Full Code of thibo73800/capsnet-traffic-sign-classifier for AI

Repository: thibo73800/capsnet-traffic-sign-classifier
Branch: master
Commit: 21a1fbce2ad2
Files: 19
Total size: 921.6 KB

Directory structure:
gitextract_oohy2z2g/

├── .floydignore
├── .gitignore
├── LICENSE
├── README.md
├── Traffic_Sign_Classifier.ipynb
├── _config.yml
├── caps_net.py
├── data_handler.py
├── floyd_requirements.txt
├── floyd_run.txt
├── logger.py
├── model.py
├── model_base.py
├── settings/
│   └── hyperparameters.json
├── signnames.csv
├── test.py
├── test_web_images.py
├── train.py
└── utils.py

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================================================
FILE: README.md
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# Capsnet - Traffic sign classifier - Tensorflow

A Tensorflow implementation of CapsNet(Capsules Net) apply on the German traffic sign dataset

[![Contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=plastic)](CONTRIBUTING.md)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg?style=plastic)](https://opensource.org/licenses/Apache-2.0)
![completion](https://img.shields.io/badge/completion%20state-80%25-blue.svg?style=plastic)

This implementation is based on this paper: <b>Dynamic Routing Between Capsules</b> (https://arxiv.org/abs/1710.09829) from Sara Sabour, Nicholas Frosst and Geoffrey E. Hinton.

This repository is a work in progress implementation of a Capsules Net. Since I am using a different dataset (Not MNIST) some details in the architecture are different. The code for the CapsNet is located in the following file: <b>caps_net.py</b> while the whole model is created inside the <b>model.py</b> file. The two main methods used to build the CapsNet are  <b>conv_caps_layer</b> and <b>fully_connected_caps_layer</b>

<img src="images/chart.jpg"></img>

## Requirements
- Python 3
- NumPy 1.13.1
- Tensorflow 1.3.0
- docopt 0.6.2
- Sklearn: 0.18.1
- Matplotlib

## Install

    $> git clone https://github.com/thibo73800/capsnet_traffic_sign_classifier.git
    $> cd capsnet_traffic_sign_classifier.git
    $> wget https://d17h27t6h515a5.cloudfront.net/topher/2017/February/5898cd6f_traffic-signs-data/traffic-signs-data.zip
    $> unzip traffic-signs-data.zip
    $> mkdir dataset
    $> mv *.p dataset/
    $> rm traffic-signs-data.zip
   
## Train

    $> python train.py -h
    $> python train.py dataset/

During the training, the checkpoint is saved by default into the outputs/checkpoints/ folder. The exact path and name of the checkpoint is print during the training.

## Test

In order to measure the accuracy and the loss on the Test dataset you need to used the test.py script as follow:

    $> python test.py outputs/checkpoints/ckpt_name dataset/ 

## Metrics / Tensorboard

<b>Accuracy: </b>
<ul>
    <li>Train: 99%</li>
    <li>Validation: 98%</li>
    <li>Test: 97%</li>
</ul>

Checkpoints and tensorboard files are stored inside the <b>outputs</b> folder.

<img src="images/tensorboard.png"></img>

Exemple of some prediction:

<img src="images/softmax.png"></img>





================================================
FILE: Traffic_Sign_Classifier.ipynb
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 0: Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34799, 32, 32, 3)\n",
      "(34799,)\n"
     ]
    }
   ],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "\n",
    "# TODO: Fill this in based on where you saved the training and testing data\n",
    "\n",
    "training_file = \"dataset/train.p\"\n",
    "validation_file= \"dataset/valid.p\"\n",
    "testing_file = \"dataset/test.p\"\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(validation_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "    \n",
    "X_train, y_train = train['features'], train['labels']\n",
    "X_valid, y_valid = valid['features'], valid['labels']\n",
    "X_test, y_test = test['features'], test['labels']\n",
    "\n",
    "# Print the shape of variables\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 1: Dataset Summary & Exploration\n",
    "\n",
    "The pickled data is a dictionary with 4 key/value pairs:\n",
    "\n",
    "- `'features'` is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).\n",
    "- `'labels'` is a 1D array containing the label/class id of the traffic sign. The file `signnames.csv` contains id -> name mappings for each id.\n",
    "- `'sizes'` is a list containing tuples, (width, height) representing the original width and height the image.\n",
    "- `'coords'` is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. **THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES**\n",
    "\n",
    "Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the [pandas shape method](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shape.html) might be useful for calculating some of the summary results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples = 34799\n",
      "Number of testing examples = 12630\n",
      "Image data shape = (32, 32, 3)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "### Replace each question mark with the appropriate value. \n",
    "### Use python, pandas or numpy methods rather than hard coding the results\n",
    "\n",
    "# TODO: Number of training example\n",
    "n_train = X_train.shape[0]\n",
    "\n",
    "# TODO: Number of validation example\n",
    "n_validation = X_valid.shape[0]\n",
    "\n",
    "# TODO: Number of testing example.\n",
    "n_test = X_test.shape[0]\n",
    "\n",
    "# TODO: What's the shape of an traffic sign image?\n",
    "image_shape = X_train.shape[1:]\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "n_classes = len(set(y_train))\n",
    "\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of testing examples =\", n_test)\n",
    "print(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Include an exploratory visualization of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc. \n",
    "\n",
    "The [Matplotlib](http://matplotlib.org/) [examples](http://matplotlib.org/examples/index.html) and [gallery](http://matplotlib.org/gallery.html) pages are a great resource for doing visualizations in Python.\n",
    "\n",
    "**NOTE:** It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TT/XrlI2vWR32lY26TSlzanWDHdxpR/UupLLjt5eZOmRlUGeuLE/gOmTJ33a5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\n1rz/EwCECmnUBCAh53ISaWIWSCe6am3h+9diRzTJTSKpRl5TrO8aO43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bBIyUeIn1ow89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PntB0pj/Aht6/fLz6c1Pk4A3W4k23cVzUxXr3NjnTuvo7mVrH+dmDj9FM0df+RBN3aujx9X13T7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\niCMRFpb9+tQLjoUkKXi8Q5zp4XFcj+zZl1L6lPy1v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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec8bd53898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common index\n",
      "index: 2 => Speed limit (50km/h) = 0.0591355252409\n",
      "index: 1 => Speed limit (30km/h) = 0.0582529054612\n",
      "index: 13 => Yield = 0.0564876659017\n",
      "index: 12 => Priority road = 0.055605046122\n",
      "index: 38 => Keep right = 0.0547224263423\n",
      "index: 10 => No passing for vehicles over 3.5 metric tons = 0.0529571867829\n",
      "index: 4 => Speed limit (70km/h) = 0.0520745670032\n",
      "index: 5 => Speed limit (80km/h) = 0.0485440878843\n",
      "index: 25 => Road work = 0.0397178900872\n",
      "index: 9 => No passing = 0.0388352703074\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec6d15eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Data exploration visualization code goes here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import random\n",
    "from PIL import Image, ImageEnhance\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "\n",
    "# Load name of id\n",
    "with open(\"signnames.csv\", \"r\") as f:\n",
    "    signnames = f.read()\n",
    "id_to_name = { int(line.split(\",\")[0]):line.split(\",\")[1] for line in signnames.split(\"\\n\")[1:] if len(line) > 0}\n",
    "\n",
    "\n",
    "graph_size = 3\n",
    "random_index_list = [random.randint(0, X_train.shape[0]) for _ in range(graph_size * graph_size)]\n",
    "fig = plt.figure(figsize=(15, 15))\n",
    "for i, index in enumerate(random_index_list):\n",
    "    a=fig.add_subplot(graph_size, graph_size, i+1)\n",
    "    #im = Image.fromarray(np.rollaxis(X_train[index] * 255, 0,3))\n",
    "    imgplot = plt.imshow(X_train[index])\n",
    "    # Plot some images\n",
    "    a.set_title('%s' % id_to_name[y_train[index]])\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# the histogram of the data\n",
    "values, bins, patches = ax.hist(y_train, n_classes, normed=10)\n",
    "\n",
    "# add a 'best fit' line\n",
    "ax.set_xlabel('Smarts')\n",
    "ax.set_title(r'Histogram of classess')\n",
    "\n",
    "# Tweak spacing to prevent clipping of ylabel\n",
    "fig.tight_layout()\n",
    "\n",
    "print (\"Most common index\")\n",
    "most_common_index = sorted(range(len(values)), key=lambda k: values[k], reverse=True)\n",
    "for index in most_common_index[:10]:\n",
    "    print(\"index: %s => %s = %s\" % (index, id_to_name[index], values[index]))\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## Step 2: Design and Test a Model Architecture\n",
    "\n",
    "Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset).\n",
    "\n",
    "The LeNet-5 implementation shown in the [classroom](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play! \n",
    "\n",
    "With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission. \n",
    "\n",
    "There are various aspects to consider when thinking about this problem:\n",
    "\n",
    "- Neural network architecture (is the network over or underfitting?)\n",
    "- Play around preprocessing techniques (normalization, rgb to grayscale, etc)\n",
    "- Number of examples per label (some have more than others).\n",
    "- Generate fake data.\n",
    "\n",
    "Here is an example of a [published baseline model on this problem](http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf). It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-process the Data Set (normalization, grayscale, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, `(pixel - 128)/ 128` is a quick way to approximately normalize the data and can be used in this project. \n",
    "\n",
    "Other pre-processing steps are optional. You can try different techniques to see if it improves performance. \n",
    "\n",
    "Use the code cell (or multiple code cells, if necessary) to implement the first step of your project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "### Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include \n",
    "### converting to grayscale, etc.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "#  I used keras only for the ImageDataGenerator\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "\n",
    "X_train = X_train / 255\n",
    "X_valid = X_valid / 255\n",
    "X_test = X_test / 255\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def preprocessing_function(img):\n",
    "    \"\"\"\n",
    "        Custom preprocessing_function\n",
    "    \"\"\"\n",
    "    img = img * 255\n",
    "    img = Image.fromarray(img.astype('uint8'), 'RGB')\n",
    "    img = ImageEnhance.Brightness(img).enhance(random.uniform(0.6, 1.5))\n",
    "    img = ImageEnhance.Contrast(img).enhance(random.uniform(0.6, 1.5))\n",
    "\n",
    "    return np.array(img) / 255\n",
    "\n",
    "train_datagen = ImageDataGenerator()\n",
    "train_datagen_augmented = ImageDataGenerator(\n",
    "    rotation_range=20,\n",
    "    shear_range=0.2,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    preprocessing_function=preprocessing_function)\n",
    "inference_datagen = ImageDataGenerator()\n",
    "train_datagen.fit(X_train)\n",
    "train_datagen_augmented.fit(X_train)\n",
    "inference_datagen.fit(X_valid)\n",
    "inference_datagen.fit(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exemple of augmented images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aSk+wptKFj1mNpaIyhvLOZYssuts9ycvWvSC6a6ZT6zFA7doaszxlypxvi7Urdp77m5QL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\nbeYyWK9M533qMauBdWuVfph3jVG+/z2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GehLyDHM5KJQFao+zODm70h8DeN17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\n10RYRiUw/iIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febbb1a5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "n = 0\n",
    "\n",
    "graph_size = 3\n",
    "\n",
    "for x_batch, y_batch in train_datagen_augmented.flow(X_train, y_train, batch_size=1):\n",
    "    a=fig.add_subplot(graph_size, graph_size, n+1)\n",
    "    imgplot = plt.imshow(x_batch[0])\n",
    "    n = n + 1\n",
    "    if n > 8:\n",
    "        break\n",
    "\n",
    "    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CapsNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def conv_caps_layer(input_layer, capsules_size, nb_filters, kernel, stride=2):\n",
    "    \"\"\"\n",
    "        Capsule layer for the convolutional inputs\n",
    "        **input:\n",
    "            *input_layer: (Tensor)\n",
    "            *capsule_numbers: (Integer) the number of capsule in this layer.\n",
    "            *kernel_size: (Integer) Size of the kernel for each filter.\n",
    "            *stride: (Integer) 2 by default\n",
    "    \"\"\"\n",
    "    # \"In convolutional capsule layers each unit in a capsule is a convolutional unit.\n",
    "    # Therefore, each capsule will output a grid of vectors rather than a single vector output.\"\n",
    "    capsules = tf.contrib.layers.conv2d(\n",
    "        input_layer, nb_filters * capsules_size, kernel, stride, padding=\"VALID\")\n",
    "    # conv shape: [?, kernel, kernel, nb_filters]\n",
    "    shape = capsules.get_shape().as_list()\n",
    "    capsules = tf.reshape(capsules, shape=(-1, np.prod(shape[1:3]) * nb_filters, capsules_size, 1))\n",
    "    # capsules shape: [?, nb_capsules, capsule_size, 1]\n",
    "    return squash(capsules)\n",
    "\n",
    "def routing(u_hat, b_ij, nb_capsules, nb_capsules_p, iterations=4):\n",
    "    \"\"\"\n",
    "        Routing algorithm\n",
    "\n",
    "        **input:\n",
    "            *u_hat: Dot product (weights between previous capsule and current capsule)\n",
    "            *b_ij: the log prior probabilities that capsule i should be coupled to capsule j\n",
    "            *nb_capsules_p: Number of capsule in the previous layer\n",
    "            *nb_capsules: Number of capsule in this layer\n",
    "    \"\"\"\n",
    "    # Start the routing algorithm\n",
    "    for it in range(iterations):\n",
    "        with tf.variable_scope('routing_' + str(it)):\n",
    "            # Line 4 of algo\n",
    "            # probabilities that capsule i should be coupled to capsule j.\n",
    "            # c_ij:  [nb_capsules_p, nb_capsules, 1, 1]\n",
    "            c_ij = tf.nn.softmax(b_ij, dim=2)\n",
    "\n",
    "            # Line 5 of algo\n",
    "            # c_ij:  [      nb_capsules_p, nb_capsules, 1,         1]\n",
    "            # u_hat: [?,    nb_capsules_p, nb_capsules, len_v_j,   1]\n",
    "            s_j = tf.multiply(c_ij, u_hat)\n",
    "            # s_j: [?, nb_capsules_p, nb_capsules, len_v_j, 1]\n",
    "            s_j = tf.reduce_sum(s_j, axis=1, keep_dims=True)\n",
    "            # s_j: [?, 1, nb_capsules, len_v_j, 1)\n",
    "\n",
    "            # line 6:\n",
    "            # squash using Eq.1,\n",
    "            v_j = squash(s_j)\n",
    "            # v_j: [1, 1, nb_capsules, len_v_j, 1)\n",
    "\n",
    "            # line 7:\n",
    "            # Frist reshape & tile v_j\n",
    "            # [? ,  1,              nb_capsules,    len_v_j, 1] ->\n",
    "            # [?,   nb_capsules_p,  nb_capsules,    len_v_j, 1]\n",
    "            v_j_tiled = tf.tile(v_j, [1, nb_capsules_p, 1, 1, 1])\n",
    "            # u_hat:    [?,             nb_capsules_p, nb_capsules, len_v_j, 1]\n",
    "            # v_j_tiled [1,             nb_capsules_p, nb_capsules, len_v_j, 1]\n",
    "            u_dot_v = tf.matmul(u_hat, v_j_tiled, transpose_a=True)\n",
    "            # u_produce_v: [?, nb_capsules_p, nb_capsules, 1, 1]\n",
    "            b_ij += tf.reduce_sum(u_dot_v, axis=0, keep_dims=True)\n",
    "            #b_ih: [1, nb_capsules_p, nb_capsules, 1, 1]\n",
    "\n",
    "    return tf.squeeze(v_j, axis=1)\n",
    "\n",
    "def fully_connected_caps_layer(input_layer, capsules_size, nb_capsules, iterations=4):\n",
    "    \"\"\"\n",
    "        Second layer receiving inputs from all capsules of the layer below\n",
    "            **input:\n",
    "                *input_layer: (Tensor)\n",
    "                *capsules_size: (Integer) Size of each capsule\n",
    "                *nb_capsules: (Integer) Number of capsule\n",
    "                *iterations: (Integer) Number of iteration for the routing algorithm\n",
    "\n",
    "            i refer to the layer below.\n",
    "            j refer to the layer above (the current layer).\n",
    "    \"\"\"\n",
    "    shape = input_layer.get_shape().as_list()\n",
    "    # Get the size of each capsule in the previous layer and the current layer.\n",
    "    len_u_i = np.prod(shape[2])\n",
    "    len_v_j = capsules_size\n",
    "    # Get the number of capsule in the layer bellow.\n",
    "    nb_capsules_p = np.prod(shape[1])\n",
    "\n",
    "    # w_ij: Used to compute u_hat by multiplying the output ui of a capsule in the layer below\n",
    "    # with this matrix\n",
    "    # [nb_capsules_p, nb_capsules, len_v_j, len_u_i]\n",
    "    _init = tf.random_normal_initializer(stddev=0.01, seed=0)\n",
    "    _shape = (nb_capsules_p, nb_capsules, len_v_j, len_u_i)\n",
    "    w_ij = tf.get_variable('weight', shape=_shape, dtype=tf.float32, initializer=_init)\n",
    "\n",
    "    # Adding one dimension to the input [batch_size, nb_capsules_p,    length(u_i), 1] ->\n",
    "    #                                   [batch_size, nb_capsules_p, 1, length(u_i), 1]\n",
    "    # To allow the next dot product\n",
    "    input_layer = tf.reshape(input_layer, shape=(-1, nb_capsules_p, 1, len_u_i, 1))\n",
    "    input_layer = tf.tile(input_layer, [1, 1, nb_capsules, 1, 1])\n",
    "\n",
    "    # Eq.2, calc u_hat\n",
    "    # Prediction uj|i made by capsule i\n",
    "    # w_ij:  [              nb_capsules_p, nb_capsules, len_v_j,  len_u_i, ]\n",
    "    # input: [batch_size,   nb_capsules_p, nb_capsules, len_ui,   1]\n",
    "    # u_hat: [batch_size,   nb_capsules_p, nb_capsules, len_v_j, 1]\n",
    "    # Each capsule of the previous layer capsule layer is associated to a capsule of this layer\n",
    "    u_hat = tf.einsum('abdc,iabcf->iabdf', w_ij, input_layer)\n",
    "\n",
    "    # bij are the log prior probabilities that capsule i should be coupled to capsule j\n",
    "    # [nb_capsules_p, nb_capsules, 1, 1]\n",
    "    b_ij = tf.zeros(shape=[nb_capsules_p, nb_capsules, 1, 1], dtype=np.float32)\n",
    "\n",
    "    return routing(u_hat, b_ij, nb_capsules, nb_capsules_p, iterations=iterations)\n",
    "\n",
    "def squash(vector):\n",
    "    \"\"\"\n",
    "        Squashing function corresponding to Eq. 1\n",
    "        **input: **\n",
    "            *vector\n",
    "    \"\"\"\n",
    "    vector += 0.00001 # Workaround for the squashing function ...\n",
    "    vec_squared_norm = tf.reduce_sum(tf.square(vector), -2, keep_dims=True)\n",
    "    scalar_factor = vec_squared_norm / (1 + vec_squared_norm) / tf.sqrt(vec_squared_norm)\n",
    "    vec_squashed = scalar_factor * vector  # element-wise\n",
    "    return(vec_squashed)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/python3\n",
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "import numpy as np\n",
    "from model_base import ModelBase\n",
    "import tensorflow as tf\n",
    "\n",
    "class ModelTrafficSign(ModelBase):\n",
    "    \"\"\"\n",
    "        ModelTrafficSign.\n",
    "        This class is used to create the conv graph using:\n",
    "            Dynamic Routing Between Capsules\n",
    "    \"\"\"\n",
    "\n",
    "    # Numbers of label to predict\n",
    "    NB_LABELS = 43\n",
    "\n",
    "    def __init__(self, model_name, output_folder):\n",
    "        \"\"\"\n",
    "            **input:\n",
    "                *model_name: (Integer) Name of this model\n",
    "                *output_folder: Output folder to saved data (tensorboard, checkpoints)\n",
    "        \"\"\"\n",
    "        ModelBase.__init__(self, model_name, output_folder=output_folder)\n",
    "\n",
    "    def _build_inputs(self):\n",
    "        \"\"\"\n",
    "            Build tensorflow inputs\n",
    "            (Placeholder)\n",
    "            **return: **\n",
    "                *tf_images: Images Placeholder\n",
    "                *tf_labels: Labels Placeholder\n",
    "        \"\"\"\n",
    "        # Images 32*32*3\n",
    "        tf_images = tf.placeholder(tf.float32, [None, 32, 32, 3], name='images')\n",
    "        # Labels: [0, 1, 6, 20, ...]\n",
    "        tf_labels = tf.placeholder(tf.int64, [None], name='labels')\n",
    "        return tf_images, tf_labels\n",
    "\n",
    "    def _build_main_network(self, images, conv_2_dropout):\n",
    "        \"\"\"\n",
    "            This method is used to create the two convolutions and the CapsNet on the top\n",
    "            **input:\n",
    "                *images: Image PLaceholder\n",
    "                *conv_2_dropout: Dropout value placeholder\n",
    "            **return: **\n",
    "                *Caps1: Output of first Capsule layer\n",
    "                *Caps2: Output of second Capsule layer\n",
    "        \"\"\"\n",
    "        # First BLock:\n",
    "        # Layer 1: Convolution.\n",
    "        shape = (self.h.conv_1_size, self.h.conv_1_size, 3, self.h.conv_1_nb)\n",
    "        conv1 = self._create_conv(self.tf_images, shape, relu=True, max_pooling=False, padding='VALID')\n",
    "        # Layer 2: Convolution.\n",
    "        #shape = (self.h.conv_2_size, self.h.conv_2_size, self.h.conv_1_nb, self.h.conv_2_nb)\n",
    "        #conv2 = self._create_conv(conv1, shape, relu=True, max_pooling=False, padding='VALID')\n",
    "        conv1 = tf.nn.dropout(conv1, keep_prob=conv_2_dropout)\n",
    "\n",
    "        # Create the first capsules layer\n",
    "        caps1 = conv_caps_layer(\n",
    "            input_layer=conv1,\n",
    "            capsules_size=self.h.caps_1_vec_len,\n",
    "            nb_filters=self.h.caps_1_nb_filter,\n",
    "            kernel=self.h.caps_1_size)\n",
    "        # Create the second capsules layer used to predict the output\n",
    "        caps2 = fully_connected_caps_layer(\n",
    "            input_layer=caps1,\n",
    "            capsules_size=self.h.caps_2_vec_len,\n",
    "            nb_capsules=self.NB_LABELS,\n",
    "            iterations=self.h.routing_steps)\n",
    "\n",
    "        return caps1, caps2\n",
    "\n",
    "    def _build_decoder(self, caps2, one_hot_labels, batch_size):\n",
    "        \"\"\"\n",
    "            Build the decoder part from the last capsule layer\n",
    "            **input:\n",
    "                *Caps2:  Output of second Capsule layer\n",
    "                *one_hot_labels\n",
    "                *batch_size\n",
    "        \"\"\"\n",
    "        labels = tf.reshape(one_hot_labels, (-1, self.NB_LABELS, 1))\n",
    "        # squeeze(caps2):   [?, len_v_j,    capsules_nb]\n",
    "        # labels:           [?, NB_LABELS,  1] with capsules_nb == NB_LABELS\n",
    "        mask = tf.matmul(tf.squeeze(caps2), labels, transpose_a=True)\n",
    "        # Select the good capsule vector\n",
    "        capsule_vector = tf.reshape(mask, shape=(batch_size, self.h.caps_2_vec_len))\n",
    "        # capsule_vector: [?, len_v_j]\n",
    "\n",
    "        # Reconstruct image\n",
    "        fc1 = tf.contrib.layers.fully_connected(capsule_vector, num_outputs=400)\n",
    "        fc1 = tf.reshape(fc1, shape=(batch_size, 5, 5, 16))\n",
    "        upsample1 = tf.image.resize_nearest_neighbor(fc1, (8, 8))\n",
    "        conv1 = tf.layers.conv2d(upsample1, 4, (3,3), padding='same', activation=tf.nn.relu)\n",
    "\n",
    "        upsample2 = tf.image.resize_nearest_neighbor(conv1, (16, 16))\n",
    "        conv2 = tf.layers.conv2d(upsample2, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
    "\n",
    "        upsample3 = tf.image.resize_nearest_neighbor(conv2, (32, 32))\n",
    "        conv6 = tf.layers.conv2d(upsample3, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
    "\n",
    "        # 3 channel for RGG\n",
    "        logits = tf.layers.conv2d(conv6, 3, (3,3), padding='same', activation=None)\n",
    "        decoded = tf.nn.sigmoid(logits, name='decoded')\n",
    "        tf.summary.image('reconstruction_img', decoded)\n",
    "\n",
    "        return decoded\n",
    "\n",
    "    def init(self):\n",
    "        \"\"\"\n",
    "            Init the graph\n",
    "        \"\"\"\n",
    "        # Get graph inputs\n",
    "        self.tf_images, self.tf_labels = self._build_inputs()\n",
    "        # Dropout inputs\n",
    "        self.tf_conv_2_dropout = tf.placeholder(tf.float32, shape=(), name='conv_2_dropout')\n",
    "        # Dynamic batch size\n",
    "        batch_size = tf.shape(self.tf_images)[0]\n",
    "        # Translate labels to one hot array\n",
    "        one_hot_labels = tf.one_hot(self.tf_labels, depth=self.NB_LABELS)\n",
    "        # Create the first convolution and the CapsNet\n",
    "        self.tf_caps1, self.tf_caps2 = self._build_main_network(self.tf_images, self.tf_conv_2_dropout)\n",
    "\n",
    "        # Build the images reconstruction\n",
    "        self.tf_decoded = self._build_decoder(self.tf_caps2, one_hot_labels, batch_size)\n",
    "\n",
    "        # Build the loss\n",
    "        _loss = self._build_loss(\n",
    "            self.tf_caps2, one_hot_labels, self.tf_labels, self.tf_decoded, self.tf_images)\n",
    "        (self.tf_loss_squared_rec, self.tf_margin_loss_sum, self.tf_predicted_class,\n",
    "         self.tf_correct_prediction, self.tf_accuracy, self.tf_loss, self.tf_margin_loss,\n",
    "         self.tf_reconstruction_loss) = _loss\n",
    "\n",
    "        # Build optimizer\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate=self.h.learning_rate)\n",
    "        self.tf_optimizer = optimizer.minimize(self.tf_loss, global_step=tf.Variable(0, trainable=False))\n",
    "\n",
    "        # Log value into tensorboard\n",
    "        tf.summary.scalar('margin_loss', self.tf_margin_loss)\n",
    "        tf.summary.scalar('accuracy', self.tf_accuracy)\n",
    "        tf.summary.scalar('total_loss', self.tf_loss)\n",
    "        tf.summary.scalar('reconstruction_loss', self.tf_reconstruction_loss)\n",
    "\n",
    "        self.tf_test = tf.random_uniform([2], minval=0, maxval=None, dtype=tf.float32, seed=None, name=\"tf_test\")\n",
    "\n",
    "        self.init_session()\n",
    "\n",
    "\n",
    "    def _build_loss(self, caps2, one_hot_labels, labels, decoded, images):\n",
    "        \"\"\"\n",
    "            Build the loss of the graph\n",
    "        \"\"\"\n",
    "        # Get the length of each capsule\n",
    "        capsules_length = tf.sqrt(tf.reduce_sum(tf.square(caps2), axis=2, keep_dims=True))\n",
    "\n",
    "        max_l = tf.square(tf.maximum(0., 0.9 - capsules_length))\n",
    "        max_l = tf.reshape(max_l, shape=(-1, self.NB_LABELS))\n",
    "        max_r = tf.square(tf.maximum(0., capsules_length - 0.1))\n",
    "        max_r = tf.reshape(max_r, shape=(-1, self.NB_LABELS))\n",
    "        t_c = one_hot_labels\n",
    "        m_loss = t_c * max_l + 0.5 * (1 - t_c) * max_r\n",
    "        margin_loss_sum = tf.reduce_sum(m_loss, axis=1)\n",
    "        margin_loss = tf.reduce_mean(margin_loss_sum)\n",
    "\n",
    "        # Reconstruction loss\n",
    "        loss_squared_rec = tf.square(decoded - images)\n",
    "        reconstruction_loss = tf.reduce_mean(loss_squared_rec)\n",
    "\n",
    "        # 3. Total loss\n",
    "        loss = margin_loss + (0.0005 * reconstruction_loss)\n",
    "\n",
    "        # Accuracy\n",
    "        predicted_class = tf.argmax(capsules_length, axis=1)\n",
    "        predicted_class = tf.reshape(predicted_class, [tf.shape(capsules_length)[0]])\n",
    "        correct_prediction = tf.equal(predicted_class, labels)\n",
    "        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "        return (loss_squared_rec, margin_loss_sum, predicted_class, correct_prediction, accuracy,\n",
    "                loss, margin_loss, reconstruction_loss)\n",
    "\n",
    "    def optimize(self, images, labels, tb_save=True):\n",
    "        \"\"\"\n",
    "            Train the model\n",
    "            **input: **\n",
    "                *images: Image to train the model on\n",
    "                *labels: True classes\n",
    "                *tb_save: (Boolean) Log this optimization in tensorboard\n",
    "            **return: **\n",
    "                Loss: The loss of the model on this batch\n",
    "                Acc: Accuracy of the model on this batch\n",
    "        \"\"\"\n",
    "        tensors = [self.tf_optimizer, self.tf_margin_loss, self.tf_accuracy, self.tf_tensorboard]\n",
    "        _, loss, acc, summary = self.sess.run(tensors,\n",
    "            feed_dict={\n",
    "            self.tf_images: images,\n",
    "            self.tf_labels: labels,\n",
    "            self.tf_conv_2_dropout: self.h.conv_2_dropout\n",
    "        })\n",
    "\n",
    "        if tb_save:\n",
    "            # Write data to tensorboard\n",
    "            self.train_writer.add_summary(summary, self.train_writer_it)\n",
    "            self.train_writer_it += 1\n",
    "\n",
    "        return loss, acc\n",
    "\n",
    "    def evaluate(self, images, labels, tb_train_save=False, tb_test_save=False):\n",
    "        \"\"\"\n",
    "            Evaluate dataset\n",
    "            **input: **\n",
    "                *images: Image to train the model on\n",
    "                *labels: True classes\n",
    "                *tb_train_save: (Boolean) Log this optimization in tensorboard under the train part\n",
    "                *tb_test_save: (Boolean) Log this optimization in tensorboard under the test part\n",
    "            **return: **\n",
    "                Loss: The loss of the model on this batch\n",
    "                Acc: Accuracy of the model on this batch\n",
    "        \"\"\"\n",
    "        tensors = [self.tf_margin_loss, self.tf_accuracy, self.tf_tensorboard]\n",
    "        loss, acc, summary = self.sess.run(tensors,\n",
    "                feed_dict={\n",
    "                self.tf_images: images,\n",
    "                self.tf_labels: labels,\n",
    "                self.tf_conv_2_dropout: 1.\n",
    "            })\n",
    "\n",
    "        if tb_test_save:\n",
    "            # Write data to tensorboard\n",
    "            self.test_writer.add_summary(summary, self.test_writer_it)\n",
    "            self.test_writer_it += 1\n",
    "\n",
    "        if tb_train_save:\n",
    "            # Write data to tensorboard\n",
    "            self.train_writer.add_summary(summary, self.train_writer_it)\n",
    "            self.train_writer_it += 1\n",
    "\n",
    "        return loss, acc\n",
    "\n",
    "    def predict(self, images):\n",
    "        \"\"\"\n",
    "            Method used to predict a class\n",
    "            Return a softmax\n",
    "            **input: **\n",
    "                *images: Image to train the model on\n",
    "            **return:\n",
    "                *softmax: Softmax between all capsules\n",
    "        \"\"\"\n",
    "        tensors = [self.tf_caps2]\n",
    "\n",
    "        caps2 = self.sess.run(tensors,\n",
    "            feed_dict={\n",
    "            self.tf_images: images,\n",
    "            self.tf_conv_2_dropout: 1.\n",
    "        })[0]\n",
    "\n",
    "        # tf.sqrt(tf.reduce_sum(tf.square(caps2), axis=2, keep_dims=True))\n",
    "        caps2 = np.sqrt(np.sum(np.square(caps2), axis=2, keepdims=True))\n",
    "        caps2 = np.reshape(caps2, (len(images), self.NB_LABELS))\n",
    "        # softmax\n",
    "        softmax = np.exp(caps2) / np.sum(np.exp(caps2), axis=1, keepdims=True)\n",
    "\n",
    "        return softmax\n",
    "\n",
    "    def reconstruction(self, images, labels):\n",
    "        \"\"\"\n",
    "            Method used to get the reconstructions given a batch\n",
    "            Return the result as a softmax\n",
    "            **input: **\n",
    "                *images: Image to train the model on\n",
    "                *labels: True classes\n",
    "        \"\"\"\n",
    "        tensors = [self.tf_decoded]\n",
    "\n",
    "        decoded = self.sess.run(tensors,\n",
    "            feed_dict={\n",
    "            self.tf_images: images,\n",
    "            self.tf_labels: labels,\n",
    "            self.tf_conv_2_dropout: 1.\n",
    "        })[0]\n",
    "\n",
    "        return decoded\n",
    "\n",
    "    def evaluate_dataset(self, images, labels, batch_size=10):\n",
    "        \"\"\"\n",
    "            Evaluate a full dataset\n",
    "            This method is used to fully evaluate the dataset batch per batch. Useful when\n",
    "            the dataset can't be fit inside to the GPU.\n",
    "            *input: **\n",
    "                *images: Image to train the model on\n",
    "                *labels: True classes\n",
    "            *return: **\n",
    "                *loss: Loss overall your dataset\n",
    "                *accuracy: Accuracy overall your dataset\n",
    "                *predicted_class: Predicted class\n",
    "        \"\"\"\n",
    "        tensors = [self.tf_loss_squared_rec, self.tf_margin_loss_sum, self.tf_correct_prediction,\n",
    "                   self.tf_predicted_class]\n",
    "\n",
    "        loss_squared_rec_list = None\n",
    "        margin_loss_sum_list = None\n",
    "        correct_prediction_list = None\n",
    "        predicted_class = None\n",
    "\n",
    "        b = 0\n",
    "        for batch in self.get_batches([images, labels], batch_size, shuffle=False):\n",
    "            images_batch, labels_batch = batch\n",
    "            loss_squared_rec, margin_loss_sum, correct_prediction, classes = self.sess.run(tensors,\n",
    "                feed_dict={\n",
    "                self.tf_images: images_batch,\n",
    "                self.tf_labels: labels_batch,\n",
    "                self.tf_conv_2_dropout: 1.\n",
    "            })\n",
    "            if loss_squared_rec_list is not None:\n",
    "                predicted_class = np.concatenate((predicted_class, classes))\n",
    "                loss_squared_rec_list = np.concatenate((loss_squared_rec_list, loss_squared_rec))\n",
    "                margin_loss_sum_list = np.concatenate((margin_loss_sum_list, margin_loss_sum))\n",
    "                correct_prediction_list = np.concatenate((correct_prediction_list, correct_prediction))\n",
    "            else:\n",
    "                predicted_class = classes\n",
    "                loss_squared_rec_list = loss_squared_rec\n",
    "                margin_loss_sum_list = margin_loss_sum\n",
    "                correct_prediction_list = correct_prediction\n",
    "            b += batch_size\n",
    "\n",
    "        margin_loss = np.mean(margin_loss_sum_list)\n",
    "        reconstruction_loss = np.mean(loss_squared_rec_list)\n",
    "        accuracy = np.mean(correct_prediction_list)\n",
    "\n",
    "        loss = margin_loss\n",
    "\n",
    "        return loss, accuracy, predicted_class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, Validate and Test the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation\n",
    "sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Init model\n",
    "model = ModelTrafficSign(\"TrafficSign\", output_folder=\"outputs\")\n",
    "model.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 0, loss = 2.42539, acc = 0.02\n",
      "[Validation] Batch ID = 0, loss = 0.800529, acc = 0.04\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.781535 Best loss: None\n",
      "[TOTAL Validation] Batch ID = 0, loss = 0.781535, acc = 0.0185941043084\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.891\n",
      "[Train] Batch ID = 10, loss = 0.754666, acc = 0.04\n",
      "[Validation] Batch ID = 10, loss = 0.753758, acc = 0.04\n",
      "[Train] Batch ID = 20, loss = 0.690922, acc = 0.06\n",
      "[Validation] Batch ID = 20, loss = 0.702244, acc = 0.06\n",
      "[Train] Batch ID = 30, loss = 0.6284, acc = 0.1\n",
      "[Validation] Batch ID = 30, loss = 0.659595, acc = 0.06\n",
      "[Train] Batch ID = 40, loss = 0.63592, acc = 0.1\n",
      "[Validation] Batch ID = 40, loss = 0.659709, acc = 0.04\n",
      "[Train] Batch ID = 50, loss = 0.616082, acc = 0.16\n",
      "[Validation] Batch ID = 50, loss = 0.599504, acc = 0.06\n",
      "[Train] Batch ID = 60, loss = 0.621295, acc = 0.1\n",
      "[Validation] Batch ID = 60, loss = 0.604651, acc = 0.1\n",
      "[Train] Batch ID = 70, loss = 0.617128, acc = 0.06\n",
      "[Validation] Batch ID = 70, loss = 0.597139, acc = 0.1\n",
      "[Train] Batch ID = 80, loss = 0.590053, acc = 0.14\n",
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      "[Validation] Batch ID = 760, loss = 0.427461, acc = 0.36\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 770, loss = 0.480728, acc = 0.32\n",
      "[Validation] Batch ID = 770, loss = 0.491949, acc = 0.34\n",
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      "[Validation] Batch ID = 930, loss = 0.376776, acc = 0.54\n",
      "[Train] Batch ID = 940, loss = 0.434811, acc = 0.44\n",
      "[Validation] Batch ID = 940, loss = 0.467765, acc = 0.38\n",
      "[Train] Batch ID = 950, loss = 0.483722, acc = 0.3\n",
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      "[Validation] Batch ID = 960, loss = 0.452141, acc = 0.4\n",
      "[Train] Batch ID = 970, loss = 0.47038, acc = 0.36\n",
      "[Validation] Batch ID = 970, loss = 0.407389, acc = 0.4\n",
      "[Train] Batch ID = 980, loss = 0.442751, acc = 0.34\n",
      "[Validation] Batch ID = 980, loss = 0.406002, acc = 0.48\n",
      "[Train] Batch ID = 990, loss = 0.506468, acc = 0.26\n",
      "[Validation] Batch ID = 990, loss = 0.434915, acc = 0.42\n",
      "[Train] Batch ID = 1000, loss = 0.497463, acc = 0.26\n",
      "[Validation] Batch ID = 1000, loss = 0.4204, acc = 0.4\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.420879 Best loss: 0.781535\n",
      "[TOTAL Validation] Batch ID = 1000, loss = 0.420879, acc = 0.470975056689\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.8019000000000001\n",
      "[Train] Batch ID = 1010, loss = 0.496853, acc = 0.24\n",
      "[Validation] Batch ID = 1010, loss = 0.409362, acc = 0.62\n",
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      "[Validation] Batch ID = 1440, loss = 0.338345, acc = 0.48\n",
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      "[Validation] Batch ID = 1450, loss = 0.27971, acc = 0.72\n",
      "[Train] Batch ID = 1460, loss = 0.25103, acc = 0.8\n",
      "[Validation] Batch ID = 14