Full Code of osh/KerasGAN for AI

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Repository: osh/KerasGAN
Branch: master
Commit: 017014734eb4
Files: 4
Total size: 1.7 MB

Directory structure:
gitextract_nmu0_69e/

├── MNIST_CNN_GAN.ipynb
├── MNIST_CNN_GAN_v2.ipynb
├── README.md
└── mnist_gan.py

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FILE CONTENTS
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================================================
FILE: MNIST_CNN_GAN.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 0: GeForce GTX TITAN X (CNMeM is disabled, cuDNN 5004)\n",
      "Using Theano backend.\n",
      "/usr/local/lib/python2.7/dist-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os,random\n",
    "os.environ[\"KERAS_BACKEND\"] = \"theano\"\n",
    "#os.environ[\"THEANO_FLAGS\"]  = \"device=gpu%d,lib.cnmem=0\"%(random.randint(0,3))\n",
    "import numpy as np\n",
    "import theano as th\n",
    "import theano.tensor as T\n",
    "from keras.utils import np_utils\n",
    "import keras.models as models\n",
    "from keras.layers import Input,merge\n",
    "from keras.layers.core import Reshape,Dense,Dropout,Activation,Flatten,MaxoutDense\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.activations import *\n",
    "from keras.layers.wrappers import TimeDistributed\n",
    "from keras.layers.noise import GaussianNoise\n",
    "from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D, UpSampling2D\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.regularizers import *\n",
    "from keras.layers.normalization import *\n",
    "from keras.optimizers import *\n",
    "from keras.datasets import mnist\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import cPickle, random, sys, keras\n",
    "from keras.models import Model\n",
    "from IPython import display\n",
    "\n",
    "sys.path.append(\"../common\")\n",
    "from keras.utils import np_utils\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 1.0\n",
      "('X_train shape:', (60000, 1, 28, 28))\n",
      "(60000, 'train samples')\n",
      "(10000, 'test samples')\n"
     ]
    }
   ],
   "source": [
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, shuffled and split between train and test sets\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "\n",
    "X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)\n",
    "X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "X_train /= 255\n",
    "X_test /= 255\n",
    "\n",
    "print np.min(X_train), np.max(X_train)\n",
    "\n",
    "print('X_train shape:', X_train.shape)\n",
    "print(X_train.shape[0], 'train samples')\n",
    "print(X_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def make_trainable(net, val):\n",
    "    net.trainable = val\n",
    "    for l in net.layers:\n",
    "        l.trainable = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n",
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_1 (InputLayer)             (None, 100)           0                                            \n",
      "____________________________________________________________________________________________________\n",
      "dense_1 (Dense)                  (None, 39200)         3959200     input_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "batchnormalization_1 (BatchNormal(None, 39200)         78400       dense_1[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "activation_1 (Activation)        (None, 39200)         0           batchnormalization_1[0][0]       \n",
      "____________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)              (None, 200, 14, 14)   0           activation_1[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "upsampling2d_1 (UpSampling2D)    (None, 200, 28, 28)   0           reshape_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_1 (Convolution2D)  (None, 100, 28, 28)   180100      upsampling2d_1[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "batchnormalization_2 (BatchNormal(None, 100, 28, 28)   56          convolution2d_1[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "activation_2 (Activation)        (None, 100, 28, 28)   0           batchnormalization_2[0][0]       \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_2 (Convolution2D)  (None, 50, 28, 28)    45050       activation_2[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "batchnormalization_3 (BatchNormal(None, 50, 28, 28)    56          convolution2d_2[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "activation_3 (Activation)        (None, 50, 28, 28)    0           batchnormalization_3[0][0]       \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_3 (Convolution2D)  (None, 1, 28, 28)     51          activation_3[0][0]               \n",
      "____________________________________________________________________________________________________\n",
      "activation_4 (Activation)        (None, 1, 28, 28)     0           convolution2d_3[0][0]            \n",
      "====================================================================================================\n",
      "Total params: 4262913\n",
      "____________________________________________________________________________________________________\n",
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_2 (InputLayer)             (None, 1, 28, 28)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_4 (Convolution2D)  (None, 256, 14, 14)   6656        input_2[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "leakyrelu_1 (LeakyReLU)          (None, 256, 14, 14)   0           convolution2d_4[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)              (None, 256, 14, 14)   0           leakyrelu_1[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "convolution2d_5 (Convolution2D)  (None, 512, 7, 7)     3277312     dropout_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leakyrelu_2 (LeakyReLU)          (None, 512, 7, 7)     0           convolution2d_5[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)              (None, 512, 7, 7)     0           leakyrelu_2[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)              (None, 25088)         0           dropout_2[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dense_2 (Dense)                  (None, 256)           6422784     flatten_1[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "leakyrelu_3 (LeakyReLU)          (None, 256)           0           dense_2[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)              (None, 256)           0           leakyrelu_3[0][0]                \n",
      "____________________________________________________________________________________________________\n",
      "dense_3 (Dense)                  (None, 2)             514         dropout_3[0][0]                  \n",
      "====================================================================================================\n",
      "Total params: 9707266\n",
      "____________________________________________________________________________________________________\n",
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_3 (InputLayer)             (None, 100)           0                                            \n",
      "____________________________________________________________________________________________________\n",
      "model_1 (Model)                  (None, 1, 28, 28)     4262913     input_3[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "model_2 (Model)                  (None, 2)             9707266     model_1[1][0]                    \n",
      "====================================================================================================\n",
      "Total params: 13970179\n",
      "____________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "shp = X_train.shape[1:]\n",
    "dropout_rate = 0.25\n",
    "opt = Adam(lr=1e-4)\n",
    "dopt = Adam(lr=1e-3)\n",
    "\n",
    "# Build Generative model ...\n",
    "nch = 200\n",
    "g_input = Input(shape=[100])\n",
    "H = Dense(nch*14*14, init='glorot_normal')(g_input)\n",
    "H = BatchNormalization(mode=2)(H)\n",
    "H = Activation('relu')(H)\n",
    "H = Reshape( [nch, 14, 14] )(H)\n",
    "H = UpSampling2D(size=(2, 2))(H)\n",
    "H = Convolution2D(nch/2, 3, 3, border_mode='same', init='glorot_uniform')(H)\n",
    "H = BatchNormalization(mode=2)(H)\n",
    "H = Activation('relu')(H)\n",
    "H = Convolution2D(nch/4, 3, 3, border_mode='same', init='glorot_uniform')(H)\n",
    "H = BatchNormalization(mode=2)(H)\n",
    "H = Activation('relu')(H)\n",
    "H = Convolution2D(1, 1, 1, border_mode='same', init='glorot_uniform')(H)\n",
    "g_V = Activation('sigmoid')(H)\n",
    "generator = Model(g_input,g_V)\n",
    "generator.compile(loss='binary_crossentropy', optimizer=opt)\n",
    "generator.summary()\n",
    "\n",
    "\n",
    "# Build Discriminative model ...\n",
    "d_input = Input(shape=shp)\n",
    "H = Convolution2D(256, 5, 5, subsample=(2, 2), border_mode = 'same', activation='relu')(d_input)\n",
    "H = LeakyReLU(0.2)(H)\n",
    "H = Dropout(dropout_rate)(H)\n",
    "H = Convolution2D(512, 5, 5, subsample=(2, 2), border_mode = 'same', activation='relu')(H)\n",
    "H = LeakyReLU(0.2)(H)\n",
    "H = Dropout(dropout_rate)(H)\n",
    "H = Flatten()(H)\n",
    "H = Dense(256)(H)\n",
    "H = LeakyReLU(0.2)(H)\n",
    "H = Dropout(dropout_rate)(H)\n",
    "d_V = Dense(2,activation='softmax')(H)\n",
    "discriminator = Model(d_input,d_V)\n",
    "discriminator.compile(loss='categorical_crossentropy', optimizer=dopt)\n",
    "discriminator.summary()\n",
    "\n",
    "# Freeze weights in the discriminator for stacked training\n",
    "make_trainable(discriminator, False)\n",
    "\n",
    "# Build stacked GAN model\n",
    "gan_input = Input(shape=[100])\n",
    "H = generator(gan_input)\n",
    "gan_V = discriminator(H)\n",
    "GAN = Model(gan_input, gan_V)\n",
    "GAN.compile(loss='categorical_crossentropy', optimizer=opt)\n",
    "GAN.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_loss(losses):\n",
    "        display.clear_output(wait=True)\n",
    "        display.display(plt.gcf())\n",
    "        plt.figure(figsize=(10,8))\n",
    "        plt.plot(losses[\"d\"], label='discriminitive loss')\n",
    "        plt.plot(losses[\"g\"], label='generative loss')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_gen(n_ex=16,dim=(4,4), figsize=(10,10) ):\n",
    "    noise = np.random.uniform(0,1,size=[n_ex,100])\n",
    "    generated_images = generator.predict(noise)\n",
    "\n",
    "    plt.figure(figsize=figsize)\n",
    "    for i in range(generated_images.shape[0]):\n",
    "        plt.subplot(dim[0],dim[1],i+1)\n",
    "        img = generated_images[i,0,:,:]\n",
    "        plt.imshow(img)\n",
    "        plt.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 9s - loss: 0.0405     \n"
     ]
    }
   ],
   "source": [
    "ntrain = 10000\n",
    "trainidx = random.sample(range(0,X_train.shape[0]), ntrain)\n",
    "XT = X_train[trainidx,:,:,:]\n",
    "\n",
    "# Pre-train the discriminator network ...\n",
    "noise_gen = np.random.uniform(0,1,size=[XT.shape[0],100])\n",
    "generated_images = generator.predict(noise_gen)\n",
    "X = np.concatenate((XT, generated_images))\n",
    "n = XT.shape[0]\n",
    "y = np.zeros([2*n,2])\n",
    "y[:n,1] = 1\n",
    "y[n:,0] = 1\n",
    "\n",
    "make_trainable(discriminator,True)\n",
    "discriminator.fit(X,y, nb_epoch=1, batch_size=128)\n",
    "y_hat = discriminator.predict(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 100.00 pct (20000 of 20000) right\n"
     ]
    }
   ],
   "source": [
    "y_hat_idx = np.argmax(y_hat,axis=1)\n",
    "y_idx = np.argmax(y,axis=1)\n",
    "diff = y_idx-y_hat_idx\n",
    "n_tot = y.shape[0]\n",
    "n_rig = (diff==0).sum()\n",
    "acc = n_rig*100.0/n_tot\n",
    "print \"Accuracy: %0.02f pct (%d of %d) right\"%(acc, n_rig, n_tot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set up loss storage vector\n",
    "losses = {\"d\":[], \"g\":[]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def train_for_n(nb_epoch=5000, plt_frq=25,BATCH_SIZE=32):\n",
    "\n",
    "    for e in tqdm(range(nb_epoch)):  \n",
    "        \n",
    "        # Make generative images\n",
    "        image_batch = X_train[np.random.randint(0,X_train.shape[0],size=BATCH_SIZE),:,:,:]    \n",
    "        noise_gen = np.random.uniform(0,1,size=[BATCH_SIZE,100])\n",
    "        generated_images = generator.predict(noise_gen)\n",
    "        \n",
    "        # Train discriminator on generated images\n",
    "        X = np.concatenate((image_batch, generated_images))\n",
    "        y = np.zeros([2*BATCH_SIZE,2])\n",
    "        y[0:BATCH_SIZE,1] = 1\n",
    "        y[BATCH_SIZE:,0] = 1\n",
    "        \n",
    "        #make_trainable(discriminator,True)\n",
    "        d_loss  = discriminator.train_on_batch(X,y)\n",
    "        losses[\"d\"].append(d_loss)\n",
    "    \n",
    "        # train Generator-Discriminator stack on input noise to non-generated output class\n",
    "        noise_tr = np.random.uniform(0,1,size=[BATCH_SIZE,100])\n",
    "        y2 = np.zeros([BATCH_SIZE,2])\n",
    "        y2[:,1] = 1\n",
    "        \n",
    "        #make_trainable(discriminator,False)\n",
    "        g_loss = GAN.train_on_batch(noise_tr, y2 )\n",
    "        losses[\"g\"].append(g_loss)\n",
    "        \n",
    "        # Updates plots\n",
    "        if e%plt_frq==plt_frq-1:\n",
    "            plot_loss(losses)\n",
    "            plot_gen()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f612992c950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f612992c950>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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43Y+c2pZ6VDRVAmg/Hw22RpQ2lqNrchcU1RcHtV+nxy9+CGdnnhXSPoiIiKJNNWA1NDRg\n8uTJGDZsmGvZ4sWL0blzZ7z22mtYvHgxtmzZghEjRijuo7C2BE22Zs0FarQ3AQDe3TEPU4a/iH3l\nBz3CFQCfcAXAFZRO1xUhzqz8YxXVl/hs41TWWI46S6JseQCgqKEUZphk91vXUg+7wzfUeO8DAE7V\nFSLJ6zj+tgEAm2RHSUOZ6nYAYDKZIEmSz3LnPiubqtBo0x56lcrlLItzWajhCgA+P7gUYy97JOT9\nEBERRZNqwEpISMCsWbMwe/Zs17JVq1bh0UcfBQDceeedfg8w5zevoqSkRnOBHv6xdTbwelsDAMAh\nOTRvCwB3nXcbruox1Gd5RWMl6m0N6J7SFY+sHCe77fnZ52LM4P+TLQ8APDv0MeR06uyxzOnBC+7B\npV0Hy+53V+lezNw53/X9jOsnw2SSD2pO3x5bia8OL/dZ/tq1L6luBwBWa5rsOX93xzzsLstDr7Se\nGHfZ3/zuR8724l2Yu3uBR1keW/Usmh0tQe3PW21LrS77ISIiiibVdiqLxYKEhASPZQUFBVi9ejVG\njx6Nxx9/HFVVVWEtYKABy66wflZSJnqmdoe5rWmuk1dTIADccOZw1X2bVE6XSaFmS3ZdP+EKAEae\neS0eGfwnzfvUwnluEszxQe8jweK7rXdNIBERUUfntw+WN0mS0LdvX4wZMwbvvvsuZs2ahbFjfWt0\n3FmtaUEVzmpNQ3Jpa8Dz7i+lJDkl3u/x5v1mKixmCyaumYH9pYddy4efd4nPuj3Tu6GguhAA0CUn\nHdnJ8vvOzEhWPG5mi2d/J63nw2odjOm53su0buu73p+G3o13Nn2Ah4beD2tGcNcktbk9cDuPoWe8\nMlvMQb9fos2o5TYynvPI4zmPPJ5zYwo4YOXk5GDo0NYmuGHDhmH69Ol+twmkidB7u5ra1qbCK7tf\ninWnN/vdprqmQePxHPjrhX9EXvkBV/Od3HZ/ufBBjF83CQBQUVEPe51Fdm811Y2Kx62u9uzvFOz5\nGJB9nqZtlZoIE5GKxwY/BDQHX4aKyvb+cM59BFrLqMZhl4IuWzQpnXMKH57zyOM5jzye88jTK9Bq\nGqbBvcP0NddcgzVr1gAAdu/ejb59++pSECXtN29tTXB2Sb6juZx4cxxyOnVWXSc7Kcv1tVnlyT+1\nZj/tjYfqhvW8Qqc9BS/ekuCzTK5DvZLzs8/1swabG4mIyPhUa7Byc3Mxfvx4lJWVwWKxYNGiRZg7\ndy4mTpyIzz77DCkpKZg8eXJYC+gMWBq6LQEI7GYPqIcmb2pDK6j1wQqkf5aazETl4TAi5fzsc3BT\n7xG4uOsgn9eGWC/C9pJdqtv/7pxR+O7Yamwo3BKuIhIREUWdasAaPHgwli5d6rP8zTffDFuBvDkC\nDExaOpC78zcelce+2yr8hnQZiO3FO7UfV6cqrN7pvfTZUQjMJjNGnX2TxzJn37juKV39BiyTyQyz\n6jXSq76PiIgoeoQfyd1Zg2U2yfd9CpXak4HenGFsWI/LZfYTnmDwl4EPhGW/4aClNrD1PDFEERFR\nbBM/YKE1YF3SZWBY9q9em+K9bnB9sEKhZeR2UWg5B2kJqaqvlzWW47tjq3QqERERUXSIH7DaarAS\n49RHPg9WIGM4qQasMNXKZCZmIMmShBG9rgnL/vXl/xzIjT/m7cvD/9WjMERERFET8DANkebsg2UW\nIAuqPikYphqsOHMcpg5/MWz715PSNEJEREQdTfRTix+Sqw9WeIrqb05AOd1SugAAkuM6uZZlJCg/\n4af2mhaih6uzM/oAALok50S5JERERGIwQA1WeANWcnwyxgz6P1hVwsGkYePR4DY5cmZiBiZePR6p\n8cnYUboHdocdPVK7KW6v9los+PPAP2B/xSEMsl6oaX3B8yIREVHIDBSwtN2Vg+kLdX5n9cEv0xPS\nkJ7gObJrRmLr9xeHqfO9kaTEJ/M8EBERuRG+idDVB0tjDRYnHiYiIqJoEz9gtQ3TEMh4VSK6oHP/\naBdBGMlxydEuAhERUVgJn1oi0UQYCaKWKxqu6jE02kUgIiIKKwMFrPCM5B4p7NjdrnNSFromW6Nd\nDCIiorAxQMBy9sHSWoMlpgCnVIxpFrMFz1/xJHqnRX9uRSIionAQPmAFOg6WqDkm3iz8A5uRJ2oa\nJiIiCpHwAcvZyT2QOQOF1Fb+rsldolwQcbBfGhERxSrxA5ariVC+D5Z3M5Oot+wTNQUAgKL64iiX\nJHrCNVgsERGRaIRvt3J1cleITllJGThWcyKSRQpKaUNZtIsQVbf1+x9c2nWwxzJRwzAREVGohK9S\n8D9Vjtdt2uhNiTFq5JnXIjMxQ/P6ucW7wlgaIiKi8DJMwFKa8NhnKR/Xiwlzdi+IdhGIiIiCZoCA\nJcEEk3INFmusiIiISDAGCFgOV+1VVmKm7wreNVYMXAbCa0VERLFJ/IAFh6v2aljPK6JcGiIiIiL/\nhA9YkuRwPUFoc9h8V2CNFREREQlG+IDlkCTXGFgX5vSPcmmIiIiI/DNAwHK4RnE/K/1MpMWneq7A\npwaFlxKfLLv84q4DI1wSIiKiyDBIwGovZoIlXnV9Tr9iHNefMQy39fufaBeDiIhId+IHLDg8xsDy\nqa9iHyzxKVQymkwmWDt1jmxZiIiIIkD8gCVJMLsVU2KToNAeHvTHaBeBiIgo6gwQsBzqkwQzcAll\nQOfzMKrvTR7LBlkvUNmCNZBERBR7DDHZs6XtKUIAkJTam0hIT1zyMM5I66n4OuMVERHFIuEDliQ5\nYDbHuX3PkdtF535F+mT0jlo5iIiIosUATYSSVxMha7BEF8iTnLyaREQUi8QPWPDsg+VzQ2YfLPEE\nVKnI60dERLFH/IDl1cmdTxGKT68aLF5rIiIyKmMELLiPg8U+WDFFJUTxgQYiIjIqAwQsCSa1YRq8\ncCT36DMFEHoZoYiIKBYJH7AkyQGLRx8syXuFCJeI9GSX7IqvsYmQiIiMSviAZZccHjVYvOnGll2l\nexVfYxMhEREZldABS5IkSJBg5lyEhhJIM63NYVN8jWGaiIiMSuiAVd1c2/ZvjdtS3nQ7Cl5pIiIy\nKqED1ucHvwYAFNeXupb5VGr4jOwe5kJRxLCJkIiIjMpvwMrLy8PIkSPx0UcfeSz/6aef0L9//7AV\nDABq2mqw3ElwqG/Ee3LMYBMhEREZlWrAamhowOTJkzFs2DCP5U1NTZg9eza6dOmie4H6pLfOXZca\nnwKHTJhiH6yOhAGLiIiMSTVgJSQkYNasWcjJyfFYPnPmTNx3332Ii9N/rugHLrgbANA/+xw45Gow\n/NVqMG8ZjPIFYxMhEREZlWrAslgsSEhI8FiWn5+Pw4cP48YbbwxLgeLM7aFNktRrsEwwcRwsAQUS\njJLiEpX3w0tLREQGFXAn98mTJ+Opp54KR1l8yNVgOW/eN/QajmnXvezzOkdyN5aMhHTF11iDRURE\nRhVQG19RURGOHDmCxx9/HABQUlKC0aNHY8GCBarbWa1pmo9haWgd2TsxMQ7mZuV9pKV2QveuWUg8\nHO+xPDU1MaDjRUMkyhfNc5BS1l4r5a8cnU63Xj+L2QK7w3NU986dU5CWmKp/AcNE9PddLOI5jzye\n88jjOTcmTQHL+TRX165d8e2337qWjxgxwm+4AoCSkhq/6zhVNrU+OdjUZENzS/sglM59ONqaDevr\nm1FSUoOmxhaP7WtqGwM6XjSEu3xWa1pUz0FdXZPra3/lcK4rV/NYUlqDxgRj1GJF+5x3RDznkcdz\nHnk855GnV6BVbSLMzc3FqFGjsHDhQsycOROjRo1CZWWl6/VAJvUNhtpj+kpNgWwijL5AhlcY0Pk8\nAMD1ZwzzsyYREZFxqNZgDR48GEuXLlV8/YcfftC9QE7OaXLklgN8WDBWnJt1Nl4Z9jxS41Pw3fFV\nHq/JPkVKRERkAPqPsxAi9xooh8xThK71nLVnXrVoDF7Gk5ag1M+KAYuIiIxJ6Kly5AJWe61WW5Ty\nquXgLTl28ClCIiIyKqEDlnofLBKVXsGIU+UQEZFRCR2wHCo3aqUO9gxeAmAuIiKiDk7sgKXWBwvy\nfbAodrCTOxERGZWAASvATu4+N2ExA1eSRXlKmFijX98pBiwiIjImAQNWO/U+OEpBSsyb8qizbwIA\n3NP/t1EuiXGwkzsRERmVcMM0uHNArYnQWK7teRUGWy9EZmJGtIsSdrrVXzFfERGRQQlbgyVBCrIP\nlpjRy2QydYhwpSdJJWATERGJTLiA5Z6XBuVcCADondZLZj2lPlgUfToN06DLXoiIiCJPuIDlrlda\nTwDAyN7XRrkkFA0cB4uIiIxK6IDl7OQsN4FzuCeapuDpl4sYsIiIyJiEDlhOqlGKQStmcRwsIiIy\nKmEDlgS3x/RlQpRJYS5C5i0RMBgREVHHJlzA8mgOlJxNhDLrMUkJS7e5CBnUiIjIoIQLWO7ab68y\nYYrNRzGPndyJiMioBA9YyjVYLqzJilmMV0REZFTiBiy32gvZ5kCOgyUs/WYi5ECjRERkTOIGLATb\nRMQaragLMvS+fu2/kJGQ5rYfncpDREQUYUIHLCe5cbAo9iRaEpAYl+j6np3ciYjIqAwRsFSxD1Zs\nkdy/ZMAiIiJjEjZgeYyDpboib8Ki0a0PFq8tEREZlHABy3McrLZlagONkoA42TMREXVswgUsd+01\nWNrDFGNX9LVftdCuBmuwiIjIqAQPWK04DlbH4d4szD5YRERkVEIHLLgGGlUJUazliFmswSIiIqMS\nOGBJ7dmJlVTGEkIwklS+IyIiMgrxApZHmNJQg0XCceXiEJtv7RJHciciImMSL2C50dQHx+smzjBm\ncG61X7N2zo9eOYiIiEIgeMBqxT5YHVOzoyXaRSAiIgqKsAFLAlzhiQ8KGguf/iMioo5OuIAlX1vF\nhGVEwTTXMpoREVEsEC5gudN0s2X1lnCcwyvwyhARUUcldMBqf4pQbRWvGMa7usGxDouIiIxP6IDV\nfqsNYC5C3p+JiIgoysQNWJLk1smd1VKGFMR1Yz4mIqJYIFzAcr8lB9UHi1ks6iQtTbtEREQxTLiA\n5U7iXIRERERkQEIHLCe2EHYcZpMh3pJERESqhL2bSWh/3F+2sYmhS1gSgp+l22Ky6FsYIiKiKBAw\nYMk9MSijfUbhcBaGgtF2bYK5MnFmBiwiIjI+vwErLy8PI0eOxEcffQQAOH36NB544AGMHj0aDz74\nIEpLS8NeSNVbtVcfLE72LJAgwi9rsIiIKBaoBqyGhgZMnjwZw4YNcy178803ceedd2LBggUYOXIk\n3nvvvbAVTnVOO+aomMQaLCIiigWqASshIQGzZs1CTk6Oa9nzzz+PG2+8EQCQlZWFysrKsBRMavsP\nYCug0YQy2TNrsIiIKBaoBiyLxYKEhASPZcnJybBYLLDb7Vi4cCFGjRqla4Hcw5Szk7tZrZhMX8IK\n5sqY+BQhERHFgLhgNrLb7Rg7diyuuOIKXHHFFX7Xt1rTNO+7vrm1SIkJcUhKav06OzsV1izPfaSl\nJsFqTUNiguePkJaWFNDxYlU0z0Gnk62h3GwyB1yOxATPGiwjXUsjlTVW8JxHHs955PGcG1NQAevp\np59Gnz598PDDD2tav6SkRvO+G2wNAICmZhvqzM0AgKrKBpTYPPdRU9uIkpIaNDW1eC6vaQroeLHI\nak2L6jloaGi9bpIkBVyOlmaHx/dGuZbRPucdEc955PGcRx7PeeTpFWg1tcdIbk/qff3110hISMCY\nMWN0KYCW48o9GcinBcUVSh8sDjRKRESxQLUGKzc3F+PHj0dZWRksFgsWLVoEu92OpKQkjB49GgDQ\nr18/vPDCC2Eomnsnd4YpQ3Hlq8Cv2x3n3ooXN0zRtThERESRphqwBg8ejKVLl0aqLG3ab8qS5Ghb\nwoBlRMHk4i7JVgyxXoTtJbv0LxAREVGECN0eU9JQBoA1WEYTShNh6/ZERETGJnTA2l9xCABgc9g0\nb8MoJpLQr4ZDcvhfiYiISDDCBiz3GXCa7c0+rzNIiSv0Gqj2PeSVHwx5b0RERJEmXMCSC0521mIY\nkh4huNnR4n8lIiIiwQgXsOT0Tu8V7SJQQNiLioiIOjahA9Y5mX0BAHGcn86Qgn36M8mSpHNJiIiI\nIkvogKVYEy4aAAAgAElEQVQ+DpbCzZtPHEadFGIF1m/63aJPQYiIiKJE4IAlQZLUakHYDCW8ILNu\nWkJq+zehpjUiIqIoEDBgtd+Vi+qLQx5TiaKB14yIiDo2AQNWqxM1p1DbUqeyBpsCReWMVxyBn4iI\nOiphA1ZVc3W0i0ACYF0YEREZkbABK1isM4ktbCImIiIjEi5gcd5B42MoIiKijk64gBWqrsnWaBeB\n2p78Yx8sIiLqqOKiXYBgeVd0ZSZm4I8X3os+Gb2jUyDyxXxFREQdVMzUYCXFJaFvxlnRLgaBHdOJ\niIhiJmCReNhESEREHZVwASvoWzJH/BYIrwUREXVswgUsMj5n1mX9FRERdVQGDlhet28O7yCg0K+J\nxJpJIiIyIAMHLOoYGLCIiMh4BAxYQdZ6sKZDIPpdi8K6Yt32RUREFCkCBiwyOtdkzzo02/736Pch\n74OIiCjSYidgsQ8WERERCSJ2AhYRERGRIIQLWBwHy/g42TMREXV0wgUsrdggKDDXOFi8SkRE1DEZ\nNmD5YB8sIiIiEkTsBCwiIiIiQQgfsC7KGaBtRfbBEgb7YBERUUcnXsDyauobkH1elApCodJjHCwi\nIiIjEi9geVG6Sft0oObNXBjOGixeESIi6qjED1gKy9kMRURERKISP2BprZliH6yYxKEeiIjIiIQL\nWN63U5NCEXnjFVd71uU1IiKijkm4gOVN8y2afbAEol8fLHaUJyIiIxI/YCndYHnfjWl9M3oDAC7q\nfH6US0JERBQ48QOWUpLy7nLFPlgx5ZazfgEAODP9jCiXhIiIKHDiByw2ERmO6wnPUK5d26bMzURE\nZETCBSzvGivFGiyf3vAMYqIJ5Yq0X3cmLCIiMh7hApY31mAZF5/0JCKijspvwMrLy8PIkSPx0Ucf\nAQBOnz6N0aNH495778Xf//53NDc3h7WAmm/RbEuKSRxQloiIjEg1YDU0NGDy5MkYNmyYa9lbb72F\n++67Dx999BF69+6Nzz//PKwFNJmEr2QjL5IOYddZ+8V4RURERqSaXhISEjBr1izk5OS4lm3atAkj\nRowAAFx//fVYv359WAuouZmJTYniCaWPOy8nEREZWJzaixaLBRaLxWNZQ0MD4uPjAQDZ2dkoLi4O\nX+mgfI9m/x7x6XKN2PRLREQGpBqw/NHaFGS1pmnep91h9/g+IyNZdvu0tCRYrWlITGwNexaLKaDj\nxLponovExNa3lcViDrocxVIKACA5JcEw19Uo5YwlPOeRx3MeeTznxhRwwEpOTkZzczMSEhJQVFSE\nLl26+N2mpKRG8/69A1ZNdaPs9jU1rcubmlpat7M5AjpOLLNa06J6Lhqd18Qe/DWpqqwHANTVNRni\nukb7nHdEPOeRx3MeeTznkadXoNXUg9y9puqqq67CihUrAADffvsthg8frktBnLyHZdA8TAM77Qij\n/d0S2khYRERERqVag5Wbm4vx48ejrKwMFosFixYtwty5c/H000/jk08+Qc+ePXHbbbeFtYDsa2Vc\nelw59sAiIiIjUg1YgwcPxtKlS32Wz5s3L2wF8qa5BoudocWhxzANJs6VQ0RExiX8IFOswTKei3IG\nAAAu63ZxyPtivCIiIiMK6SnCSFAOWF7L2QdLGJd3vwT9MvsiOykz6H0wWBMRkZGJH7AYnAypc6cs\nXfbDqXKIiMiIDNtE6LOUfXViSiC5ur6lAUerj4evMERERAESP2Ap3GgZp2KdesLaVboXu0r3AgCm\nbJ2OKVtmoKi+JBIFIyIi8kv8gMW5CDs0pdkCZu6cj5k75wMAiutLAQDljRWRKhYREZEq4QKWd6Ay\nmeSLyDgV24K5vg42ExMRkSCEC1hErVojViCd3CXJEa7CEBERBUT4gGVm01+HFMxl5xOHREQkCuED\nFsdD6tgCCU1sIiQiIlEIH7DY26pjcgXrADITmwiJiEgUwgUs74FFFZsI2XTYIQRUg8UmQiIiEoRw\nAcsbmwg7Jud1X3lirep6O0p2R6I4REREARE+YDFfdWz+arBm7/qgfV32wSIiIkEIH7BM4heRwoHB\nmoiIDEz49MJhGjqm4JqGWYNFRERiED5gUUfFYE1ERMYlfMBiJ3fSivVXREQkCuEDllITIWNXbOP1\nJSIiIxMyYHnWWvFW2xF5j4dGRERkJEIGLHe80ZLm4Rc4TAMREQlC/IDFGqwOT+to7oxXREQkCuED\nFodp6JgYrImIyMiED1hKfbB4A45tDreJmzlCOxERGY3wAYtBqmNqsDVGuwhERERBEz5gsYmQtPfB\nYk0XERGJQfiARWTUJsLKpiocqDgU7WIQEVEUCB+wOEwDGTNeAc/9PBFvbp+NmubaaBeFiIgiTMiA\n5R6qTGIWkSLKmBHL2WTZYGuIckmIiCjShE8vrMAirfHKqE2JREQUe8QPWHyKsMMTPTjVNNdi/PdT\ncLDiiOzrYpeeiIjCQfyA5acKa6D1AgDA0K5DIlEcigKtTwduLNyKd3bM8xhDKxJWn1yH/WVHMG37\nzIgel4iIxBUX7QL4468Ga2i3i9Evsw+yEjMjVCKKjPZQdaq2EGdnnuX63u6wy26xv+2JvcK6YvRI\n7RbW0rnz90cA62CJiDoe8WuwlEZyd7upZSdl8WnDGONeZ3Wq7rTHa18c/k/Yj19SX4aXN76O/Krj\nftc1i/9rREREESb8nUEpOJlZLxDj2iOWd8jeU5oX9qMvy/8Gp+oK8f7ehX7X5WC4RETkTfyApbSc\nN7WY5t6v3ftam03qb1s9R3TX8pCFv/IQEVHHI/ydwaRw81JaTrEhLSHV9bX3WGiRCDSBPLnIsE9E\nRN6ETCnutQZKNQhsIoxt7p3UA63B0pWGt5m/9yKHaSAi6niEDFju2ERIafEpHt9Hss9Ti90WsWMR\nEVHsED9gKdxMOQBpx5GekObxfUVjVcSOXdFUiZUn1kbseEREFBuCClh1dXUYM2YM7r//ftx9991Y\nuzZ8NyDFJkL2weowvDut17SEf/Jk92N+c+zHsB+PiIhiS1ADjX7xxRfo27cvHn/8cRQXF+MPf/gD\nli9frluh3COVYg0WmwhJgehT6xARUewLqhqoc+fOqKysBABUVVUhOztb10KpGZTTOjVOj5TIjdRN\n0aXnsAvaj0lERBS8oGqwbr75ZixZsgS//OUvUV1djTlz5uhdLhfvpsD/u2g0GmyNSIlPDtsxSSyB\nVkgxHBERUbQFFbC++uordO/eHXPmzEFeXh7Gjx+PTz/9VHF9qzVN8TVZJhMgtTYDym+bEdj+OqCA\nz7nAyqUSDG2b1FuL7KxkWLNC+/m3F+90fW1WfB+2SilPcn3tXM+9mTI7OwXWtNi5HiKJpfe5UfCc\nRx7PuTEFFbC2b9+OYcOGAQD69++PwsJCSJKk2C+qpKQmsAO03ZxMMAW+LcFqTYup8zZv2yfo1+kc\nZCSma1q/vKIOKTb9fn6HQ1I9n3W1ja6vS0pqsOrEz/j04Fft5SmvQ3xj7FwPUcTa+9wIeM4jj+c8\n8vQKtEH1werduzd27NgBACgoKEBycrK+nc7b9sXBRMmpvLEy2kVQ5N0k6R6uiIioYwqqBuuuu+7C\nM888g9GjR8Nms2HChAl6lwsAnxSk4EiQ0GBrRKe4JP8r6+xkzamIH5OIiMQTVMBKTk7GtGnT9C6L\nDAYs8rTu1Ga/6/x714cobSzHxKvHIyNRe1VvZVMVNhdux/W9hnksD+QpxuM1BZrXJSKi2BVUwIoU\n1mCRk/OtsPTICr/rljaWAwCK6osCClgzd87HiZoCxJvjAytbQGsTEVFHIORw6M4bFvtgkTeLyRK2\nfZ+qLQQAVDVXh7AXbbVdDsmB/KpjsDvsIRyLiIhEJWTAcmINFnkLbIqkyLx/ghl3a23BRkzd+ja+\nOqzfDAhERCQOsQMWa7DIiyWEOSgdkgML9i7G7tJ9sq87+1qF8r7T2l/rUOURAMDuMvmyEBGRsYkd\nsFiDRV4CqcHyfvecqCnAhsIteHfnewCApUe+kQ1bkXzXcd5EIqLYJHbAYg0WtXG9F0II3Q7J4fq6\nrqUeK47+4ApbQOBhR5IktDhsQZXF+cdDNOZZJCKi8BP0KcLWmw9rsChcAglTSiFozu4F2FGyG7/q\n80v3lTVxBkbGKyKi2MQaLKI2gdYm7SjZDQCoaKryu26TvRnfH1+N2pY6r4MyYhERxSIGLIph3u8f\nk/JLIZHcvpIPTMvzv8cXh/6Dj/d91np4E2uwiIhimdgBi02E5C2AGp83t8/CTwXrXd+bPPJVcO+t\nYDull7UNflpYX+JxfPbBIiKKTUIGLGewYg0WeQskjkiQsGj/F7oddN2pzRiz8inXgKQqq3otVC41\nnyIkIopNQgYsc1uxzKzBIh/BBxL3wO7+RKHWIyw+0BrWNhVu81w/iCK5l8XusGNb8U6O6k5EFEOE\nDFiN9kYA7J9C7fRuUhu3doLH90V1xX63cY7BZZfUgpB8+byX5lcfb1suYeXJtfj37g+xaP8Sv2UI\nxJxdH2Dmzvm67pOIiLQRdJiGVuWNFdEuAnUQEzZO9buOuW0eRLXar9rmer/7OVFTgKL69kB3uq4I\nALC3/IDfbQOR2/aUIxERRZ6QNVhEAJAWn+q7MArVms5aM+c0Pb4Bq71QP5/aKLO950OLJQ1lrq/r\nbQ3YcHqLXkUlIiJBMGCRsLqldPFZFmwT4cmaU6hvaQhq23pbAyRJcj18YfcKWJLH19qaCJ2a7c3t\n63h15lpx9EfsLz8UcHmJiCj6hG4iJHIqb6zAmelnBLVtfUsDJm2epnl9uSbAveUHFJ9qdZ/PUJJt\nPmwPTuqPbbSvV9Nci6VHVgAA3h7xqs+amwu3IyspE/0y+6jukYiIooM1WCQs99qgObsXtC0LXL0t\nsJqr746v8llWpTJae01Lretrh8wjhZLC13LrOWux1Pp5AcD8vQvxxrZ3VdchIqLoYcAiYcnWGIVh\n3KgtRbmaSqOledJnKhwvEpSDU3VzDcasfKrtiUZtQ5Qcrz6paT0iIoosIQPWL868LtpFIEGFo4+7\nloDlHnd+PrVRcYBQuRDmvm5xfSmqm2tUj7X+9BbVMeDc9/fDiTWq+wKA+XsWwuaw+V2PiIj0I2TA\nyk7KinYRKIb4qwsKZsaAk7WngysMgIMVh1Vfb3G0qJYp0I7+m4u2uyamJiKiyBAyYHEEd1LinNMv\n4nzek8HXpfkbn8rmsAU8GbW/PlveTz4SEVF4CRqwhCwWRVmwU8msO7VJ55IAgSQgm2TD3rK8AHbt\n51lDr+bJZnszHlk5Dh/s/UT7MYiIKKyETDImBiySEewYWCuO/aj6upao5L3O/oqDmo///bHVaHIb\n70oTlR/V+zyUtc14sLFwa2DHICKisBEyyZiD6BNDsce7H1I056Y0weRRgC8O/Ufzts55BwOxR6XG\ny7sGy/37vPKDaLA1Bnw8IiLSl5ABy8IaLJIThiEaAPhtkgt590H8wfDBPuXmPrWzMD13DmbJTPAc\nTBmIiCh4QiYZNhGSnGCbCKPNFGCAW1uwQfV17/Pg/f3ByiO+ZQioBEREFCohkww7uZOccMWrGj/j\nUgGBhyR3ejd5K43BRURE4hByLsJQbmYUu+Tn+QvdkapjftfZW3Yg6P0XN5QGva23/KrjWLBvsccy\nTYGLv1NERBElZsCKdgFIDCZxOrlvLtqGtPjUKJag1dStM3yWGbXplIgolgnZFscOuQRAplN7dIOE\nqEFGS7ncf6OWHl6BH477n2KHiIiCJ2YNFpszSAb7HikI8LQ4xwW74czhYSgMEREBgtZgEQEQqokw\nmj4/uBRF9SX4qWC97OvaatZ8/2g5Vn3C7xQ7REQUHDFrsNhESDI6ag3Wjyd+wo8nfgppH3K1wq9u\nmY57+v8WV/e4PKR9ExGRL9ZgkbB8p1eObsCqbamL6vHlSJIU0nk5UHFYx9IQEZGTkAGLA40Sabet\neGe0i0BERF6ETDJsICQ5jg7aROhPKE8EdtRmVyKicBM0YDFikRyGAW9an7hV+p2KdrMrEVGsErOT\nO4dpIBk2hy3aRRCOUg1UfUu9x/cmAC32Fnx2aKnn9uEqGBFRBydkDRYR4FvrsujAFx7f33zWyEgW\nx1Ca7M0+yzYWbvWZSJpNhERE4SFkwGITIQHARdYBHt/vLdvv8X3v9DNcX/dI6RaRMhmWySQbuioa\nK6NQGCKi2Bd0wPr666/x61//GrfffjtWr16tZ5k4Ly0BAM7J7Kt53Y7al2hX2T7Z5XLN7HK/Vsdq\nTuhcIiIiAoIMWBUVFXj77bexcOFCzJo1Cz/88IPOxWLCItZkatEsUyslx4Tg+1s12BqFHAOMiEhk\nQXVyX79+Pa666iokJycjOTkZEyZM0LVQvK2SFgxgkfHEmucBAG+PeDXKJSEiMo6garAKCgrQ2NiI\nv/71r7j33nuxfr38HGnBsibnAAD6pPfWdb9EHZepwzajEhFFQ1A1WJIkobKyEm+//TYKCgpw//33\nY+XKlYrrW61pAe3fijS886uXkZmUjjiLkCNJCC/Qcy6ihvhq1dezMlNdX1ssQj6vETVZ2cke32dm\ndEKDOUl23ZycVE1Do4j4nhKxTLGO5zzyeM6NKaj0kpOTgyFDhsBsNqNXr15ISUlBeXk5srOzZdcv\nKakJ4ijxqKhvCKZ4HZ7VmhbkORdLea1yv5+beo9AF3N3dEnOwTU9r8TPpzZFsGTiG7NsvMf3VVUN\nqG1olF23uKQaZg3TU4n2noqV97mR8JxHHs955OkVaIP6s//qq6/Ghg0bIEkSKioqUF9frxiuiMLh\nF72vQ7w5Di9cMRYjel0T7eIIxyE5PL5Xq6HiWFhERPoLqgara9euuPHGG3HnnXcCAMaPH+9nCyJ9\naalxIW0ckgMWWLClKBe7S/PwhwF3cTYFIqIQBd3B6a677sJdd92lZ1mIPKjVrJgYsAJyuPIoOsXJ\n98Fydn5/b8/HAIBf9f0l4swWZCZmRKx8RESxhncpEpZaw5XZa4iGRpt8/yJq9d3xVVh+9HvZ1xxe\nQfb9vQvx7M8v42TNqZCO2eKw4d+7P8TpuqKQ9kNEZEQMWCQw5YhlMVs8vm+0NYW7MLKeGfpYVI7r\nlJOkve9ji8Jk2d7DNxypOgYAOFp9PPiCAZi46XVsK96Jf218LaT9EBEZEQMWxQS7ZI/KcXumdo/K\ncZ3sXp3Zg6HUFLtw/5KQ9ltcXxrS9kRERsaARcIKZGBM76fmou2SLoMicpyKptAnaxZlAFLRriER\nUSgYsCgmOGuwrup+WZRL0spIT+GpBZtVJ3+OSBk+P7gUj6wch/oWjn1HRLGBAYvEFUDFSt+MswAA\nvdJ6hqcsMUytBuvTA19FpAw/nvgJAFBQG1rHeiIiURg+YBWW1yP3EPt6dCSp8Sk+y/580R/whwF3\n47JuQyJenq7J1ogfU08caJSISH+GD1jPzN6Atz7bifpG+SekyLiUalYeHvRHn2WpCSkY2u1imBD5\nprknLx0T8WPqSZQ+WEREscTwAcupxc4Osh1Fp7hOKq9GPmCpl0d8rMEiItJfzAQs6jgM1H/cELwH\nGo0mcUpCRBSa2AlYAt0kSB9KTVdqzYBGenpPFBIkTNz0hv/1+DtGRKRZ7AQs6jDUQpS/ePWQTP+t\njs4hOVBQe9rveuyrRUSkXewELNZcxBylCpNQOrLHm4Oe3zxmbS3KjXYRwoI1bhRNBbWnUd1cE+1i\nUBTFTsDih2nMyUrKkF2u3gyoHr6i8ZSh6Jblf6tpvcgEFn2OsbUoF2NWPhXyfIpEwbA5bJi46Q08\nvfalaBeFoih2AhbFnMzEDHTplOOz3BTC25Z9tMSmV4Zbcug/AIC1BRv12SFRAPSYI5SML2YCFuuv\nYpM1WSZgqWQkf/GJNVjBO1x1VPO6VU1sGiGiji1mAhZ1HGa1t62fGirRa7BuPuuGaBdB0ZvbZ8ku\nP1BxCMX1nrMp2Bwtuh33WPUJTNs2k6GNiAwlZgKW2LdN0pPFbAl620jVYA3rcUVQ24k0JpUWxfUl\neHP7bLy44VUU1J7G61vfRWlDObT+Rjbbm/HUTy+qrvPujvdwsPIIvjn2Y8Dl0/rko0NywO6wB7x/\nIjm8HxEQQwHLWLcl0kruBmkxKQcsv02EEfjku/msG9A9pWtQ26YlpOpcmvDZXboPL26Y4vr+/b2L\ncLgqH18c+o/m83yy9hRqW+pU13GgtT9LIJ3sXUFa4yYvbZyKv69+VvP+jeKT/V/g9a3vRLsYRB1S\nzAQs6jgsphA6uUfqb8sgDzOsZ3A1X9GwrXinx/ftAUjSfJ6911OvcQrfn1HF9aVwxGDH5DUF6wPq\nO0f64B/8BDBgkQGZVQKWvxt7JPpgSRrKocRI43TpcS7VrqXrOGxwIYPhGGwEMGCRAYVyY4/IzTqG\nP1xXn1zn+lqtJtH7GindcMIdeNVqxHaU7OFAkBQmsfsZQNoxYJGhnJ1xVrSLoEkoseGxi/+qWzn0\ntvjAl66vzQp94SS0dkz3XKY0r6T2jyD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6Fo/vnZoS2jv9x5nbr0VqaZLs+krS0pKwungN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\nEAgEgoX5f8EVoQD+VCPXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61292bb1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TjkugSRY8Qjdn62sFv5+ejYD7DewabwUjwx5wdzBv9kwYdjXSAbyhYrQxZwn7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\n4zPG86tu0EBkC0ek3N7eyvX1tStJs6K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71+EP4tLpdALJJIeIGIcgEom4hic0P2UyGVcC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\nGeaa3E9PTy7UjI0coK81PjcfNWkGGS4Wi1I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Download .txt
gitextract_nmu0_69e/

├── MNIST_CNN_GAN.ipynb
├── MNIST_CNN_GAN_v2.ipynb
├── README.md
└── mnist_gan.py
Download .txt
SYMBOL INDEX (6 symbols across 1 files)

FILE: mnist_gan.py
  function make_trainable (line 54) | def make_trainable(net, val):
  function make_trainable (line 103) | def make_trainable(net, val):
  function plot_loss (line 117) | def plot_loss(losses):
  function plot_gen (line 126) | def plot_gen(n_ex=16,dim=(4,4), figsize=(10,10) ):
  function train_for_n (line 170) | def train_for_n(nb_epoch=5000, plt_frq=25,BATCH_SIZE=32):
  function plot_real (line 223) | def plot_real(n_ex=16,dim=(4,4), figsize=(10,10) ):
Condensed preview — 4 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,780K chars).
[
  {
    "path": "MNIST_CNN_GAN.ipynb",
    "chars": 910675,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"out"
  },
  {
    "path": "MNIST_CNN_GAN_v2.ipynb",
    "chars": 832981,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"out"
  },
  {
    "path": "README.md",
    "chars": 1477,
    "preview": "# WARNING!!!!\n\n**This repository is not maintained!**\nI highly recommend you use a better maintained and up to date libr"
  },
  {
    "path": "mnist_gan.py",
    "chars": 7413,
    "preview": "#!/usr/bin/env python\n#\n# Keras GAN Implementation\n# See: https://oshearesearch.com/index.php/2016/07/01/mnist-generativ"
  }
]

About this extraction

This page contains the full source code of the osh/KerasGAN GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 4 files (1.7 MB), approximately 1.2M tokens, and a symbol index with 6 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.

Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.

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