee4yuXbsyZMgQevbsWWL9ljz00ENs2bLFPGgLygLaycnJTJgwoUxlOSoxMTEA9O/f386W2IYRI0YgpWTHjh1KwqRJEB8PBoPyrop9mYiKiiIoKMghHrayRA2PXJkYffhYuGfQaJQek6+v/eyyA5s2bWLbtm188cUX5S7Dkf7/qVOn8s0333D9+vVq79IBxUXVunVrAgIC7oi+Srm4evUqzZo14/XXX+cf//hHlddfXHjkarvEYbXAJOqXLyvuHRcXaNas1on9M888wzfffMOuXbvsbYrNiImJoX///jVC7EFZqnPEiBGsWLGC9PR0vLy87G1StWXr1q1IKR3OnQNqLJ3Kx9cXunWDkBDlvZaJPcCHH37IuXPnaN++vb1NsQkJCQkkJCTUGHeOiREjRpCTk2NevUulfERFRdGhQwc6d+5sb1MKoQq+ikoZiY2NBagxA7YmQkNDqV+/Ptveew/8/RX3o7+/+oRtGUhKSmL//v2MGjXKIe/+VMFXUSkjsbGx1KtXr0ZML7XEycmJBzt35usDB8izDKsweTL4+anCXwp27NiBXq93SHcOqIKvolJmYmJi6NevH1qt1t6m2JwRv/9OMvB9wQ1JSWo8nVIQFRVFy5Yt6dHDMZ8tVQW/kjEYDNy+fZvr169z5coVrl+/zu3bt/M9WKVSfbh69Spnz56tcf57E/ffuIEbsK2ojWo8nWJJS0tj9+7dDuvOgWo+SycqCq5ft115jRqBre7E8vLyuHr1KklJSUU+aKXVagkLC+Pnn38uNiSxv78/hw4dws/PzzaGlZPShjzevn07p06d4qWXCi2LYKa04aMdkZrqvzfh2aoVgxMS2Aq8BxSSLYvQHCr52bVrFzk5OQ73dK0l1Vrwr1+H5s1tV15iYsXLkFJy8+ZNEhMTMRgM+Pj4UL9+fTw9PXFyckKn05GZmWluCE6fPo2zszN+fn4O2yvQ6XRER0fj5eVVouCHhYURFhZWbB5T+OjqKvheXl6lehiuWrJ4MSNmzGBHbi5xQKGjFEJx66gPYhUiKiqKRo0acdddd9nbFKuoLp0yEB8fT8eOHZk2bRrt27dn0qRJ7N27l759+9KuXTt++ukn/vzzT44dO8bcuXOZNm0aEydO5OLFizg7O3Pr1i2GDx/O3XffzZIlS3BycsLT05OEhATee+89evXqRVBQEI8//niJ4Rcs50lv2rSJadOmAcoTs88++yx33303bdq0yRcW4a233iIgIIDAwEBzD/z8+fMMHTqUHj160K9fP/OTuKYnb3v37s24ceMKhTzesWMHvXv3pnv37tx3333KI/koC6k8/fTTxdpSMHx0//79iYuLM9sZGhrK0aNHK/hvVQ6xsbH07dsXJ6dq3VeyzqRJPPjuuwhga1HbpVTdOkWQlZXFzp07GTFihEOP7aiCX0bOnTvH888/z5kzZzhz5gxr167lwIEDvP3227z66qvcunWLyMhIQkNDOX78OEuWLGHKlCkAvP7664SGhnLy5ElGjhzJpUuXaNu2LRkZGURFRbFq1SqOHDmCVqslsgKDY1evXuXAgQN8/fXXZmH/5ptv2LZtGz///DNHjx5l3rx5AMycOZMPP/yQw4cPs3z58nyreSUmJnLw4EGioqIKhTwODQ3lp59+4rfffmP8+PG8/fbbpbYlX/joOXP4v//7P3O8/z/++IPs7GwCAwPLffyVxc2bNzlx4kSN9d+baPjkk/QNDS3ajw9qmOQi2LNnDxkZGQ7tzoFq7tKxB6bHzwG6dOnCoEGDkFLi7e3NxYsX8ff357fffuPVV18FYODAgSQlJXH79m1iY2PNMbKHDx+Oj48PQgiOHz/O2bNnGTlyJFqtFoPBQMOGDctt44gRI9BoNHTu3Nnc8967dy/Tp0/HwxgFsX79+qSnp3Pw4MF8y6/l5OSYP48dO9ZqbyUxMZGHH36Yq1evkpubS+vWrUttS0HGjh3Lm2++ybJly/j888/NdyuOxoEDB4Ca67+3JDw8nLkHDhAP+BfcqIZJLkRUVBT16tVjwIAB9jalWFTBLyOmcMAAGo0GFxcXEhISyMzMRKvVlmtwVUrJ9OnTee6550hISMDPz49WrVoVu4+lv99a+GRT2dYwGAzUq1cvnzvFEmvhk0EJl/Dcc88RFhZGdHQ0CxcuLDJfaWzx8PBg8ODBbNu2jQ0bNnD48GGr9dqTmJgY3NzcCAkpMkxJjSI8PJy5c+ey3dmZZy1XJ1PDJBciLy+P7du3ExYW5vAL+6gunQqSkpJCUlISjRo1MveG+/XrZ3bJREdH4+fnR506dejfv795rdtvvvmG5ORkAAYNGsSmTZuQUtK4cWPOnz/PkSNHiq23UaNGnD59GoPBYA7FWhyDBw9m1apVZBqXrrt16xZ16tShdevW5uXtpJRWfecFQx6npqaaF3ZYvbrIBcysUlT45EcffZRnn32Wnj174uPjU6byqorY2FjuuuuufI1YTaVdu3Z06tSJbe3aqWGSSyA6Oprk5GSHd+dANe/hN2pkm5k1luWVhby8PG7dukX9+vXz9bgXLlzIjBkz6NatGx4eHmZBXLBgARMmTKBLly7cfffdtDTeGnfu3JlFixZx//33YzAYMBgMvPDCC3Ts2NFq3UuXLuXBBx+kQYMGhISEmNeqtcbQoUOJi4sjJCQEFxcXHnjgAZYsWUJkZCRPPPEEixYtIi8vj/HjxxfpP3/ooYcYM2YM27Zt48MPP2ThwoWMHTsWHx8fBg4cyJ9//lnq380cPjowkGnTpjFnzhx69OhBnTp1HDZefmpqKnFxcXaJfmgvwsPDWbZsGck3bjhsI+wIREVF4enpWT2WupRSOuSrR48esiCnTp0qlGYv8vLy5NGjR+WxY8ekTqezedlxcXHy+PHjNi/bUbl8+bJs166d1Ov1VvPY8//fuXOnBOS+ffvsZkOV8uWX8sfGjSUgv/T1lfLLL+1tkUOi1+tl48aN5dixY+1tihngkLSiq6pLp5xcunSJvLw82rRpY/NpWE5OTrRu3Zrs7GwuX75c8g7VnDVr1tC7d28WL16MRuOYp2RMTAzOzs707t3b3qZUPpGRMHMmva5dozGwTQ2rYJWffvqJa9euMXLkSHubUioc8+pycG7fvk1SUhKNGzcudmCzItSpU4eGDRvy119/kZGRUSl1OApTpkzh0qVL+WYLORqxsbH07NnTPMupRjN/PmRmogEeAr4FctSwCkWyZcsWnJ2deeCBB+xtSqmodoIv7bxCl8FgICEhAVdXV5o0aVLyDhWgWbNmODs7k5CQYPfjtjf2PP6MjAwOHTpUK6ZjAvnm2YcBaUB0gXQV5ZyMiorivvvuo27duvY2p1RUK8F3c3MjKSnJrhf/1atXycnJoVWrVpXuftBqtbRo0YLMzExu3LhRqXU5MlJKkpKScHNzs0v9P/74IzqdrsY/cGXGYp79IMADYzA1df59Po4fP86FCxeqjTsHqtksnebNm5OYmGg38dPpdFy5cgUPDw8uX75cZf719PR04uLiaNasmcP6uCsbNzc3mtsycFIZiImJQaPR0LdvX7vUX+UsXqz47DMzcQeGANuF4ONFiwoHU6vFREVFIYQgPDzc3qaUmmol+M7Ozlaf6KwKHnnkETZt2sTvv/9unlJZFWRlZdGjRw9efvlllixZUmX1qijExsYSHByMt7e3vU2pGkzz7OfPh4sXCa9fny1JSRzu2JGa/8hZ6dmyZQuhoaEVeiq+qqmd3cVycPjwYb788ktmz55dpWIPEBwczMSJE3nvvfeUu4rISHUJuioiOzubn3/+ufb4701MmgTx8WAwMPzMGTQaDdu2WY2uU+s4f/48x44dq1buHFAFv9TMmzcPPz+/YuO8VyaLFi1Cr9ezYNIk5Xbbcgk6dcpcpfHLL7+Qk5NTe/z3ReDn50doaKgq+BaYnm5XBb8GEhsby759+5g/f77dRuNbt27NrFmziIiJId4YHsGMOmWu0oiJiUEIQb9+/extil0JDw/n+PHjZXqiuiazZcsWunfvjr+/v71NKROq4JeCN954g0aNGvH444/b1Y558+ahBZYWtVGdMlcpxMbGEhAQUOtDC5gGJtVevjJT7+DBgw67UHlxqIJfAj/88APfffcd8+bNw93d3a62NGvWjBleXqwCCoUQUqfM2Zy8vDwOHjxY+/z3RdC2bVu6dOmiCj53Gr3q5s4BVfBL5I033qBBgwZ2792bePHNNzEAyywT1ZC1lcLhw4fJzMys1f57S8LDw/n++++5deuWvU2xK1u2bKFdu3Z07tzZ3qaUGVXwi+Hnn39m9+7dzJ07t9JCKJQV/9mzeaR/f1YKwU1QQ9ZWIqYFy1XBVwgPD0ev17Nz5057m2I3kpOT2bdvH6NGjXLYNaiLQxX8Yli+fDn16tXjiSeesLcp+Xjh3/8mW0r+s2iRMnVOFftKISYmho4dO1aredaVSUhICE2aNKnVbp2dO3ei0+mqpTsHVMG3yp9//mley9VywXBHoHPnzgwZMoSPPvoo35KEKrZDr9dz4MAB1X9vgUajISwsjG+//bbQKmu1haioKJo1a0bPnj3tbUq5UAXfCh988AEajYann37a3qYUyXPPPce1a9dYv369vU2pkRw9epTbt2+r7pwChIeHk5GRwb59++xtSpWTmZnJt99+a16nuTpSPa2uZFJTU/n0008ZP368eRk/R2Pw4MF06dKFd999t9ZH0qwMVP990QwcOBAvL69a6db53//+R1ZWVrWcjmlCFfwi+PTTT0lPT2fOnDn2NsUqQghmz55NXFwcMTEx9janxhETE0ObNm3sFrDNUXF1dWXo0KFs374dg8Fgb3OqlC1btlC/fv1q3QlQBb8AOp2O999/nwEDBhAcHGxvc4pl8uTJ+Pr68vHHH9vblBqFwWDg+++/r9YXdmUSHh7OtWvX+PXXX+1tSpWRl5fHjh07eOihh3ByqlYxJ/OhCn4Btm3bxqVLlxy6d2/Czc2N6dOns3XrVq5du2Zvc2oMp06dIikpSR2wtcIDGRlogW19+tSa4H3fffcdKSkpjB492t6mVAhV8AuwYsUKWrZsyfDhw+1tSqmYOXMmOp2Ozz//3N6m1BhU/30xREZS/7nn6I9xUZRaErxv48aN1KlTh/vvv9/eplQIVfAtOHv2LHv37mXmzJk2X5i8smj3yy8McnNj5fz56Fu1qvEXXlUQExND8+bN7br2gsNiXO82HDgFnIMaH7wvLy+PLVu2EBYWhqurq73NqRA2EXwhxFAhxO9CiHNCiELxg4UQrkKI9cbtPwsh/G1Rr61ZuXIlTk5OzJgxw96mlI7ISJg5k8ezs0kA/nfxYq3obVUmUkpiY2Pp379/tXySstJJSADAtMbTNsv0GnrefffddyQnJzN27Fh7m1JhKiz4Qggt8DEwDOgMTBBCFAwy8X9AspTyb8C7wFsVrdfWZGdns2rVKkaMGFHpi5PbDIveViPgP1Dje1uVzdmzZ7l27Zrqv7eG8c7XH+iGheBDje1sbNy4EW9v72rvzgHb9PB7AeeklBeklLnAV9zpAJgIB1YbP28CBgkH6z5t3ryZpKQkZs2aZW9TSo8xJLILMAP4Grhkka5SdlT/fQno9eaP4cAPoMR0ghrZ2cjLy2Pr1q2EhYXh5uZmb3MqjC0EvxlGnTGSaEwrMo+UUgekAr4FCxJCzBRCHBJCHKrqhcpXrFhBu3btuPfee6u03gphERL5McAArCqQrlI2YmJiaNiwIR06dLC3KY5Jq1bmj+Eo59zXlttrWGdj37593Lp1q0a4c8DBBm2llCullCFSypAGDRpUWb0nTpzgwIEDPP7449XrkenFi5XQyEBrYCCwWgjkokV2Nas6o/rvS8DinAsGWqHcspupYZ0NkztnyJAh9jbFJthC3S4DLSy+NzemFZlHCOEE1AWSbFC3Tfj0009xcXFh6tSp9jalbEyapIRGbtUKhGCary8XpOSARS9MpfTEx8dz8eJF1X9fHKZzztcXAYwFdgMpUOPWZTDNznnooYdqhDsHbCP4vwLthBCthRAuwHhge4E82wGTmo4B9kkHCQCTm5tLZGQkI0aMwM/Pz97mlJ1Jk5QQyQYDoxIS8PLyIiIiwt5WVUtU/30pmTQJbt6EL79kbOPG5AHbfH1r3LoM+/fvr1HuHLCB4Bt98k8D/wNOAxuklCeFEG8IIcKM2T4DfIUQ54DngEJTN+3Fzp07uXnzJtOmTbO3KRXG09OTcePGsWHDBjIyMuxtTrUjJiYGHx8funbtam9TqgeTJtHzyhVatWrFxj59apTYg+LO8fLyqjHuHLCRD19KuUtK2V5K2VZKudiY9pqUcrvxc7aUcqyU8m9Syl5Sygu2qNcWRERE0KRJEwYPHmxvU2zCtGnTSE9PJyoqyt6mVDtiY2Pp169f9RrHsTNCCMZ06cLunTtJEaLGhFqwdOfYey1rW1Krz+zr16+zc+dOHnnkkWodEMmS0NBQ2rRpo7p1ysiVK1c4d+6c6r8vK5GRjP3uO/Iw+nFrSKiFPXv2kJSUxIQJE+xtik2p1YK/du1a9Hp99RusLQYhBNOmTWPfvn0kGJ+KVCkZ1X9fTubPp1dODi2BDaa0GjAfPzIykvr169codw7UYsGXUrJq1Sp69epVLVefL44pU6YAsGbNGjtbUn2IiYnBy8uLoKAge5tSvbh4sfBsHWN6dSU9PZ2tW7cyduxYXFxc7G2OTam1gv/bb79x/PjxGjFYW5BWrVoxYMAAIiMj1dWwSklMTAz9+vWrMa69KsM4734s3HHrWKRXR7Zv305mZiYTJ060tyk2p0YKfkpeHltv3OD7lBROpqeTkJ3NjdxcMvR6DEYBjIiIwNXVlfHjx9vZ2sph0qRJ/P777xw5csTepjg8f/31F6dPn1b99+XhgQdACHoBLYGNUHg+fmSkMpir0VSLQd3IyEhatGhBaGiovU2xOTWyO5NpMHAhK4sUnY5TBgMSxbdt6u266XREfPklocOHc83FhbTsbDw0Gty1Wtw1GjQ14CnL0aNH89RTT7F27Vp69Ohhb3McGtMSkargl5HISFi9GqREoDxg8yGQPH48PqYpmsaIrmRmKt9Ng7rgkNM4b9y4wf/+9z+ef/75KputlWswkKnXk2F8T87LI0dKQuvWtfkT3zVS8AE8tVp8nZ0LpUsp+X73btKSkwkYNYqYFMXrKMDcMHhpNNRzcqKeszP1tFq8nJxw12jML6dqMG3Px8eHBx54gK+++oq333672sT3twcxMTF4enqqDWNZMUZrNTER+BewaetWHvvssyLzAHcGdR1Q8Ddu3Iher2eSDW3TS0mmXk+mUdBv63Tc0ulIzssjRa8n12Aw6w+AsxDkSEnfunWxddezxgq+NYQQ/LB5M/UaNOCeQYPQFvDZSinJlZIUnY6/8vLINd0hKDsjpcRNo6GOVqs0CE5O1NFqcddqcTM2CG4OcpcwceJEtm7dSkxMDAMHDrS3OQ5LTEwMffv2xbmIDoJKMRQYmA0GOgJf3rrFY1byWNvXUYiMjKRr165069at1PvoDAayjK9MvZ4MvZ5bOh0pxleGXm/2MEhAC7hqNLhqNPg4OeFUhFZczsmx3UFZUOsEPz01lUN79zJ06tRCYg9Kg+AqBK7F9OLzDAZypSQxO5vzUqI3uoos7xI8jI1CHScn6jk54W1sENyMriM3jQZtJTcKDz74IN7e3qxdu1YVfCvcvHmTEydO1Lj51lVCy5bmBVFAOf8nA68CCQkJtGrVqlCefPs6GH/++ScHDx5kyZIl5jQpJTkWgp5lMHDbKOSpxleWwaC4XoyCLlAE3cWoA3W1WocJxlfrBP+nnTvJy8nhngosRuys0eCM4jYqCikleVKSaTCQkp3NuSIaBQA3jQZvrRZvY4NQR6vFw9gYuBobB9cKNAzu7u6MGjWKTevW8dGePbhduqRcaIsXO+TttD0wzb8fMGCAfQ2pjjzwAPz73/mSJqIIfuTcubyyYYNyrln68MFhgqxZinm2wcB7n36KEIL2Dz7Izps3SdXrSTdO9LAUdK0QuAiBi/H6rV+N7gxrneDHREXR2N+fdt27V1odwnRCgHmFoKLQSUmuwcBfubkkGgzkmXoIFicXKA2Dp1Zrbhy8jN9Nt4WuGg2uxhOwoCtpYuPGrM7M5JuLFxkJDj9oVtXExMTg7u5OSEiIvU2pfuzaVSipNRAKfLFlCy9LiTCdY/PnK26c+vWV7488oqRVQufD5JbNNhjIMYp5tsFAmk7Hbb2eNL2eNJ2ODIMBvZRohMBgMPDl6tV07NePND8/snU6XDUaGjk7O4R71lbUSME3GCAnU2BwVmaCmbh17RrHv/+esXPmOMQtlpMQOJViMDXP2BjczMvjam4ueQYDBuM201GYGgd3jQYPrRYvrRZPjYa+kZE0BNaCIvgAmZkYXnkFJk6sUSdzeYiJieHuu++ucQ/YVAlW/PCTgVk6Hb81bUrw9et37iqh3DN2TCKeYzCQazCQY+qdGwU83fjKML6bx924c21Y9sxNd9cmHYiLieHW5cvMWLCgyMkeVUluNmSlCWhq+7JrpODf/At+3OjOH25OeNUzUNdPUtdPcmDbDgwGA33DR9nbxDJhciGVhJQSndGddCsvj+tS0u/yZcajrHebirIQAYC4dIkVV67gqtHgYWwkTI2Fh/EOwtl4cTgLcedl/F4TGopbt25x7Ngx3njjDXubUj2x4p8fCzwDfHntGsFwR9jd3YucsaN/5RWujh5tFvQMo3CbZrWk6/VkW06eMGL67iwETsbz0k2jwUurLfP5+d26dXjWrUufYcPKtF95kRJysyAzXZCdIbh9S5B6U0PqTQ26PEgTWgztQWPjyXU1UvANBhAaqNdAkpstuJag4dIf8N26KPyaBXL65wAunZHU8ZPU9TXgWUfi5ilx95I4VR93XCGESZQt0rKaNWNiYiIfAFHAdIv0Zi4u6FFcSxl6Pak6HTpjo2Eac4D8M5RAudBcNBrcjBeYaSDa1ThLyTTo7WRsIJyEKPJV2YPWJfH9998jpVTn35cRU8dCvvkmzo8+isjNzbfdBxiOclf5FijnY2YmMjOzyGmGmkuX+DrpznpIpvPD2fjurdXi4+RUKXflTTdtovGbb/LTlStM8/TE/+uvuTJmjM3KNxggO1OQlS7ISofbSYqw376lwaADo/cWrQu4ukm86km0TpBcSZOYaqTgmxAacPUAVw/JrWvn+evSIQZNeJP6jSR5uZB0RcPVC1qkQSrPHEtwdYc6vgbq+hrw9jE2BJ4SF3flz6lunJ4/n+A5c2ibnc06FMHXu7igzcjgoUaNyGrWjNPz55f5JNcbL3qdlKTqdCTpdOY0g8X4A1jMXiri3dnYQDgbZzU4GxsLZ4s003cn452FkxBoUG7RNUKgtfhsTgfzd42VO5KYmBjc3Nzo1atXmX9XR0VKiQEwGBtt82eLND3K/2ew+Kw3jiflSkme0WWSa0zLKZhuMDoUBw7kb++8Q9/XXsMtORmA7Hr1cEtJYTqwFdiFsvZtceT6+NDM1bVyfpBiaLppE4HPPcd/s7LIAWZmZNDtuecAynw95OVCdobSW8+4LUhN0nA7SZCeqpx3QirnvLMLuLhJ6vpKm/feS0ONFnxLTv64GYSgS5/RCA24uCk/PPmkCXS5kJ6i4dY1DXrdHXHSaKGOjwHv+pI6vhIPb4mbh9IgOPJdwZUxY/D55RcejohgqZRcE4KGej2uxgvUIzGRwHKc5Fob9NBlASHKlpIMo0iZBMkkWIYi9jcNbhdINM93tvThCgubTY3Clr17adOjB9tTU9EY8zhpNAjI11BYfjd9xvjd8h0o9kEZS0tNIT7M78btpmOVBY5dosz3lpDvN9OZ8gF6gwG90QbLJ8ut2SULfDYdo9bi2LUWaW4aDZ7G38/U284aP569BcKTDOrenQcSE2kC/Jc7gp/r44NTRgbaAncETmlpNN20yaY969LQafFinLKy+BwIBLoDIiuLTosXF2mLqbeekwFZGYK0ZEHaLQ2pSRpys42ZJAiNwMVN4uImqd9QIhzoOc1aIfhSSk4c3EDLDn2p49us2LxOLuDkIvHwzp9uMJDPPQSYWwM3D4m3j6SOrwGvesodgaunkm7vh3KbbtpEy6++YqKULAE2ScnTen2+PE5ZWXQtRy+/ogghlBOwCm6dTI2ASVTTU1NJOHGCMS+8oLgojNty9HpzXoxp5m6BhUurYEi6soSoK3i0wjLN2KhYppkE3DLd5PZwL7DNEcZWTs+fT+BzzzE9K4ulQCLQ2N2dE0uW0PWVVwoJvjYvz6rIFkXTTZvotHgx7pcvl/sOFcD98mXigEPA+9z5vd0vXyb1piA7U5CZprhg0pKV3rqppy6lohUuruDuJfH2sSzZcQMW1grBv55wjKSrZ+k19Mlyl6HRgJuHIuIF0eVCeqqGW39p0OXm1y+POpI6PhLv+ga86kpcPZQ7A1cPKq0xsLwgpEaDRq+nCxAArENZj7IgLrdu2aWXVdlYE4dzv/6KlJKgvn1xV8NO2BTTOTTh9ddZcu0a/61Th7C33uLKmDEEkN5UHAAAIABJREFUP1n0Neh++XKpyja5YZyysoDy3aFKCXk5kNGkGR9fScQDmGKx/Vbd5hz82kXprWsVUXfE3np5qBWCf+LgRjRaJzr1GlEp5Vu7KzCdWCk3NPyVqMFg7FgLQArwrCPxrifx8lEaA1ND4Oohi5u+XywFLwhh0ZufALwCJACtCuwnoEy9rOpAceJw4sQJnFxcaB8cbE8TayxXxoyBMWMIHD2aFfHxBI8ahQZlsoBHYmKh/FnNir/zNmFyw1jiVIQbxmBQZsHkZN3pqacnC24nC9JTlAHTq3fNJXLz35kM1DPul+vizr4Jr+HbxHF76RWhxgu+NBg4+eNm2na7Dw/v+lVatxB3xgo8C9mlDPSYGwMdpukwgDI24F1P4lXXgKcPipvIXXk5u1r3ghR1QZgYjyL4XwEvFrG9tL2s6kJx4nDcz48OPXrgWoPWK3VEBk+ezPKZMzkaE0P3e+81u3ss/xeduzunS7lClrVz1P3yZc7+piU9RUN6iiAjTWDQG68TqUzgcHaFXnHrGbb1DeolJbLEoy5ZwCN1GyFT/yLFtzl7x73Gsb7jbHDkjkmNF/yLvx8kLfkKgya8aW9T8mE5cJy/MZBICfo8xU2U/JcyL9e8VSoP73rWk3jVlXjVU6aVurorM4mKE+3WQB8Ut05Rgl/aXlZ1wdpvkZOYyIUrVxj3/PNVbFHto8+wYdT182PnZ5/R/d57zb3w0vjgdXnKuFlOptJTz8oQ3PZrRt0bhe8QbtVtzp8nnXB2AWdXiU+Dwu6Xbj9sIPyLZ3HJzcIARGSmcLfQcG3iIl6rwSJvSY0X/BMHN+Ls6kH74Kp5oMIWCHHHTYR34e0GvXJ3cOu6husXLebzAvfXbY5vyqXC+2i0CGlgVJ06zEtN5birKwEWEfnK0suqLlhzH+z29cWQlERgv352sKp24eLmxpApU9j47rtc/fNPmrRuzZUxY7g8egy52Yqg52YLci5AVrogPVVDZqrS2dHlYZ6oLqUyU27bsIWM/+oZXHLv3CGY3DD1GhTvhrlvwxvm/fYA54A3pIH7NrxRo3v1ltQ4wZcSTG5rvS6X079spX2P4bi4FXSqVF80WuV5AVf3wif4vgmvEf7Zs/kuiBxndyLHfMih4HFk3r6OWNSB1zoOZtXlo9RNSiStYTMOPf0PEvuNxjkVXFyLdxtVF6y5D6ICAnD95Rfaq/HvKwWDQRm7ys0W5OYIQu6bwab3P2D1m58x8OG3yExT/OrSIj6INIDWSZmn7uRy5wGkgjNezt4/lm2ekvs2KG6Zsrhh6iXdafzfBxoBowHnpMKdgppKjRP8lBTo3d4FZzdnXFwzyM7YSfI1f3asdMPdS5k/7+6tvHt4S3Oam6f9p1DaAtOJX/CC+LPvWHyR3HshmngnZ04c/Rq9T3O+mPxffg4ch5QgvlEGk5HKDCJXj/y/l7uHxNntToPg7CpxduAQNNbcBz+++y5d+vTBWY2fUyb0etDlQF6uUAQ9R+mdZ6VDZpqGrDTIytCQk8Wdhx+EANmStt1G8uvutQQPfBXPOnVw9yz/jJdjfceVSuC7/bAh33WQ4emDV8YtjgPfAIsAFyDZt3n5DKmG1DjB12jgsWf07P9Fx/Wzx9Bodeh1DblwQkNmmkCfZ6XbKpT586YGIJ/QmdK8jGnG94qctJWJtQui2w8bCP/sWW7n5fAo8GdyIuPXP4O7lyyUXxoUH2pWuiAtWbm91ussev0FGgZ3T4mbF7h7GnD3MjYGruDsLHEyvbtU/V3DlTFj8vmHk65d49ITT3BfDVyguqxIqUwpzssT6HKNn3OF+X/P+v/2zj3Orapa/N+VTObZ95t2+qCISi0oUhChPIRerFwtUKGXUi/gDywFFRAQ1CovHazIj1v0ilARP4WOQCkiFNBiqYCg1VZAKCICpe102tIyndJ2JpPJY98/9jnJSeZkXsk0mZn1/XzyOefss3POzkmy1t5rrb12kxBpstuWJkn5kiQ1c1QEAiVQEoKSkI00qxrs/Z5tD336mZfy1ssP8++Xfs0xn13Qpi2Zwtmv196ZOt66Zy25jJK4bfTQhjriEiQWDHFbPEoVcCnWHLR6zvU5P8veQp8T+IMHwyVXxtl93y4ee/k0PnHyuZz+ZZsK2Q2TDO+3YVrN+4XwPrG9E6fMHtvJFtvftXWyKQkRqwwqPEqhoiqVl8ediVsxIJWiobyKgo0mXBvmbOyP/QFgWmuYs3/+FWYsvzntD+RGNYTKoL2JJK5iaAnbKeWxWMB5Xp4kCu5G7PXKKg3lTsRRWaVJOq9doVESgmAISkoMwZAd6udLUbzq5L8/og/Z7+Mx+4rFhHgURzlLUklHwkJrM7SE7YzQSFiIhIVoJPX9WAGeWq0hELRCPFhiv5OUiaV7jDvkKMZ96GjWrbqLaf/xFQKevAJuR8Q1Qw5tqOOMX14OpEasnanj5XP3XZcU9i5BE+etYCm/jkf5KiDDx/NYH4/KyaTPCXyX+jd+TzTSzNRPn5Ms84ZJDh7RuThbV0mklIOrGALpZfuFPTsDbHd6Q/bPlJ2ySq8SyFAUWcoqqqxw7K7wc22YQ4HPAQ8BP8ZOqe/oD5QNf8WQuXWOTEo47f8gwN7dqWOTwAoeUj1HNzOAiBU8peWGklJDaZk7yzFBqMwqh9IyCAYNgRIbxRQIQiBg85UEgradgSC8tOZ5Bg4dRvWhU4lFbblIz0yCM8bas03C7htnP5GARMLasBNx5zhuyxJxkq9o1ArtqGNGiUXSj6OttmeezKDgXV3Hg/sMgiX2GQVL7ByQQNBk/JZ6Nvb82NO/ziM/OZ9/rn2UqcelRl1eZ6pLaWs4zZnamTpeqpp2+7bh561h4oEgidv/we0jxuf6kbqESUAkbNMztDRbORFx953jlibbIY2H4nBt/tvQZwX+5ldWMGh4NeM/fGxO1/EqiSGdVBJghVi4SWjZnxoSJ7f7nS+2SZIJl3Y2BpJ1E/HsEl0CxjpsnV5yaYUzYavCmbTlHrtlbt1Kw4YhxzBhzxsMZB9zMTwO/Alwc0WWtoaZffcCzv75/B6JSRZxh/7QFeFijEcwxoTmVkjEhUQi4BGYqdwx4uYZcBUG1jdhDKxf/QKjJ57Imoe88fe2soh9r6sEXHOdKxRFUm02RpJtgwyhbtzyLBLYVWwmrahNTRFBAnY0aBUYBIL2uCRkf5OBAEVpVvTjo9O+wIhxH+XFx2/jY8fORhwtOySL03RIQx1HvLicV4+f006dzjtcdwB3AR/79NkM7oawd0dLkbCTKK3ZI7CTx6SXewR7JNxBT00M5RXWAjDooC43r1P0SYHfuPt9tr+1hmNP/2ryR3WgCZZg4+QHd63XlDQ7pSkHSaZYdX9MkTDJH9Xe3YFkmY1+8P9hLWFtcn8gexE+4Cw+4DA+YCD7GMB+BiT2M4D9DGzYR8WSrWx99XV2HfYJmwzKmWJuX85+mXXk9uRjdnv4QbBeNvukMmp1/Jwbtr9N0wf1fOSoqzNmUjo5chJuzpx0YZyZn837Hm8P2VU07vw5r4LoHn1rtqcEAkw/4xp+e+fFvPn3J/jo0bMA2DO8mqENbUOJBZKjzmx19mRxuDYPGEbl/t20UM4+BrKPgXyPQUQYyISP1PD6X0qsqatFnC3J49YwRFqE1rDYbQu0hoVYNv9fssG2g1Vemep0DRmZoHxiaha9W+6ae73H3nQrb2+Jk5r/mz/6pMB/5nePYhKxNHNObyHN7DS863941xHX4vQoIs1Ci6McIs3C5OdWMuTtN9jLYB5nMPUMpozBNDKUOsZjxb19tcbL4EXsqwNKSk1KKVRYRVASsuVuqJ3vNmSduyWlhpBbv8wpL4WSslR5sCQ3e/6m158FYNIU//z3Emg/26WSO1OOnc1zjyzib8tu4kdLF1Gxp4HtlaPZFhhELBEkQhlNVNFMpd22VvL+fe9y+eEPc/CeP9ASL02e2x8YyMYBx9BwSyUxJ2rIjSC6PbyVCCHiPiLuqXvbtitUZigrtyNm9/83aHiC0gqbp760PDW50SusyysNZa7gLi/+0VafFPi/f/xBBo36CKMmTC10Uw44rmM0VGYzeGZy1W+vYSi2p3QqMAu4Bjjd51qthNjHQG5avMkJwbOx1dEWoTWSmjTTGsGWZZRbxRNIRYJEUlEg7ZmtOiIQdBy7JSkl4N0vCRmP0zFVvvWtjxMqu591T3+MEmf5S3FNJa69P5Cy97tlEkg3p6TMK+65VL1U7z71faR6/SbtfNsRgd97SI440l6O6ciekw7Ouy87Oy/pM4jbUMtEXNJ9CXFxylPH6e/x+BoSHgdxNOUojrUKsRjEom0dyYn4WzQCh7hfaMYiWG1oAjs4/RTltFDFfsoDEcygKhIykFDM9o4HDE0QKjWM2fMOh/1rNQPZa0er7OMR9vI8TXxhwY8ZNma0Fd4eAd8XQrI7Q58T+Fu2bOHldS9y+H98pyjWrS02vDbPz2IduA/gL/BLiRIYXuU4uPNrXrBCIRUS6DogrTPSERgZ5dbBK0lHr7vvCpPM8kgY4tGAjR+Pwt6GDxMsOYoNfy4lHksJuGwmMMVBTMoR7ijCYNAqS1tuQ25dBRsqg4oBieRxMuoqZJXvCat/ytLwLlpp4SYiDKCFMiKU0UI5EapoopJmKmmmiibKJMLiX7xJqNTtQZeSsu01tWnuVVd8lqGJlPnnDeAbwIVlVYyd/ivsigL9kz4n8A866CDu+OVvWfPWIR1X7od4baGl2JmGD2I7WRUI4hHsPRmj7Pa6y7JE9OSb7e++wi+/dxKnX3Q3R0xPX7DD7QF7e60mkdGzTVjFkOwJeyJrjKf3m/QBuB/JDXf0+AWSPW9Izjb1O+e+J+lEznglRwoBx1/gU8frhHb9CiKkC/BganQTLEmPbgoG82umOOLF5Zwd/ianYDscrcCF7veAv0nNGGvm7CzeTo0BrgSqgB9Gmvhpt1rdd+hzAj8UCjH9MzNZu7OjcWL/ZPWc65l99wKCTq7mucA9wBPAkVNOZMR7G7s8Zb038G7Sfn9im3OuYAwEIH21+FyWOVEycWPpBTgNK/C/D1wADMcqRvF5xnuGdy2ixtupeRJ4GrgdCHXxOn2RfmK5UlxePX4O4YrByeOTgIOwvfwR723k9js2cP2yPdx+x4Y+I+wBNr76DCOrpzBo2NhCN6XfkhlLfxuwD/gmjir1McHGgqEujzJXz7me1tIK9gOXAx8F5ofK+9WM2mz0uR6+S6sz8zONLB20rP22bPU76Oj5nje+u23u4Xcu2/3SypP70vZ8cmKO3alqakzWCQJzsPHJ0lBHw/Y82LPF/0A6eHB+Z7O1xiSFg0nb+BGN7GfLm2s5fPoCGra5nlFscH7SPGLamkrIMKcEUiaRtHh9d99zrLQlM2Z+KnZu0Q+B84AZyWxqKSIVA9vteGSmW3jzE6fxkVeeJtQa5iqETRieGDSKp+bV9KkOTHfpkwK/agCM+0iUwSGPsPGp52ub9PnDBrLFU7sTfNxDIW1I6nf9gPfanvOZUQJp8d2B9G3yXpJe1ztRqG1Zql37x4xj4I7Un28uNnvgg4OGcuTJ0dRnw+ce3va3d85z3u56nkuOAjFp73ZvkBEz79rB3eNXnnuWRLyVU+aczEePjqbZyV07vUnYiJXM2a/xuJ3XkIi5ESkpe33McQ4n4ilHcSKR+oxpM2BJ6tvMJls7vXeSlRsp5EQGJfc9EUS9Uan4xdJ/D1gBXAK8Qtts4JX7G8mGX7qFTz3zSwR4DvgZhq8GgjSpsE/SJwX+wEEw5aRWxpb1wn/FAeDfN6SnDT4GmCzC0nFj+M6ktr2s4qVzNvWNr62mrLKS6WccQ6is5z+fN4TRJCDuOHu9zl03jYIb/uiGLMZa7eI3NnrJKpFoxCqX1ghEI7YsbbDkaJeMMY/NIR/IPls3FXXTs/HjqV54XRvHbAVwL9a0OB/4dcb5bBOrwD/dggDvYUcMHwJ+lIjT2o/y3XdETgJfRIZhU7JMAjYBc4wxbVSyiMSB15zDLcaYWbncV8kNv7TBJ02dytI//IE9u3YxZOTIArcwv7y0Zg1HTJ9OqKzsgNzP7Y2nHMAd2Pi6QTyOHXU4o5DUSMNz7CRUi7am5+OJtpIMfY02ZeTjgeSygGnZIZyDQEnXlEdmL9z95F6hPh2bqvg7wInYxH62nrRrd/dLqxDDLuXZCDyFjc6p7Ef57jsi1x7+t4BnjDGLRORbzrHv6nnGmE/keC8lj3jTBo9dsYJLb7yRX8XjvH/ccUz54Q/7zGLm2zZuZMemTcy65JJCNyWvBJ2QSUvu0USuAvEqDKtEUvuxmDMnwplc585qbU95nPKAfy88U+hfB7yAdbIeDMx0Pserx8/JmhY500RkgMuAZ4GlwMed8vZGCf2NXAX+GcDJzv5S7LP2E/hKkTJ2xYqkeWcq8MiePVx56aVMXbiQDTU1vV7wv/zHPwLwyVNOKXBLihtXgbRNh52b8hh2bfbe9d7R1QzcWY+RAIFEnAewpp2zsb3zqUPHc/CTK5i14nLKoik7/ax7Lmdfo/DoaTcwb8XXKYuGMdgZ478AFgLnO/doLa3gd2deT2sLbXwh/ZFcP/ZoY8x2Z38HdtUwP8pFZL2IrBWRM7NdTETmO/XW79q1K8emKZ3hsJqapC1/LjZtTh1Qtns3n7zsMj4/ciSnHnkkY1esKGQzu81La9YwZtIkxk6eXOim9BtcxVFeZdcV9iNcXc1zG17miZ07efln/0usooJB2JWoqrEx+r8853Oc+6cbksLepSwa5sLll3DIEXH+cuNido0ex4XYWPv/Ovgwrhk5DiPCnhHjefzin7DhhHOSSQmbPhAadwoN2wM0bBcatgu7twu7d0jyuGGb8P524f1tzqteaNgWYPcO+94P3hf27hb277GRgM37bPbbSBhaWxw/jOPA7yii70DTYQ9fRFYDY3xOpa14bYwxkj094ERjTL2ITAbWiMhrxph3MisZY5YASwCmTZtWZI+qb1JRX5/cPxf7pT6IDZdzwygrt27l41ddBdCrevzRSITXXniBU849t+PKSo+QbV3hNxamxIfXpzS6vp5nBg1i9v79XLtkCRuBW7ApQLwEEnGOv/lKfn355Vw4bBCb36vnvOuu47+uvpo/eUKYyoBPEyUTr2O9TX4gH6d6IuGsCpbMC2RTdceijmM9mnK+RyOeVB9xSYbyeqO2hNRx0tnu2rrouSisDgW+MWZGtnMi8p6IHGSM2S4iBwE7s1yj3tluFJFngSOBNgI/n+yNxzGRSKoN2OFMQATJ3BchAMl9AYKec+5xXyQ8bhyVW+2wezLwKWxuncy1F0rCYQ7rZSaef/71r7Q0N3PUqacWuin9lmzrCmf+jlyfkmtifD4eZyHw/7G/xwuxi/ZMAsLAP4CHwmF+96MfMXTUKG544AGOmpFVVLWhY8d6fvqb3kVwkpFaPgvgGKfM3d8Zi/RIQrdcbfiPY2dGL3K2j2VWEJGhQLMxJiIiI4DjgVtzvG+7jAyFmDtqFHEgbgwJY0gA0USCmDFEjSFmDHGgNZEg7imLJhLEIXnsfYn9PJ6FNjxq27OfbQJRpmLxUzp+dXoyCVxmD2wuNvfIv7AzFL14RwO9gXVPP02orIzDjz++0E3p12SuK9weromxBDsT93xs+oW7sHNFvIwDbgYO/ctfqBo0KI8tzh9u3iKCdJC2I70sGumZ8OFcBf4iYLmIXARsxk7aRESmAQuMMRcDhwF3i0gC27FeZIz5Z473bZdQIEB1eReyLXUC4yiNhDFWiUD61lEg7vl4Rl2vsml1ytsoGWfbkkgQda4RTSR8k0rZ1LltFY53ro93P5si2XvmmUQSCT753e9S1tjIHOAqbK/qpox7ZrPHFiPGGP62ahUfP+EEyquqCt0cpZNkdiqOAB7GpmBYD9RjQy0nO+fC1dU8U6TCvhjJSeAbYxqwadUzy9cDFzv7fwYOz+U+xYCIEMSadkId1s4vCR+F0pHC8Z6PJRK0Oool5pS7yiduDGWBAPGqKmhsZHQwyMlxGzFxIylFE62o4M/XXUe9YyZLG91kkK3v4k5M9prVJGPfVUpuube+73GW0U/dv//Njk2bmP21r3X6OSuFY+yKFRxWU5P1NzUQ+ExGWaYvQOmYPjnTtq8RECEg0jNfVm0tXH01NNvsohKPM7e0lK+0tvLSmDEc9d57mPHjCdTUcOJ55zHdo1gSGdu4T5l36yqamFPXNavFEonk+czyOFZZuMrJq8zizojGT+SvfvJJAMafdBLbIpGMXAcOGaOitFN4ZqySnkUj4CgZV1HZS0nqfEaZW56pyDLLksc+1+xIwfVmvKHBncUAWxxn/KlHHtmuf0BJISaLRi0006ZNM+vXry90M/o+kybB5s1pRY3Y+NrLr76a2267rRCt6jReU5vxbE854QRaWlp47m9/SytPbj3v9ZZn289UaEml5yqgjGOTpZ7xHBtSS3HEjEn6hmLeNrpt8Lwf/JVcNtqMxvyO7cP0rdMdCeFtZ2b6Ja/SOuQ3v+HkK68kEO/6oiQtQ4dS0tLSJgJo7W23sWX27NQ9M5SkZGwz8SrcbOey0ZnvpTN1dkSjLBg7Ntm56Aoi8ndjzDTfcyrw+zmBgO8wehbwcnU1mzdvJtDL1n/btWsXo0eP5oYbbuCGG24odHPyivGMSFxl4O4bz/nOHNPNOh29z9uepPLMUMwGGLJ8ORO+/nWCXejZpz0L/IVnc3U1f3z55bS2ubjKNvM6YBVsZhkZnyuznnu+vTZ2R8KWBwKcPXJkt0Z07Ql8Nen0dyZMaNPDB5g7YgQrt27lhRde4MQT2y4aUnTU1sLChbBlC08OG4Yxhi984QuFblXe8Zp5oJeHC3//+9BNYQ/Ze8qV9fX854gR3b5uX6Z3dd2U/FNTA5WV6WWVlcxatIjKykoeeOCBwrSrK9TWwvz5VnEZw8qGBsaJcOQ/ezQYTMmVLVu6/97KShg+3P/chAndv24fRwV+f2fePFiyBCZOtHbbiRNhyRKqLrqIWbNm8fDDDxNdutTa+gMBu62tLXSr01m4MOl0bgZ+D8wyBvnudwvaLKUDuiuYnd8od9zh21mhpib3tvVVjOMQKrbXUUcdZZTC8thjjxnArCwrMwZSr8pKY5YtK3TzUogk27bCMZs+A7ZcKV6WLbO/Je9vq72X3+9u2TJjJk603/XEicX1uywQwHqTRa6q01bJSjQapbq8nOMSCR7NPDlxImzaVIBW+eCJNDoXWANsA0qKqY2KPx7fC8OG2bKGBjs9NR5PbSdOtD33efMK295eQHtOWzXpKFkJhUJcmEiwEpsKNY1c7K/5xvFDhIEngLOAEh3a9w7mzbNKOZGA999PmWncMM14PGWmUWGfMyrwlXa5aOxY4tjFDtIoJseY44f4/ciRNAHnjBplbbwqIHofHn9MkuZmuOCC4vUh9SJU4Cvt8uFbb+VEEe7BE09cWlp8ved583h4xgyGDx/OyfX1Kux7K9lGjvG4teRv3mwjslTodwsV+EqHXBwI8DbwvFtQhH6fcDjMypUrOeussygp0eklvZbOjBybm+1IQOkyKvCVdGpr00Mwr7iCL8bjDAbucetEo0X3h1u1ahX79+/nnHPOKXRTlFzwmxfiRzH5kHoRKvCVFBkTmNi8GRoaqATOA1Zg8+wARfeHW7ZsGaNGjeIzn8nMqaj0KjLnhaRWak+nmHxIvQgV+EoKP4eZw8VAC7DMLTCmaBxojY2NrFy5krlz5xIKhdqOUoqgjUoX8EbuLF2qk6vyiAp8JUU7vfZPAtOAO/Ekj8p0oBVI0C5fvpzW1lbOP/98/1GKOvl6L1lmgqtTvnvoxCslhU+qZACqqqCpifuxS86tAk7znncnxcyfnz5CqKw8IH/O6dOn09jYyIYNG5CDD/b/DDoJS+kn6MQrpXNkSaSGs1zkHGAU8NPM923Zkj1+uoedu++88w4vvvgi559/vk0lm22UUmQ+B0UpBCrwlRTZhs+7dwNQBswHngTe8b5vwoSCCdr77rsPEWGeO4rI5sxTJ5+iqMBXMvA6zDZtssceYbkACAI/cwtE7MigAII2Fotxzz33cNppp1FdXW0Ls41S1MmnKCrwlU5QUwMhu3T7OOCLwL3AXoAFC6xS6EjQ9oBD94knnmDbtm1ceumlqUJ18ilKdrKl0Sz0S9MjFxnLlhkzfLgxYP7qpCC+9dxz29bxS1XrlwY3lxTLzn1OA1MdDJro0qW5fDJF6VOg6ZGVfDNjxgxef/113n33Xcodp25WskX/dCdyxgm7fKe5mQ8BNwHXH6BoIEXpDWiUjpJfamv59muvsWPHDpaOG9execZP2EP3HLpONNDdWF/CRaC5VRSlk6jAVzpPbS2MGAFf+hKn7NzJ0cCtu3cT+8pXsgv92lprS/ejOw7dLVvYBywBzsT6FNxyRVHaRwW+0jncGawNDQAI8B1gI/BQOGzzlYtASUn69oIL/LNrutE9XWXCBO4BPgC+mVGuKEr7qMBXOofPxKpZwFSsHT3qXaHIb5uJMd2yuUdvuon/EeFE4FNuoYZdKkqnUIGvdA4fk0kAuAV4Cxum2SUmTkztdyFk86FgkDpj+ObIkRp2qShdRKN0lM6RJdLGACdgTTtvA53IZJ6eY8c1FXUiB08sFmPKlCmUl5fzyiuvEAhof0VRMtEoHSV3amp8na8CLAK2A3e09/5g0L9H3oUcPEuXLuWtt97iBz/4gQp7RekG2sNXOs9ll8Fdd/k6YWcBa4B/AdWZJ0XsjNw772x7zWwRPCI2vYNDJBLh0EMP5aCDDmLt2rU2UZqiKG3QHr6SH+68E+6/P5W2wCN0FwNx4Cq/9xljF7LItM13IWRz8eLF1NXVUVNTo8JeUbqJCnyla7h5cyZMSOvpT8aGaT6MzZffhuZm+NKX0p2yCxd2KmSzrq6Om2++mTPOOIMZM2apxnQaAAAGzklEQVTk77MoSj+jpNANUHoZfk5Wh2uBWuzs19eAoX7vd1egguyTpTJCNr/xjW9gjGHx4sW5tV1R+jnaw1e6Rjvr3pZh17x9D7isvWu4Ttlsk6U8IZsrVqzgkUceYeHChUyaNKl7bVYUBVCBr3SVDlIYTANuBB4EftHRdTpIqVxfX8/8+fM5+uijufbaa7vfZkVRABX4SlfpRAqDbwGfxfbyn23vOu3krg+Hw5x99tm0trZSW1tLyMnHryhK91GBr3QNv155BkHgIeBQ4IsDBvCPzPTJ3lQIPitsxeNxvvzlL7N27Vruu+8+Dj300Px/DkXph+Qk8EXkHBF5XUQSIuIb9+nUmykib4rI2yLyrVzuqRQYv165D4OBJ4DKIUM4paSEdWPGdCoVQiwW44ILLuChhx5i0aJFzJ49u+c+i6L0M3Lt4W8AZgPPZ6sgIu4SqJ8DpgBzRWRKjvdVCklmr3z4cN9qk4cP57nnnmPQiBGc0NjI3T//OYmNG7MK+23btjFjxgxqa2u55ZZbuO6663ruMyhKPyQngW+MecMY82YH1Y4B3jbGbDTGtGL9eWfkcl+lyLjjDigtTS8rLYU77mDy5MmsW7eOE044gQULFnDcccfx6KOPEolEklW3/uQn3DRkCIeNG8e6559n6SWX8O1vf/sAfwhF6fscCBv+OKDOc7wVz7oVXkRkvoisF5H1u3btOgBNU/LCvHlw773pZp5770325EeMGMGqVav41a9+RV1dHbNnz2bQoEEccsghjB06lPFXXMGNH3zAqcBLxnD+/ffnZZFzRVHS6TCXjoisBsb4nFpojHnMqfMscI0xpk3yGxE5G5hpjLnYOf5v4FPGmK+1d1/NpdM3icVirF69mmeffZa6ujpKH3uMjzU1MQv4sLdid9a7VRSl3Vw6Hc60NcbkOpe9HhjvOa52ypR+SElJCTNnzmTmzJm2IFvWS12yUFHyzoEw6awDDhWRg0WkFDgXePwA3FfpDWSL69clCxUl7+QalnmWiGwFPg08KSKrnPKxIvIUgDEmBnwNm1PrDWC5Meb13Jqt9Bk6mG2rKEr+yCl5mjHmUeBRn/JtwOme46eAp3K5l9JH8S6EsmWL7dnX1OiShYrSA+hMW6XweOP6a2qs8O/E+raKonQNFfhK/unCouRt3jd/vk2hbEwqlbIKfUXJCyrwlfySi9Duwvq2iqJ0HRX4Sn7JRWhnC8XUEE1FyQsq8JX8kovQ1hBNRelRVOAr+SUXoa0hmorSo6jAV/JLLkK7nQVRFEXJHV3EXMkvucbVz5unAl5ReggV+Er+UaGtKEWJmnQURVH6CSrwFUVR+gkq8BVFUfoJKvAVRVH6CSrwFUVR+gkdLnFYKERkF7C5gE0YAbxfwPsXO/p82kefT/vo82mfXJ7PRGPMSL8TRSvwC42IrM+2LqSiz6cj9Pm0jz6f9ump56MmHUVRlH6CCnxFUZR+ggr87CwpdAOKHH0+7aPPp330+bRPjzwfteEriqL0E7SHryiK0k9Qga8oitJPUIHfCUTkahExIjKi0G0pJkTkxyLyLxF5VUQeFZEhhW5TMSAiM0XkTRF5W0S+Vej2FBMiMl5E/igi/xSR10XkikK3qRgRkaCIvCwiT+TzuirwO0BExgOnAbqwalv+AEw1xhwB/Bv4doHbU3BEJAj8DPgcMAWYKyJTCtuqoiIGXG2MmQIcC3xVn48vVwBv5PuiKvA75n+AawH1bmdgjHnaGBNzDtcC1YVsT5FwDPC2MWajMaYVeBA4o8BtKhqMMduNMS85+/uwQm1cYVtVXIhINfCfwD35vrYK/HYQkTOAemPMPwrdll7A/wN+V+hGFAHjgDrP8VZUoPkiIpOAI4G/FrYlRcdibCczke8L9/sVr0RkNTDG59RC4DtYc06/pb3nY4x5zKmzEDtUrz2QbVN6LyIyAHgEuNIYs7fQ7SkWROTzwE5jzN9F5OR8X7/fC3xjzAy/chE5HDgY+IeIgDVXvCQixxhjdhzAJhaUbM/HRUQuBD4PnGp0UgdAPTDec1ztlCkOIhLCCvtaY8xvCt2eIuN4YJaInA6UA4NEZJkx5kv5uLhOvOokIrIJmGaM0Qx/DiIyE7gdOMkYs6vQ7SkGRKQE68A+FSvo1wHnGWNeL2jDigSxvaelwG5jzJWFbk8x4/TwrzHGfD5f11QbvpIL/wsMBP4gIq+IyF2FblChcZzYXwNWYR2Sy1XYp3E88N/AKc5v5hWnN6scALSHryiK0k/QHr6iKEo/QQW+oihKP0EFvqIoSj9BBb6iKEo/QQW+oihKP0EFvqIoSj9BBb6iKEo/4f8AAq9cgV66bloAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[0.0827434] 0.1 0.042364288208821585\n" ] } ], "source": [ "device = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n", "x_dim, y_dim = x_train.shape[1], y_train.shape[1]\n", "# build a BNN, with hidden layer width = h_dim\n", "h_dim = 50\n", "layer_sizes = [x_dim, h_dim, h_dim, y_dim]\n", "# you can change the activation function here or define your own customized activation\n", "activation=nn.GELU()\n", "# you can change the prior parameters as you wish\n", "layer_kwargs = {'prior_weight_std': 1.0,\n", " 'prior_bias_std': 1.0,\n", " 'sqrt_width_scaling': False,\n", " 'init_std': 0.05,\n", " 'device': device}\n", "mfvi_regression_net = make_mfvi_bnn(layer_sizes, activation=activation, **layer_kwargs)\n", "# we assume a Gaussian likelihood with homogeneuous noise\n", "log_noise_var = nn.Parameter(torch.ones(size=(), device=device)*-3.0)\n", "# print out the BNN settings\n", "print(\"BNN architecture: \\n\", mfvi_regression_net)\n", "\n", "# plot the BNN prior in function space\n", "K = 50 # number of Monte Carlos samples used in test time\n", "x_test_norm = normalise_data(x_test, dataset.x_mean, dataset.x_std)\n", "x_test_norm = torch.tensor(x_test_norm, ).float().to(device)\n", "\n", "def to_numpy(x):\n", " return x.detach().cpu().numpy() # convert a torch tensor to a numpy array\n", "\n", "def get_regression_results(net, x, K, log_noise_var=None):\n", " y_pred = predict(net, x, K=K) # shape (K, N_test, y_dim)\n", " y_pred_mean = y_pred.mean(0)\n", " if log_noise_var is not None:\n", " # note here the preditive std needs to count for output noise variance\n", " y_pred_std = (y_pred.var(0) + torch.exp(log_noise_var)).sqrt()\n", " else:\n", " y_pred_std = y_pred.std(0)\n", " # unnormalise\n", " y_pred_mean = unnormalise_data(to_numpy(y_pred_mean), dataset.y_mean, dataset.y_std)\n", " y_pred_std = unnormalise_data(to_numpy(y_pred_std), 0.0, dataset.y_std)\n", " return y_pred_mean, y_pred_std\n", "\n", "# plot the BNN prior and ground truth\n", "def plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std, title=''):\n", " plt.plot(x_train, y_train, 'ro', label='data')\n", " plt.plot(x_test, y_test, 'k-', label='ground-truth')\n", " plt.plot(x_test, y_pred_mean, 'b-', label='prediction mean')\n", " # plot the uncertainty as +- 2 * std\n", " # first for the total uncertainty (model/epistemic + data/aleatoric)\n", " plt.fill_between(x_test[:,0], y_pred_mean[:,0]-2*y_pred_std[:,0], \n", " y_pred_mean[:,0]+2*y_pred_std[:,0], \n", " color='c', alpha=0.3, label='total uncertainty')\n", " # then for the model/epistemic uncertainty only\n", " plt.fill_between(x_test[:,0], y_pred_mean[:,0]-2*y_pred_std_noiseless[:,0], \n", " y_pred_mean[:,0]+2*y_pred_std_noiseless[:,0], \n", " color='b', alpha=0.3, label='model uncertainty')\n", " plt.legend()\n", " plt.title(title)\n", " plt.show()\n", "\n", "y_pred_mean, y_pred_std_noiseless = get_regression_results(mfvi_regression_net, x_test_norm, K)\n", "model_noise_std = unnormalise_data(to_numpy(torch.exp(0.5*log_noise_var)), 0.0, dataset.y_std)\n", "y_pred_std = np.sqrt(y_pred_std_noiseless ** 2 + model_noise_std**2)\n", "plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std,\n", " title='BNN init (before training, MFVI)')\n", "print(model_noise_std, noise_std, y_pred_std_noiseless.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "_iKDaUqiXuRi" }, "source": [ "Now define the data loss (i.e., negative log-likelihood) and train the BNN." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 642 }, "id": "z-rD6aj-TvLZ", "outputId": "7e1dd05d-83c2-49a1-dd48-5de1d17d8a15" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 100, nll=6.995061874389648, kl=6524.4951171875\n", "Epoch 200, nll=3.3041820526123047, kl=6280.9228515625\n", "Epoch 300, nll=1.7511743307113647, kl=6049.814453125\n", "Epoch 400, nll=1.5301305055618286, kl=5826.03369140625\n", "Epoch 500, nll=1.5045077800750732, kl=5606.9345703125\n", "Epoch 600, nll=1.0725151300430298, kl=5391.306640625\n", "Epoch 700, nll=1.069087266921997, kl=5179.81103515625\n", "Epoch 800, nll=1.5739126205444336, kl=4969.98583984375\n", "Epoch 900, nll=1.0806735754013062, kl=4763.2314453125\n", "Epoch 1000, nll=1.3580050468444824, kl=4557.697265625\n", "Epoch 1100, nll=0.8312104940414429, kl=4354.61328125\n", "Epoch 1200, nll=0.9971514940261841, kl=4155.3701171875\n", "Epoch 1300, nll=0.4093616008758545, kl=3960.3203125\n", "Epoch 1400, nll=0.9736379981040955, kl=3768.5576171875\n", "Epoch 1500, nll=0.5539526343345642, kl=3578.880615234375\n", "Epoch 1600, nll=0.44877609610557556, kl=3393.62109375\n", "Epoch 1700, nll=0.3569214642047882, kl=3213.7373046875\n", "Epoch 1800, nll=1.1329872608184814, kl=3039.186279296875\n", "Epoch 1900, nll=0.8704216480255127, kl=2870.02734375\n", "Epoch 2000, nll=0.6444283127784729, kl=2706.732177734375\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# define the training function\n", "def train_network(net, opt, dataloader, data_loss_func, learning_rate=1e-3, \n", " N_epochs=2000, beta=1.0, verbose=True):\n", " net.train()\n", " logs = []\n", " for i in range(N_epochs):\n", " nll, kl = train_step(net, opt, data_loss_func, dataloader, \n", " N_data=len(dataloader.dataset), beta=beta)\n", " logs.append([to_numpy(nll), to_numpy(kl)])\n", " if (i+1) % 100 == 0 and verbose:\n", " print(\"Epoch {}, nll={}, kl={}\".format(i+1, logs[-1][0], logs[-1][1]))\n", " return np.array(logs)\n", "\n", "# start training\n", "learning_rate = 1e-3\n", "params = list(mfvi_regression_net.parameters()) + [log_noise_var]\n", "opt = torch.optim.Adam(params, lr=learning_rate)\n", "# define the regression loss: negative gaussian log-likelihood\n", "data_loss_func = lambda y, y_pred: -gauss_loglik(y, y_pred, log_noise_var)\n", "# hyper-parameters of training\n", "beta = 1.0\n", "N_epochs = 2000\n", "# the training loop starts\n", "logs = train_network(mfvi_regression_net, opt, dataloader, data_loss_func, \n", " beta=beta, verbose=True, N_epochs=N_epochs)\n", "\n", "# plot the training curve\n", "def plot_training_loss(logs, beta):\n", " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n", " ax1.plot(np.arange(logs.shape[0]), logs[:, 0], 'r-', label='nll')\n", " ax2.plot(np.arange(logs.shape[0]), logs[:, 1], 'r-', label='KL')\n", " ax1.legend()\n", " ax2.legend()\n", " ax1.set_xlabel('epoch')\n", " ax2.set_xlabel('epoch')\n", " ax1.set_title('ELBO (beta={})'.format(beta))\n", " ax2.set_title('ELBO (beta={})'.format(beta))\n", " plt.show()\n", "\n", "plot_training_loss(logs, beta)" ] }, { "cell_type": "markdown", "metadata": { "id": "T2TDVt0-hPTi" }, "source": [ "Training is now finished, let's plot the predictions again, but now it shows the mean & std of Bayesian predictive inference." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 298 }, "id": "dDWVAvPYibn3", "outputId": "d134cd1b-7709-4dad-e9d4-6beacb309b0c" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[0.12110004] 0.1 0.1665010710437822\n" ] } ], "source": [ "y_pred_mean, y_pred_std_noiseless = get_regression_results(mfvi_regression_net, x_test_norm, K)\n", "model_noise_std = unnormalise_data(to_numpy(torch.exp(0.5*log_noise_var)), 0.0, dataset.y_std)\n", "y_pred_std = np.sqrt(y_pred_std_noiseless ** 2 + model_noise_std**2)\n", "plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std,\n", " title='BNN approx. posterior (MFVI)')\n", "print(model_noise_std, noise_std, y_pred_std_noiseless.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "7D07ALhSrpSu" }, "source": [ "**playground**\n", "\n", "You can test various hyper-parameter settings and see how they would affect the regression results.\n", "\n", "In particular, you can go back to previous code blocks and change the settings e.g,:\n", "\n", "1. Change the depth of the network, by changing the list of ```layer_sizes```;\n", "2. Activation functions: try e.g., ```nn.Tanh()``` or ```nn.ReLU()``` or the below ```Sine``` activation (copy-paste the code below to the place when you define the activation function);\n", "3. Training epochs: try setting different values for ```N_epochs``` when calling ```train_network()```, e.g., ```N_epochs = 1000``` vs ```N_epochs = 10000```, and see what happens to the training curves;\n", "4. Tempered ELBO: try using different values of ```beta```, e.g., ```beta = 0.1``` vs ```beta = 10```;\n", "5. Prior settings: try ```sqrt_width_scaling=True```;\n", "6. Initialisation of the q standard deviation ```init_std```.\n", "\n", "Come up with you own conclusions: which settings work well?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "x_ZNoqFvkl2-" }, "outputs": [], "source": [ "# copy-paste below code to the appropriate place, and call it by\n", "# activation = Sine()\n", "class Sine(nn.Module):\n", " def __init__(self):\n", " super(Sine, self).__init__()\n", " def forward(self, x):\n", " return torch.sin(x)" ] }, { "cell_type": "markdown", "metadata": { "id": "r4k3n8ihJ1uO" }, "source": [ "# Using other q distributions?" ] }, { "cell_type": "markdown", "metadata": { "id": "f9GetssjlunI" }, "source": [ "Hopefully from above tests, you can see that MFVI works but it needs careful settings of the hyper-parameters (i.e., tricks that are mostly hidden in paper appendices). \n", "\n", "A natural question is to ask whether other design of the q distribution would return better results. Within the Gaussian family, Gaussians with full-rank covariance matrices are more expressive than factorised Gaussians. But at the same time, it requires much more variational parameters if we were to use full covariance Gaussians for every layer. For example, a hidden layer with ```dim_in = dim_out = 50``` would need $50 \\times 50 = 2500$ parameters for parameterising the mean, but $\\sum_{i=1}^{50\\times 50} i = 3126250$ parameters for parameterising the (symmetric) full covariance matrix! Therefore, when selecting the q distribution family, one needs to also consider the computational & memory costs for such approximation.\n", "\n", "In below we will implement 2 \"economic\" solutions:\n", "\n", "1. The so-called \"last-layer BNN\" approach: only apply full-covariance Gaussian posterior approximation to the last layer of the network, and use MLE/MAP solutions for the previous layers. \n", "2. Monte Carlo dropout (MC-dropout). \n", "\n", "For mathematical foundations, see [lecture notes here](http://yingzhenli.net/home/pdf/ProbAI2022_lecture_note.pdf)." ] }, { "cell_type": "markdown", "metadata": { "id": "ZztkJAkyNZUV" }, "source": [ "## Last-layer BNN with full-covariace Gaussian approximation" ] }, { "cell_type": "markdown", "metadata": { "id": "zEmpAu4gWUGI" }, "source": [ "For the \"last-layer BNN\" method we need to use a full-covariance Gaussian posterior approximation for the output layer of the network.\n", "\n", "In below block you will implment a BNN layer with variational inference and Gaussian q with **full covariance matrix**, and more specifically:\n", "\n", "* the initialisation of the variational parameters\n", "* the forward pass with Monte Carlo\n", "* the $KL[q||p]$ regularisation term for one layer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Hhi3VIdSNQ8r" }, "outputs": [], "source": [ "class FullCovGaussianLinear(nn.Module):\n", " \"\"\"Applies a linear transformation to the incoming data: y = xW^T + b, where \n", " the weight W and bias b are sampled from the q distribution.\n", " \"\"\"\n", "\n", " def __init__(self, dim_in, dim_out, prior_weight_std=1.0, prior_bias_std=1.0, init_std=0.5,\n", " sqrt_width_scaling=False, device=None, dtype=None):\n", " factory_kwargs = {'device': device, 'dtype': dtype}\n", " super(FullCovGaussianLinear, self).__init__()\n", " self.dim_in = dim_in # dimension of network layer input\n", " self.dim_out = dim_out # dimension of network layer output\n", "\n", " # define the trainable variational parameters for q distribtuion\n", " # first define and initialise the mean parameters\n", " self.weight_mean = nn.Parameter(torch.empty((dim_out, dim_in), **factory_kwargs))\n", " self.bias_mean = nn.Parameter(torch.empty(dim_out, **factory_kwargs))\n", " # then define and initialise the parameters for covariance matrix\n", " num_params = dim_out * dim_in + dim_out # total number of parameters\n", " self._cov_diag = nn.Parameter(torch.empty((num_params,), **factory_kwargs))\n", " self._cov_tril = nn.Parameter(torch.empty((num_params, num_params), **factory_kwargs))\n", " self.reset_parameters()\n", " \n", " # define the prior parameters (for prior p, assume the mean is 0)\n", " prior_mean = 0.0\n", " if sqrt_width_scaling: # prior variance scales as 1/dim_in\n", " prior_weight_std /= self.dim_in ** 0.5\n", " self.prior_weight_std = prior_weight_std\n", " self.prior_bias_std = prior_bias_std\n", " # prior parameters are registered as constants\n", " self.register_buffer(\"prior_mean\", torch.full((num_params,), prior_mean, **factory_kwargs))\n", " prior_weight_std = torch.full_like(self.weight_mean, prior_weight_std)\n", " prior_bias_std = torch.full_like(self.bias_mean, prior_bias_std)\n", " prior_std_diag = torch.concat((prior_weight_std.flatten(), prior_bias_std.flatten()))\n", " self.register_buffer('prior_scale_tril', prior_std_diag.diag_embed())\n", "\n", " def extra_repr(self):\n", " s = \"dim_in={}, dim_in={}, bias=True\".format(self.dim_in, self.dim_out)\n", " s += f\", weight prior std={self.prior_weight_std:.2f}\"\n", " s += f\", bias prior std={self.prior_bias_std:.2f}\"\n", " return s\n", "\n", " def reset_parameters(self, init_std=0.5):\n", " nn.init.kaiming_uniform_(self.weight_mean, a=math.sqrt(5))\n", " bound = self.dim_in ** -0.5\n", " nn.init.uniform_(self.bias_mean, -bound, bound)\n", " _init_std_param = math.log(math.expm1(init_std))\n", " self._cov_diag.data = torch.full_like(self._cov_diag.data, _init_std_param)\n", " self._cov_tril.data = torch.full_like(self._cov_tril.data, 0.0)\n", "\n", " # define the q distribution standard deviations with property decorator\n", " @property\n", " def mean(self):\n", " # flatten the weight matrix into a vector\n", " return torch.concat((self.weight_mean.flatten(), self.bias_mean.flatten()))\n", "\n", " @property\n", " def scale_tril(self):\n", " # this returns the cholesky decomposition L of the covariance: Cov = LL^T\n", " return F.softplus(self._cov_diag).diagflat() + torch.tril(self._cov_tril, diagonal=-1)\n", "\n", " # KL divergence KL[q||p] between two Gaussians\n", " def kl_divergence(self):\n", " q = dist.MultivariateNormal(self.mean, scale_tril=self.scale_tril)\n", " p = dist.MultivariateNormal(self.prior_mean, scale_tril=self.prior_scale_tril)\n", " kl = dist.kl_divergence(q, p).sum()\n", " return kl\n", "\n", " # forward pass with Monte Carlo (MC) sampling\n", " def forward(self, input):\n", " weight, bias = self._normal_sample(self.mean, self.scale_tril)\n", " return F.linear(input, weight, bias)\n", "\n", " def _normal_sample(self, mean, scale_tril):\n", " sample = mean + scale_tril @ torch.randn_like(mean)\n", " # chunk out the weight and bias\n", " weight_vec, bias = torch.split(sample, [self.dim_in * self.dim_out, self.dim_out])\n", " return weight_vec.reshape(self.dim_out, self.dim_in), bias\n", "\n", "# now also define a function to make a network with only last layer using variational inference\n", "def make_last_layer_bnn(layer_sizes, activation='LeakyReLU', **layer_kwargs):\n", " nonlinearity = getattr(nn, activation)() if isinstance(activation, str) else activation\n", " net = nn.Sequential()\n", " linear_layer_kwargs = {}\n", " if 'device' in layer_kwargs:\n", " linear_layer_kwargs['device'] = layer_kwargs['device']\n", " for i, (dim_in, dim_out) in enumerate(zip(layer_sizes[:-2], layer_sizes[1:-1])):\n", " net.add_module(f'Linear{i}', nn.Linear(dim_in, dim_out, **linear_layer_kwargs))\n", " if i < len(layer_sizes) - 2:\n", " net.add_module(f'Nonlinarity{i}', nonlinearity)\n", " # last layer using variational inference with full covariance matrix Gaussian\n", " net.add_module(f'FullCovGaussianLinear{i+1}', \n", " FullCovGaussianLinear(layer_sizes[-2], layer_sizes[-1], **layer_kwargs))\n", " return net" ] }, { "cell_type": "markdown", "metadata": { "id": "shfw6zNCXhj4" }, "source": [ "Now we can run the same regression test again and see how the above full-covariance approximation method compare with the MFVI approach above." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 437 }, "id": "8AHiqu5eXpC_", "outputId": "d1c6eaea-1b64-470d-905b-4b930f7ea1cb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BNN architecture: \n", " Sequential(\n", " (Linear0): Linear(in_features=1, out_features=50, bias=True)\n", " (Nonlinarity0): GELU()\n", " (Linear1): Linear(in_features=50, out_features=50, bias=True)\n", " (Nonlinarity1): GELU()\n", " (FullCovGaussianLinear2): FullCovGaussianLinear(dim_in=50, dim_in=1, bias=True, weight prior std=1.00, bias prior std=1.00)\n", ")\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[0.0827434] 0.1 0.32889561076520935\n" ] } ], "source": [ "# build a last-layer-BNN with 2-hidden-layers, with hidden layer width = h_dim\n", "h_dim = 50\n", "layer_sizes = [x_dim, h_dim, h_dim, y_dim]\n", "# you can change the activation function here\n", "activation=nn.GELU()\n", "# you can change the prior parameters as you wish\n", "layer_kwargs = {'prior_weight_std': 1.0,\n", " 'prior_bias_std': 1.0,\n", " 'sqrt_width_scaling': False,\n", " 'init_std': 0.5,\n", " 'device': device}\n", "last_layer_bnn_regression_net = make_last_layer_bnn(layer_sizes, activation=activation, **layer_kwargs)\n", "# we assume a Gaussian likelihood with homogeneuous noise\n", "last_layer_bnn_log_noise_var = nn.Parameter(torch.ones(size=(), device=device)*-3.0)\n", "# print out the BNN settings\n", "print(\"BNN architecture: \\n\", last_layer_bnn_regression_net)\n", "\n", "y_pred_mean, y_pred_std_noiseless = get_regression_results(last_layer_bnn_regression_net, x_test_norm, K)\n", "model_noise_std = unnormalise_data(to_numpy(torch.exp(0.5*last_layer_bnn_log_noise_var)), 0.0, dataset.y_std)\n", "y_pred_std = np.sqrt(y_pred_std_noiseless ** 2 + model_noise_std**2)\n", "plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std,\n", " title='BNN init (before training, last-layer BNN)')\n", "print(model_noise_std, noise_std, y_pred_std_noiseless.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "w2FjUc8XZKFh" }, "source": [ "Now start training for this \"last-layer-BNN\" network.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 642 }, "id": "WPERGYPbZKl-", "outputId": "5a283db7-c85e-419d-e4e6-9c8ee75ce657" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 100, nll=7.5741448402404785, kl=16.825037002563477\n", "Epoch 200, nll=7.512971878051758, kl=17.12297821044922\n", "Epoch 300, nll=10.485010147094727, kl=17.375591278076172\n", "Epoch 400, nll=11.8046875, kl=17.535341262817383\n", "Epoch 500, nll=6.36690616607666, kl=17.731889724731445\n", "Epoch 600, nll=6.978448867797852, kl=17.925243377685547\n", "Epoch 700, nll=4.8727216720581055, kl=18.133678436279297\n", "Epoch 800, nll=4.2853007316589355, kl=18.337812423706055\n", "Epoch 900, nll=5.617944240570068, kl=18.60341453552246\n", "Epoch 1000, nll=3.699162483215332, kl=18.92740249633789\n", "Epoch 1100, nll=4.4642863273620605, kl=19.24707794189453\n", "Epoch 1200, nll=4.251572608947754, kl=19.634838104248047\n", "Epoch 1300, nll=1.185157299041748, kl=20.055099487304688\n", "Epoch 1400, nll=1.1818914413452148, kl=20.376676559448242\n", "Epoch 1500, nll=2.855886936187744, kl=20.68749237060547\n", "Epoch 1600, nll=0.8053992986679077, kl=20.981109619140625\n", "Epoch 1700, nll=1.329728603363037, kl=21.242769241333008\n", "Epoch 1800, nll=0.6363283395767212, kl=21.482852935791016\n", "Epoch 1900, nll=3.1070165634155273, kl=21.68404769897461\n", "Epoch 2000, nll=0.37617164850234985, kl=21.825599670410156\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# start training\n", "learning_rate = 1e-3\n", "params = list(last_layer_bnn_regression_net.parameters()) + [last_layer_bnn_log_noise_var]\n", "last_layer_bnn_opt = torch.optim.Adam(params, lr=learning_rate)\n", "data_loss_func = lambda y, y_pred: -gauss_loglik(y, y_pred, last_layer_bnn_log_noise_var)\n", "\n", "beta = 1.0\n", "N_epochs = 2000\n", "logs = train_network(last_layer_bnn_regression_net, last_layer_bnn_opt, \n", " dataloader, data_loss_func, beta=beta, verbose=True, N_epochs=N_epochs)\n", "# plot the training curve\n", "plot_training_loss(logs, beta)" ] }, { "cell_type": "markdown", "metadata": { "id": "UWLGn6sxZmnC" }, "source": [ "Training is now finished.\n", "\n", "Let's plot the predictions again, and compare it with the MFVI results above." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 298 }, "id": "9C7CmwUfcpui", "outputId": "77ac7f17-f9dc-4420-8cc6-87196333679d" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[0.12902853] 0.1 0.17606723866117474\n" ] } ], "source": [ "# plot the prediction results\n", "y_pred_mean, y_pred_std_noiseless = get_regression_results(last_layer_bnn_regression_net, x_test_norm, K)\n", "model_noise_std = unnormalise_data(to_numpy(torch.exp(0.5*last_layer_bnn_log_noise_var)), 0.0, dataset.y_std)\n", "y_pred_std = np.sqrt(y_pred_std_noiseless ** 2 + model_noise_std**2)\n", "plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std,\n", " title='BNN approx. posterior (last-layer BNN)')\n", "print(model_noise_std, noise_std, y_pred_std_noiseless.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "smpFt88Gi0FS" }, "source": [ "Similar to the MFVI tests, you can also test various hyper-parameter settings and see how they would affect the regression results using \"last-layer BNN\"." ] }, { "cell_type": "markdown", "metadata": { "id": "9YwGM-0WQhTx" }, "source": [ "## Monte Carlo dropout (MC-dropout)" ] }, { "cell_type": "markdown", "metadata": { "id": "Vfg88KutVyPb" }, "source": [ "For the mathematical foundation, see [lecture notes here](http://yingzhenli.net/home/pdf/ProbAI2022_lecture_note.pdf).\n", "\n", "For the MC-dropout method with dropout probability $p$, the q distribution is a mixture of delta measures. Specifically, for a layer with parameters $\\{W, b \\}, W \\in \\mathbb{R}^{d_{out} \\times d_{in}}, b \\in \\mathbb{R}^{d_{out}}$, the q distribution is\n", "\n", "$q(W, b) = \\prod_{i=1}^{d_{out}}q(W_i, b_i),$\n", "\n", "$q(W_i, b_i) = p \\delta(W_i = 0, b_i = 0) + (1 - p) \\delta(W_i = M_i, b_i = m_i),$\n", "\n", "with variational parameter for the layer as $\\{M, m \\}, M \\in \\mathbb{R}^{d_{out} \\times d_{in}}, m \\in \\mathbb{R}^{d_{out}}$. This means there are two equivalent way to compute $\\mathbb{E}_q[\\log p(y|x, \\theta)]$ with Monte Carlo: for the forward pass of a layer, below computations are equivalent:\n", "\n", "1. **drop weights**: sample $W, b \\sim q(W, b)$, then compute $y = xW^T + b$;\n", "2. **drop units**: compute $\\hat{y} = x M^T + m$, then apply dropout $y = \\text{dropout}(\\hat{y}; p)$.\n", "\n", "The $KL[q||p]$ regularizer for MC-dropout is ill-defined for Gaussian prior $p(\\theta)$. In practice this is replaced by an $\\ell_2$ regularizer, i.e., $\\frac{1-p}{2\\sigma^2_{\\text{prior}}}|| M ||_2^2$ for the weight variational parameter $M$ (and similarly for $m$).\n", "\n", "In below block you will implment a BNN layer with MC-dropout, and more specifically:\n", "\n", "* the forward pass with Monte Carlo: you are asked to implement both \"drop weight\" and the \"drop unit\" approach.\n", "* the $KL[q||p]$ regularisation term for one layer: use the $\\ell_2$ regularizer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "SeuiRYwNn2Or" }, "outputs": [], "source": [ "class MCDropoutLinear(nn.Module):\n", " \"\"\"Applies a linear transformation to the incoming data: y = xW^T + b, where \n", " the weight W and bias b are sampled from the q distribution.\n", " \"\"\"\n", "\n", " def __init__(self, dim_in, dim_out, prior_weight_std=1.0, prior_bias_std=1.0, dropout_prob=0.1,\n", " sqrt_width_scaling=False, device=None, dtype=None):\n", " factory_kwargs = {'device': device, 'dtype': dtype}\n", " super(MCDropoutLinear, self).__init__()\n", " self.dim_in = dim_in # dimension of network layer input\n", " self.dim_out = dim_out # dimension of network layer output\n", " self.dropout_prob = dropout_prob\n", " # the boolean indicator for the dropout implementation\n", " # if true then run the \"drop weight\" forward pass, else run \"drop unit\"\n", " self.drop_weight = False\n", "\n", " # define the trainable variational parameters for q distribtuion\n", " # first define and initialise the mean parameters\n", " self.weight = nn.Parameter(torch.empty((dim_out, dim_in), **factory_kwargs))\n", " self.bias = nn.Parameter(torch.empty(dim_out, **factory_kwargs))\n", " self.reset_parameters()\n", " \n", " # define the prior parameters (for prior p, assume the mean is 0)\n", " prior_mean = 0.0\n", " if sqrt_width_scaling: # prior variance scales as 1/dim_in\n", " prior_weight_std /= self.dim_in ** 0.5\n", " # prior parameters are registered as constants\n", " self.register_buffer('prior_weight_mean', torch.full_like(self.weight, prior_mean))\n", " self.register_buffer('prior_weight_std', torch.full_like(self.weight, prior_weight_std))\n", " self.register_buffer('prior_bias_mean', torch.full_like(self.bias, prior_mean))\n", " self.register_buffer('prior_bias_std', torch.full_like(self.bias, prior_bias_std)) \n", "\n", " def extra_repr(self):\n", " s = \"dim_in={}, dim_in={}, bias=True\".format(self.dim_in, self.dim_out)\n", " weight_std = self.prior_weight_std.data.flatten()[0]\n", " if torch.allclose(weight_std, self.prior_weight_std):\n", " s += f\", weight prior std={weight_std.item():.2f}\"\n", " bias_std = self.prior_bias_std.flatten()[0]\n", " if torch.allclose(bias_std, self.prior_bias_std):\n", " s += f\", bias prior std={bias_std.item():.2f}\"\n", " return s\n", "\n", " def reset_parameters(self, init_std=0.5):\n", " nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n", " bound = self.dim_in ** -0.5\n", " nn.init.uniform_(self.bias, -bound, bound)\n", "\n", " def set_dropout_mode(self, drop_weight=False):\n", " self.drop_weight = drop_weight\n", "\n", " # KL divergence KL[q||p]: for MCDropout this returns an l2 regulariser\n", " def kl_divergence(self):\n", " kl_weight = (1 - self.dropout_prob) * (self.weight / self.prior_weight_std).pow(2).sum()\n", " kl_bias = (1 - self.dropout_prob) * (self.bias / self.prior_bias_std).pow(2).sum()\n", " return kl_weight + kl_bias\n", "\n", " # forward pass with Monte Carlo (MC) sampling\n", " def forward(self, input):\n", " if self.drop_weight: # drop weight method\n", " # this part makes sure that the same units are dropped for all inputs\n", " dropout_mask = torch.where(torch.rand((self.dim_out,), device=input.device) > self.dropout_prob,\n", " 1.0, 0.0)\n", " weight = self.weight * dropout_mask.unsqueeze(-1) # broadcasting\n", " bias = self.bias * dropout_mask\n", " return F.linear(input, weight, bias)\n", " else: # drop unit method\n", " dropout_mask = torch.where(torch.rand((input.shape[0], self.dim_out), device=input.device) > self.dropout_prob,\n", " 1.0, 0.0)\n", " return F.linear(input, self.weight, self.bias) * dropout_mask\n", "\n", "# construct a BNN\n", "def make_mcdropout_bnn(layer_sizes, activation='LeakyReLU', **layer_kwargs):\n", " nonlinearity = getattr(nn, activation)() if isinstance(activation, str) else activation\n", " net = nn.Sequential()\n", " for i, (dim_in, dim_out) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):\n", " net.add_module(f'MCDropoutLinear{i}', MCDropoutLinear(dim_in, dim_out, **layer_kwargs))\n", " if i < len(layer_sizes) - 2:\n", " net.add_module(f'Nonlinarity{i}', nonlinearity)\n", " return net" ] }, { "cell_type": "markdown", "metadata": { "id": "jgKHmufGU4-g" }, "source": [ "Now we can run the same regression test again and see how the MC-dropout method compare with the other approaches above." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 455 }, "id": "ulX_qTfjm-JU", "outputId": "c0f570d3-265e-4e42-c8f3-91f87f9351cd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BNN architecture: \n", " Sequential(\n", " (MCDropoutLinear0): MCDropoutLinear(dim_in=1, dim_in=50, bias=True, weight prior std=1.00, bias prior std=1.00)\n", " (Nonlinarity0): GELU()\n", " (MCDropoutLinear1): MCDropoutLinear(dim_in=50, dim_in=50, bias=True, weight prior std=1.00, bias prior std=1.00)\n", " (Nonlinarity1): GELU()\n", " (MCDropoutLinear2): MCDropoutLinear(dim_in=50, dim_in=1, bias=True, weight prior std=1.00, bias prior std=1.00)\n", ")\n", "set the dropout mode: drop_weight=True\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[0.0827434] 0.1 0.018763944969284945\n" ] } ], "source": [ "# build a BNN with 2-hidden-layers, with hidden layer width = h_dim\n", "h_dim = 50\n", "layer_sizes = [x_dim, h_dim, h_dim, y_dim]\n", "# you can change the activation function here\n", "activation=nn.GELU()\n", "# you can change the prior parameters as you wish\n", "layer_kwargs = {'prior_weight_std': 1.0,\n", " 'prior_bias_std': 1.0,\n", " 'sqrt_width_scaling': False,\n", " 'dropout_prob': 0.1,\n", " 'device': device}\n", "mcdropout_bnn_regression_net = make_mcdropout_bnn(layer_sizes, activation=activation, **layer_kwargs)\n", "# we assume a Gaussian likelihood with homogeneuous noise\n", "mcdropout_bnn_log_noise_var = nn.Parameter(torch.ones(size=(), device=device)*-3.0)\n", "# print out the BNN settings\n", "print(\"BNN architecture: \\n\", mcdropout_bnn_regression_net)\n", "\n", "def set_dropout_mode(bnn, drop_weight=False):\n", " print(\"set the dropout mode: drop_weight={}\".format(drop_weight))\n", " for module in bnn:\n", " if hasattr(module, 'set_dropout_mode'):\n", " module.set_dropout_mode(drop_weight)\n", "\n", "set_dropout_mode(mcdropout_bnn_regression_net, drop_weight=True)\n", "y_pred_mean, y_pred_std_noiseless = get_regression_results(mcdropout_bnn_regression_net, x_test_norm, K)\n", "model_noise_std = unnormalise_data(to_numpy(torch.exp(0.5*mcdropout_bnn_log_noise_var)), 0.0, dataset.y_std)\n", "y_pred_std = np.sqrt(y_pred_std_noiseless ** 2 + model_noise_std**2)\n", "plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std,\n", " title='BNN init (before training, MC-dropout)')\n", "print(model_noise_std, noise_std, y_pred_std_noiseless.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "s13GEP1XVIkq" }, "source": [ "Now start training for this \"MC-dropout BNN\" network." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 660 }, "id": "W-j_7rDhnGdL", "outputId": "dc0f260e-090a-4036-87f5-f42be469ec2b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "set the dropout mode: drop_weight=False\n", "Epoch 100, nll=5.954626560211182, kl=44.787227630615234\n", "Epoch 200, nll=2.1349270343780518, kl=64.7195816040039\n", "Epoch 300, nll=2.4657979011535645, kl=70.44020080566406\n", "Epoch 400, nll=1.3780399560928345, kl=75.84844970703125\n", "Epoch 500, nll=1.6593471765518188, kl=77.41160583496094\n", "Epoch 600, nll=2.222825765609741, kl=76.26518249511719\n", "Epoch 700, nll=1.2437704801559448, kl=74.15946960449219\n", "Epoch 800, nll=0.7229245901107788, kl=71.92989349365234\n", "Epoch 900, nll=1.9560590982437134, kl=70.30957794189453\n", "Epoch 1000, nll=0.5606218576431274, kl=68.50248718261719\n", "Epoch 1100, nll=1.0137560367584229, kl=67.47718048095703\n", "Epoch 1200, nll=0.2885931730270386, kl=66.38134765625\n", "Epoch 1300, nll=0.7029552459716797, kl=65.1386947631836\n", "Epoch 1400, nll=1.0596873760223389, kl=64.25949096679688\n", "Epoch 1500, nll=0.3893250524997711, kl=63.58273696899414\n", "Epoch 1600, nll=0.48095443844795227, kl=62.772701263427734\n", "Epoch 1700, nll=0.8568823933601379, kl=62.2192268371582\n", "Epoch 1800, nll=1.1268246173858643, kl=61.76917266845703\n", "Epoch 1900, nll=0.8721067905426025, kl=61.22237014770508\n", "Epoch 2000, nll=0.9380476474761963, kl=60.454566955566406\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# start training\n", "learning_rate = 1e-3\n", "params = list(mcdropout_bnn_regression_net.parameters()) + [mcdropout_bnn_log_noise_var]\n", "mcdropout_bnn_opt = torch.optim.Adam(params, lr=learning_rate)\n", "data_loss_func = lambda y, y_pred: -gauss_loglik(y, y_pred, mcdropout_bnn_log_noise_var)\n", "set_dropout_mode(mcdropout_bnn_regression_net, drop_weight=False)\n", "\n", "beta = 1.0\n", "N_epochs = 2000\n", "logs = train_network(mcdropout_bnn_regression_net, mcdropout_bnn_opt, \n", " dataloader, data_loss_func, beta=beta, verbose=True, N_epochs=N_epochs)\n", "# plot the training curve\n", "plot_training_loss(logs, beta)" ] }, { "cell_type": "markdown", "metadata": { "id": "oCFsxSHsVTeA" }, "source": [ "Training is now finished.\n", "\n", "Let's plot the predictions again, and compare it with the results above." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 316 }, "id": "sFThZlR6nNKG", "outputId": "1276eb29-0360-48ce-f5a3-0cc26d43f77a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "set the dropout mode: drop_weight=True\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[0.12081081] 0.1 0.21194751857731547\n" ] } ], "source": [ "# plot the prediction results\n", "set_dropout_mode(mcdropout_bnn_regression_net, drop_weight=True)\n", "y_pred_mean, y_pred_std_noiseless = get_regression_results(mcdropout_bnn_regression_net, x_test_norm, K)\n", "model_noise_std = unnormalise_data(to_numpy(torch.exp(0.5*mcdropout_bnn_log_noise_var)), 0.0, dataset.y_std)\n", "y_pred_std = np.sqrt(y_pred_std_noiseless ** 2 + model_noise_std**2)\n", "plot_regression(x_train, y_train, x_test, y_pred_mean, y_pred_std_noiseless, y_pred_std,\n", " title='BNN approx. posterior (MC-dropout)')\n", "print(model_noise_std, noise_std, y_pred_std_noiseless.mean())" ] }, { "cell_type": "markdown", "metadata": { "id": "MgqfF_nqVZcw" }, "source": [ "Similar to previous tests, you can also test various hyper-parameter settings and see how they would affect the regression results using MC-dropout.\n", "\n", "In particular, try setting different value for ```dropout_prob``` in ```layer_kwargs```.\n", "\n", "Also you can try using \"drop unit\" forward pass when visualising the results, by using\n", "\n", "```set_dropout_mode(mcdropout_bnn_regression_net, drop_weight=False)```\n", "\n", "You can also try using \"drop weight\" forward pass when training:\n", "\n", "```set_dropout_mode(mcdropout_bnn_regression_net, drop_weight=True)```\n", "\n", "By doing so you'll see some visual differences in terms of learning curves and posterior predictive mean/std visualisations. Can you explain that?" ] }, { "cell_type": "markdown", "metadata": { "id": "4gyMwQLznjrr" }, "source": [ "# Case study 1: Bayesian optimisation" ] }, { "cell_type": "markdown", "metadata": { "id": "3kDCCbrqiMEJ" }, "source": [ "### What is Bayesian optimisation (BO)?\n", "\n", "(Feel free to skip this part if you already understand the idea.)\n", "\n", "Assume you are interested in optimising a function $x^* = \\arg\\max f_0(x)$, but you don't actually know the analytic form of $f(x)$. Rather, you only have access of it as a \"black-box\", where you provide an input $x$ to this \"black-box\", and it will return you a (noisy version of) output $y = f_0(x) + \\text{noise}$.\n", "\n", "Bayesian optimisation (BO) is a class of methods that tackle this challenge. \n", "\n", "To motivate BO, let's imagine you already have a set of datapoints $D = \\{ (x_n, y_n) \\}_{n=1}^N$ collected by sending the queries $x_1, ..., x_N$ to the above mentioned \"black-box\". Then we can fit a *surrogate function* $f_{\\theta}(x)$ to data. In large data limit $N \\rightarrow +\\infty$, with flexible enough model, we expect $f_{\\theta}(x)$ to be very close, if not identical, to the ground-truth function $f_0(x)$. In such case we can solve the optimisation problem by finding $x^* = \\arg\\max f_{\\theta}(x)$ instead, which is tractable.\n", "\n", "However, in practice we don't have a big dataset to train the surrogate model. This is expecially the case if the \"black-box\" corresponds to an expensive experiment (e.g., training a Transformer network where $x$ represents the hyper-parameter settings). In such scenario $f_{\\theta}(x)$ will be quite different from $f(x)$ in most of the unseen input locations. \n", "\n", "The key idea of BO is to optimise $f_0$ using \"helps\" from this surrogate by taking the uncertainty of model fitting into account, and it aims to find the optimum of $f_0$ with least amount of queries to the expensive \"black-box\". Specifically, there are 3 ingredients of an BO method:\n", "\n", "1. Acquisition function: using the current estimated surrogate function $f_{\\theta}(x)$ and its uncertainty estimate to compute an acquisition function;\n", "2. Query the \"black-box\": find the next input to query by maximising the acquisition function;\n", "3. Surrogate model update: given new queried result and historical data, update the surrogate model and its uncertainty estimate.\n", "\n", "At the beginning since the model is uncertain, a good acquisition function will take uncertainty into account and encourage \"exploration\", i.e., querying inputs at different locations.\n", "\n", "As we collect more data, with proper Bayesian posterior updates, the surrogate model $f_{\\theta}$ will become closer and closer to the ground-truth function $f_0$ and the uncertainty will be reduced.\n", "\n", "So at some point, the model will become certain about its output, and a good acquisition function will also enable \"exploitation\" at this stage, to seek for the optimum of the surrogate function $f_{\\theta}$ as to solve the original optimization problem." ] }, { "cell_type": "markdown", "metadata": { "id": "bVVZAjrRiQAw" }, "source": [ "### The Upper Confidence Bound (UCB) method\n", "\n", "The UCB is an acquisition function that uses both mean prediction and uncertainty. Specifically, assume the surrogate model provides both mean $m(x)$ and standard deviation $\\sigma(x)$ for a given input $x$, then the UCB acquisition function is the following:\n", "\n", "$$UCB(x) = m(x) + \\beta \\sigma(x).$$\n", "\n", "And the query procedure will pick the next query input as $x = \\arg\\max UCB(x)$.\n", "\n", "Initially, $\\sigma(x)$ can be quite large for many regions, meaning that UCB will mainly explore. As we collect more data, $\\sigma(x)$ will decrease around the regions that have been searched, and this allows the algorithm to \n", "\n", "1. ignore some searched regions where the model confidently thinks their function value is small;\n", "2. exploit some other searched regions where the model confidently believes the optimum might be there;\n", "3. explore some other promising regions that has not been searched before.\n", "\n", "Here $\\beta$ a hyper-parameter specified by the user at a time, to achieve a desired balance between exploration ($\\sigma(x)$) and exploitation ($m(x)$). When $\\beta = 0$, it means we trust the surrogate model and exploit on that. When $\\beta$ is large, we allow the query process to focus on regions that have large $\\sigma(x)$ value (where the model is most uncertainty about). For optimal BO, this $\\beta$ coefficient will decrease during time; for simplicity, in this demo we only consider a fixed value of $\\beta$." ] }, { "cell_type": "markdown", "metadata": { "id": "jIVBSDOViWXa" }, "source": [ "First we implement the ```ucb``` acquisition function as mentioned above." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uEZGG31Gnoak" }, "outputs": [], "source": [ "# the UCB acquisition function\n", "def ucb(net, x, beta, K):\n", " # assuming the network produces output f(x) of shape (K, N), i.e., y_dim = 1\n", " # the ucb acquisition function = mean(f(x)) + sqrt(beta) * std(f(x))\n", " y_pred = predict(net, x, K)\n", " y_pred_mean = y_pred.mean(0)\n", " y_pred_std = y_pred.std(0) # consider model/epistemic uncertainty only\n", " return y_pred_mean + math.sqrt(beta) * y_pred_std, y_pred_mean, y_pred_std\n", "\n", "# some helper function\n", "def get_next_query(net, x, acquisition_func, **kwargs):\n", " # search for the next query point in x using the acquisition function\n", " # and here we also assume that x_dim = 1\n", " f, y_pred_mean, y_pred_std = acquisition_func(net, x, **kwargs) # shape (N, 1)\n", " next_query = x[torch.argmax(f[:, 0])].unsqueeze(0)\n", " return next_query, f, y_pred_mean, y_pred_std\n", "\n", "def query_oracle(x, oracle_func, noise_std=None):\n", " # given input, query the oracle function (potentially with noise)\n", " y = oracle_func(x)\n", " if noise_std is not None and noise_std > EPS: \n", " y += np.random.randn(y.shape[0], y.shape[1]) * noise_std\n", " return y\n", "\n", "def reset_parameters(bnn, init_std):\n", " print(\"re-initialise the parameters with init_std={}\".format(init_std))\n", " for module in bnn:\n", " if hasattr(module, 'reset_parameters'):\n", " module.reset_parameters(init_std)" ] }, { "cell_type": "markdown", "metadata": { "id": "DI1pQKESiahp" }, "source": [ "Now let's implement the main loop of BO." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ne3pSg4TTrSm" }, "outputs": [], "source": [ "# now define the Bayesian optimisation search\n", "def bayesian_optimisation(net, log_noise_var, oracle_func, acquisition_func, x, x_init, T,\n", " noise_std = None, init_std = 0.1, N_epochs = 1000, **kwargs):\n", " # we start from a number of observations to initialise the dataset\n", " y_init = query_oracle(x_init, oracle_func, noise_std)\n", " observations = regression_data(x_init, y_init)\n", " # preparing the query candidates\n", " x_norm = normalise_data(x, observations.x_mean, observations.x_std)\n", " x_tensor = torch.tensor(x_norm).float().to(device)\n", " # now start the loop of BO for T steps\n", " acquisition_func_list = []\n", " mean_func_list = []; std_func_list = []\n", " for t in range(T):\n", " dataloader = DataLoader(observations, batch_size=min(100, len(observations)), shuffle=True)\n", " # train the network\n", " print(\"update BNN posterior...\")\n", " params = list(net.parameters()) + [log_noise_var]\n", " opt = torch.optim.Adam(params, lr=1e-3)\n", " data_loss_func = lambda y, y_pred: -gauss_loglik(y, y_pred, log_noise_var)\n", " # if using MC-dropout, use \"drop unit\" mode for training\n", " set_dropout_mode(net, drop_weight=False) # won't do anything if not usig MC-dropout\n", " train_network(net, opt, dataloader, data_loss_func, N_epochs=N_epochs, verbose=True)\n", " # now get next acquisition point and query\n", " print(\"query next point...\")\n", " # if using MC-dropout, use \"drop weight\" mode for computing the UCB acquisition function\n", " set_dropout_mode(net, drop_weight=True) # won't do anything if not usig MC-dropout\n", " x_next, f, y_pred_mean, y_pred_std = get_next_query(net, x_tensor, acquisition_func, **kwargs)\n", " x_next = unnormalise_data(to_numpy(x_next), observations.x_mean, observations.x_std)\n", " y_next = query_oracle(x_next, oracle_func, noise_std)\n", " # record the selections to be visualised\n", " acquisition_func_list.append(unnormalise_data(to_numpy(f), observations.y_mean, observations.y_std))\n", " mean_func_list.append(unnormalise_data(to_numpy(y_pred_mean), observations.y_mean, observations.y_std))\n", " std_func_list.append(unnormalise_data(to_numpy(y_pred_std), 0.0, observations.y_std))\n", " # add new observation to the dataset\n", " x_init = np.concatenate((x_init, x_next), axis=0)\n", " y_init = np.concatenate((y_init, y_next), axis=0)\n", " observations.update_data(x_init, y_init, update_stats=False)\n", " # reset the network for next round's training\n", " init_std_ = init_std * 0.9 ** (t+1) if init_std is not None else None\n", " reset_parameters(net, init_std=init_std_)\n", " log_noise_var.data = torch.full_like(log_noise_var.data, -3.0)\n", "\n", " return acquisition_func_list, mean_func_list, std_func_list, observations\n", "\n", "# define plot function\n", "def plot_bo_one_step(x_init, y_init, x, y_true, x_query, y_query, acquisition_value, \n", " mean_value, std_value, beta, title):\n", " plt.figure()\n", " plt.plot(x, y_true, 'k-', label='ground truth')\n", " plt.plot(x, mean_value, 'b-', label='prediction mean')\n", " plt.fill_between(x[:,0], mean_value[:,0]-math.sqrt(beta)*std_value[:,0], \n", " mean_value[:,0]+math.sqrt(beta)*std_value[:,0], \n", " color='b', alpha=0.3, label='epistemic uncertainty')\n", " plt.plot(x, acquisition_value, 'g-', label='acquisition func.')\n", " plt.plot(x_init, y_init, 'ro', label='observed data')\n", " plt.plot(x_query, y_query, 'go', label='query')\n", " plt.axvline(x_query, ymax=y_query, color='g', linestyle='--')\n", " plt.legend()\n", " plt.title(title)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "HRU1vDmnidXN" }, "source": [ "With all the helper functions set, let's test BO with MFVI-BNN on optimising a ground-truth function defined below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "ZTy4QJCAkze5", "outputId": "72e935be-7086-4032-821e-7d9cc651c8e1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BNN architecture: \n", " Sequential(\n", " (MFVILinear0): MFVILinear(dim_in=1, dim_in=50, bias=True, weight prior std=1.00, bias prior std=1.00)\n", " (Nonlinarity0): GELU()\n", " (MFVILinear1): MFVILinear(dim_in=50, dim_in=50, bias=True, weight prior std=1.00, bias prior std=1.00)\n", " (Nonlinarity1): GELU()\n", " (MFVILinear2): MFVILinear(dim_in=50, dim_in=1, bias=True, weight prior std=1.00, bias prior std=1.00)\n", ")\n", "update BNN posterior...\n", "set the dropout mode: drop_weight=False\n", "Epoch 100, nll=8.336997985839844, kl=4640.36767578125\n", "Epoch 200, nll=9.872835159301758, kl=4371.958984375\n", "Epoch 300, nll=8.439661979675293, kl=4106.19677734375\n", "Epoch 400, nll=5.578078269958496, kl=3843.329833984375\n", "Epoch 500, nll=4.22989559173584, kl=3583.70263671875\n", "Epoch 600, nll=5.672186851501465, kl=3327.124755859375\n", "Epoch 700, nll=4.718841552734375, kl=3074.73291015625\n", "Epoch 800, nll=6.496920585632324, kl=2826.08642578125\n", "Epoch 900, nll=1.650594711303711, kl=2582.082275390625\n", "Epoch 1000, nll=3.0706095695495605, kl=2344.590087890625\n", "query next point...\n", "set the dropout mode: drop_weight=True\n", "re-initialise the parameters with init_std=0.09000000000000001\n", "update BNN posterior...\n", "set the dropout mode: drop_weight=False\n", "Epoch 100, nll=8.899824142456055, kl=4921.3359375\n", "Epoch 200, nll=3.5112497806549072, kl=4656.43798828125\n", "Epoch 300, nll=1.8553911447525024, kl=4394.5244140625\n", "Epoch 400, nll=2.7215442657470703, kl=4133.98193359375\n", "Epoch 500, nll=1.5102652311325073, kl=3874.885009765625\n", "Epoch 600, nll=2.6327743530273438, kl=3618.96875\n", "Epoch 700, nll=1.5518287420272827, kl=3365.80517578125\n", "Epoch 800, nll=3.2238035202026367, kl=3116.306884765625\n", "Epoch 900, nll=0.3810604512691498, kl=2871.11181640625\n", "Epoch 1000, nll=9.798645973205566, kl=2630.802978515625\n", "query next point...\n", "set the dropout mode: drop_weight=True\n", "re-initialise the parameters with init_std=0.08100000000000002\n", "update BNN posterior...\n", "set the dropout mode: drop_weight=False\n", "Epoch 100, nll=8.753363609313965, kl=5210.28466796875\n", "Epoch 200, nll=4.8453803062438965, kl=4947.15478515625\n", "Epoch 300, nll=1.4732848405838013, kl=4687.00048828125\n", "Epoch 400, nll=2.2437050342559814, kl=4427.38671875\n", "Epoch 500, nll=1.7067071199417114, kl=4169.1796875\n", "Epoch 600, nll=0.8946418166160583, kl=3912.811767578125\n", "Epoch 700, nll=1.0503737926483154, kl=3658.716552734375\n", "Epoch 800, nll=1.1658861637115479, kl=3407.7890625\n", "Epoch 900, nll=2.1548120975494385, kl=3160.747802734375\n", "Epoch 1000, nll=1.8807153701782227, kl=2918.1259765625\n", "query next point...\n", "set the dropout mode: drop_weight=True\n", "re-initialise the parameters with init_std=0.0729\n", "update BNN posterior...\n", "set the dropout mode: drop_weight=False\n", "Epoch 100, nll=7.708780288696289, kl=5488.6728515625\n", "Epoch 200, nll=4.5420098304748535, kl=5224.7236328125\n", "Epoch 300, nll=1.5274755954742432, kl=4963.84765625\n", "Epoch 400, nll=1.1593273878097534, kl=4701.87255859375\n", "Epoch 500, nll=1.7745171785354614, kl=4440.5927734375\n", "Epoch 600, nll=0.9719876050949097, kl=4181.35888671875\n", "Epoch 700, nll=2.300814151763916, kl=3924.672119140625\n", "Epoch 800, nll=3.9958817958831787, kl=3670.648681640625\n", "Epoch 900, nll=1.9736778736114502, kl=3420.70654296875\n", "Epoch 1000, nll=3.098332405090332, kl=3173.965087890625\n", "query next point...\n", "set the dropout mode: drop_weight=True\n", "re-initialise the parameters with init_std=0.06561\n", "update BNN posterior...\n", "set the dropout mode: drop_weight=False\n", "Epoch 100, nll=6.2162909507751465, kl=5775.44677734375\n", "Epoch 200, nll=2.979985237121582, kl=5511.51513671875\n", "Epoch 300, nll=1.4080301523208618, kl=5246.4697265625\n", "Epoch 400, nll=2.6546475887298584, kl=4982.03466796875\n", "Epoch 500, nll=1.7427260875701904, kl=4719.1064453125\n", "Epoch 600, nll=1.750893473625183, kl=4457.5009765625\n", "Epoch 700, nll=0.8752027153968811, kl=4197.33154296875\n", "Epoch 800, nll=0.9941456317901611, kl=3939.23779296875\n", "Epoch 900, nll=2.1209940910339355, kl=3684.17529296875\n", "Epoch 1000, nll=0.8047113418579102, kl=3431.92236328125\n", "query next point...\n", "set the dropout mode: drop_weight=True\n", "re-initialise the parameters with init_std=0.05904900000000001\n", "update BNN posterior...\n", "set the dropout mode: drop_weight=False\n", "Epoch 100, nll=5.739528179168701, kl=6062.81640625\n", "Epoch 200, nll=2.3946373462677, kl=5799.5224609375\n", "Epoch 300, nll=1.0604619979858398, kl=5536.72509765625\n", "Epoch 400, nll=0.49678167700767517, kl=5272.49755859375\n", "Epoch 500, nll=2.0201001167297363, kl=5009.2490234375\n", "Epoch 600, nll=4.243810176849365, kl=4746.8330078125\n", "Epoch 700, nll=-0.02846052683889866, kl=4485.287109375\n", "Epoch 800, nll=0.7164862751960754, kl=4225.88232421875\n", "Epoch 900, nll=0.029599735513329506, kl=3968.31005859375\n", "Epoch 1000, nll=2.640874147415161, kl=3712.918212890625\n", "query next point...\n", "set the dropout mode: drop_weight=True\n", "re-initialise the parameters with init_std=0.05314410000000001\n", "update BNN posterior...\n", "set the dropout mode: drop_weight=False\n", "Epoch 100, nll=3.626210927963257, kl=6342.35693359375\n", "Epoch 200, nll=0.7592591643333435, kl=6077.05078125\n", "Epoch 300, nll=0.31310442090034485, kl=5809.5166015625\n", "Epoch 400, nll=0.4549700617790222, kl=5542.5859375\n", "Epoch 500, nll=1.0303164720535278, kl=5276.60595703125\n", "Epoch 600, nll=1.7761681079864502, kl=5011.90380859375\n", "Epoch 700, nll=0.3625526428222656, kl=4748.56640625\n", "Epoch 800, nll=0.37905409932136536, kl=4486.56982421875\n", "Epoch 900, nll=1.0777682065963745, kl=4226.478515625\n", "Epoch 1000, nll=0.4602893888950348, kl=3968.36181640625\n", "query next point...\n", "set the dropout mode: drop_weight=True\n", "re-initialise the parameters with init_std=0.04782969000000001\n", "update BNN posterior...\n", "set the dropout mode: drop_weight=False\n", "Epoch 100, nll=4.735955715179443, kl=6625.88037109375\n", "Epoch 200, nll=0.8928098678588867, kl=6361.349609375\n", "Epoch 300, nll=2.162907838821411, kl=6094.2646484375\n", "Epoch 400, nll=0.20870031416416168, kl=5827.06103515625\n", "Epoch 500, nll=1.1161366701126099, kl=5560.740234375\n", "Epoch 600, nll=1.0562957525253296, kl=5295.50634765625\n", "Epoch 700, nll=0.11476853489875793, kl=5031.2998046875\n", "Epoch 800, nll=0.40399837493896484, kl=4768.96142578125\n", "Epoch 900, nll=0.09225688129663467, kl=4508.11865234375\n", "Epoch 1000, nll=1.8354402780532837, kl=4248.58642578125\n", "query next point...\n", "set the dropout mode: drop_weight=True\n", "re-initialise the parameters with init_std=0.04304672100000001\n", "update BNN posterior...\n", "set the dropout mode: drop_weight=False\n", "Epoch 100, nll=4.882776737213135, kl=6911.294921875\n", "Epoch 200, nll=2.167949914932251, kl=6647.1240234375\n", "Epoch 300, nll=0.852096676826477, kl=6381.91162109375\n", "Epoch 400, nll=1.2299797534942627, kl=6114.3544921875\n", "Epoch 500, nll=0.23523586988449097, kl=5847.41064453125\n", "Epoch 600, nll=0.8623297214508057, kl=5581.27685546875\n", "Epoch 700, nll=0.27580031752586365, kl=5315.744140625\n", "Epoch 800, nll=3.4897382259368896, kl=5052.14697265625\n", "Epoch 900, nll=0.46925419569015503, kl=4789.68896484375\n", "Epoch 1000, nll=0.6628473997116089, kl=4528.66748046875\n", "query next point...\n", "set the dropout mode: drop_weight=True\n", "re-initialise the parameters with init_std=0.03874204890000001\n", "update BNN posterior...\n", "set the dropout mode: drop_weight=False\n", "Epoch 100, nll=4.574717044830322, kl=7195.13720703125\n", "Epoch 200, nll=2.279151439666748, kl=6930.05859375\n", "Epoch 300, nll=0.7961409687995911, kl=6664.25732421875\n", "Epoch 400, nll=0.48486799001693726, kl=6396.68896484375\n", "Epoch 500, nll=1.6276838779449463, kl=6129.556640625\n", "Epoch 600, nll=1.530023217201233, kl=5862.9287109375\n", "Epoch 700, nll=0.13047008216381073, kl=5596.99560546875\n", "Epoch 800, nll=2.0555262565612793, kl=5332.07470703125\n", "Epoch 900, nll=0.9974586367607117, kl=5068.40673828125\n", "Epoch 1000, nll=0.484404593706131, kl=4805.65576171875\n", "query next point...\n", "set the dropout mode: drop_weight=True\n", "re-initialise the parameters with init_std=0.03486784401000001\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# now start the test\n", "# first define the ground-truth/oracle function that we want to optimize\n", "def ground_truth_func_func(x):\n", " return np.sin(x * math.pi / 2 + 0.8) * np.exp(-0.1 * np.abs(x)) + 0.1 * x\n", "oracle_func = ground_truth_func\n", "\n", "# set-up BO hyper-parameters\n", "acquisition_func = ucb\n", "ucb_params = {'beta': 1.0, 'K': 50}\n", "noise_std = 0.1\n", "x_min = -3; x_max = 3; dx = 0.01\n", "x = np.arange(x_min, x_max+EPS, dx)[:, np.newaxis]\n", "y_true = query_oracle(x, oracle_func, noise_std=None)\n", "# choose some random query locations to start with\n", "N_init = 5\n", "x_init = np.copy(x); np.random.shuffle(x_init); x_init = x_init[:N_init]\n", "T = 10 # run T steps of BO search\n", "\n", "# then set-up the BNN\n", "x_dim, y_dim = x.shape[1], y_true.shape[1]\n", "h_dim = 50\n", "layer_sizes = [x_dim, h_dim, h_dim, y_dim]\n", "activation=nn.GELU()\n", "# MFVI-BNN hyper-parameters\n", "layer_kwargs = {'prior_weight_std': 1.0,\n", " 'prior_bias_std': 1.0,\n", " 'sqrt_width_scaling': False,\n", " 'init_std': 0.1,\n", " 'device': device}\n", "bo_net = make_mfvi_bnn(layer_sizes, activation=activation, **layer_kwargs)\n", "# we assume a Gaussian likelihood with homogeneuous noise\n", "bo_log_noise_var = nn.Parameter(torch.ones(size=(), device=device)*-3.0)\n", "# print out the BNN settings\n", "print(\"BNN architecture: \\n\", bo_net)\n", "\n", "# start the BO process!\n", "acquisition_func_list, mean_func_list, std_func_list, observations = \\\n", " bayesian_optimisation(bo_net, bo_log_noise_var, oracle_func, acquisition_func, x, x_init, T,\n", " noise_std=noise_std, init_std=layer_kwargs['init_std'], N_epochs=1000,\n", " **ucb_params)\n", " \n", "# plot results\n", "def plot_bo_results(acquisition_func_list, mean_func_list, std_func_list, observations, T, x, y_true):\n", " for t in range(T):\n", " x_init = unnormalise_data(observations.x[:N_init+t], observations.x_mean, observations.x_std)\n", " y_init = unnormalise_data(observations.y[:N_init+t], observations.y_mean, observations.y_std)\n", " x_query = unnormalise_data(observations.x[N_init+t], observations.x_mean, observations.x_std)\n", " y_query = unnormalise_data(observations.y[N_init+t], observations.y_mean, observations.y_std)\n", " acquisition_value = acquisition_func_list[t]\n", " mean_value = mean_func_list[t]; std_value = std_func_list[t]\n", " title = 'BO with UCB, t={}, beta={}'.format(t+1, ucb_params['beta'])\n", " plot_bo_one_step(x_init, y_init, x, y_true, x_query, y_query, acquisition_value, \n", " mean_value, std_value, beta = ucb_params['beta'], title=title)\n", "plot_bo_results(acquisition_func_list, mean_func_list, std_func_list, observations, T, x, y_true)" ] }, { "cell_type": "markdown", "metadata": { "id": "vCsJROl2pM7o" }, "source": [ "Let's test again but with some other settings of your choice. \n", "\n", "First, you can try using a BNN with MC-dropout, by e.g., copy-paste the code in below block to appropriate place.\n", "\n", "You can also try other hyper-parameters for the BNN, e.g., changing the activation function, number of layers, ```N_epochs```, initialisation of the q standard deviation ```init_std``` if using Gaussian approximations, ```dropout_prob``` if using MC-dropout, etc." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nReyeRH8aFZt" }, "outputs": [], "source": [ "# MC-dropout BNN hyper-parameters\n", "layer_kwargs = {'prior_weight_std': 1.0,\n", " 'prior_bias_std': 1.0,\n", " 'sqrt_width_scaling': False,\n", " 'dropout_prob': 0.1,\n", " 'device': device}\n", "bo_net = make_mcdropout_bnn(layer_sizes, activation=activation, **layer_kwargs)\n", "layer_kwargs['init_std'] = None" ] }, { "cell_type": "markdown", "metadata": { "id": "KqnMnyfGZ3Rw" }, "source": [ "Second, you can try BO for a different ground-truth function. You can define a new ground-truth function ```new_ground_truth(x)``` and set ```oracle_func = new_ground_truth``` to define the oracle to query. An example is provided below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gb5NpdDUpSWn" }, "outputs": [], "source": [ "# copy-paste some of the below code to the appropriate place\n", "# example: quadratic function\n", "def quadratic_func(x):\n", " return -2 * x ** 2 + 3 * x + 0.3\n", "oracle_func = quadratic_func" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "ProbAI 2022 BNN Tutorial - regression (with example answers).ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 1 }