Traffic_Sign_Classifier-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 0: Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "import pickle"
   ]
  },
  {
   "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": {
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evQXNSgmw6BwrScLHiLWmX8a+HCkF3CN1ztNKpER63Y/YONXix+7Sir/OjRke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\n6rTvJGmrjfDsjYHtfrZMe47nlCzNHnbbHvnSbbTvj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18c555a860>"
      ]
     },
     "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 0x7f18c55942b0>"
      ]
     },
     "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": {},
   "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": [
    "### Example of augmented images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q1UU58JKkWAGjh7I3zeGMUMdEHRwcHPrAMUUs7czVtofM76xb1HyEykBNScnq0Urrh8U/\nT5mj52s2mdJMRX+4jLHf6h+X61SpG7VG+6MznvqjFXVDvQz9g81J024Hizdfx3Pv/ykAwBwf28y4\n6Co3OiKPUyNi7Z6fEx2lof9om8yzsy6MtDZMXWpdrPC1U+JfCFMp3FdjqgPVndFtw2fm8wbbSTr0\nb6wLY91si/+ijcgsO9Kfak38VNNEVxx6p12U+I8BxC6guk8dd6WTNLbdcls6oTouzz0hc1Qrf8XI\nCsD48qCVgfbuzeurshJI6ccb069U/UY1MW1WinTqjbqHOw4dE3VwcHDoA0fORLdaOB90XujVny/m\nA7Vqnfd1ZuIMlzNT9RdF4To9ov6nPeu+bLVKoTJNtTJu1+UWd3tl1AfbOu9XKxi/dAlf+X8k0uj5\nZz4MAFi+L7qrLmOf28tSRdOP6BfYFUbYYTYg1Wk1h4V51pqi8wRj1rOqxuTLa6veGar76rJaZF7v\nXLMxRcJ4K4yYCQPRra7MXQMApAGpMyPZLFcsaR6h9Jg4/O4AC9tbsHGrTir68QjqZJSsT68rQN33\nKvRuUG8YTzPZy8qCJZe2M7rSfX1WKFBdasqVTJr793IlohGKXEk+7AwHjok6ODg49IGjZ6KFdKI7\n6yp6GtMi01SjulbZVF2o1cikPO+gnJcwS0yiF+aMUmPp1a9TrfGaD5H3Vat+qtlmwOu8rd3PdaPl\nSIkd/+YBRNTu4u3XrmKVMe33qhKRdHNOIo4W4qJOMVpmzSTu+3z+1ZowxCatvhUynDTPFylyUD/e\ntCvXRSlj3KmLW1+V+4b0/1xfF4Y6XBOrsOZOyN2FtfXSe4NSFqdYQ8aPN/fZ0SLreTl7Wm2XMe+q\nUq40RGcZKKXML9ABYor7ObzC+Vlpjbgt8z0jnLrcpjmzJePV90RLQu21dDik7GqOiTo4ODj0gaOP\nnd/y9e/pDHez0u8cY695RJX5eR51ntSVGI8x8oH8HpNCZJY6U9Wp5UxU8xnSCs/rTSnGvmcMVJ1s\nkeFu63/uZ4qBRhRFuPv2bSSb8hxvXr8BALh6S2ogxYyJjzXChW6H8aboRqu0pjeZlUl1X4lVufFG\nbWkng+hSow6ZqGaLiuT4+oJU7wxVR+cJQ4qZPcimzDfJ+2b0Aw4SiQVP+R5pxBJKGdLVyjzosBaI\nUyBjJFGodeDpp+nTah53VcepkWWlyK9tXjjFFeY2qMDVD5srjYi6c/VHtZSb+hOrrSLatTrwlj8M\n6Bk9+sSAD28HBweHh4tjiZ3P/c52C6LPZyplht6Wo8iVWD5nSL/WKF7taeytxvwyo7bmjdRmdMYk\nYwkYu60zWl4NlPe3WdFa6+Wxwzvrdrd7AQwm4iTF3YVV1NryfNYYG393ibpJekv4jPxJN8gkOhLB\nUm+wfnlN/EKjdWGSmhk9IyPdXKR/aVV0p1FX5NZh+2tkSIGn2X7kyU+MF2P6Yxrv/a7crxIzsqki\n75NmsE8S9f9VXTox6EuLLbDW5v63QV6/lVZx6o79ijJSYfgVHY+60ivZMHp+m/ldCscNmabqXjta\nf16rfZpizoKybtvXvKfb/5ittzk0PD5vg4ODg8NDwJ5M1BhzAcD/AWAa8tF/wVr7vxpjJgD8ewCX\nAbwN4Fettct7tWd32DGl/Jx5hJIGAKlqcrcZJDfSaYy8RhzpViOVmAVIre+8AVVwiKg7tSX/UyTF\nGVQ7ktBqrzWftPfeNivkyeOihynXLMnQXt7E2rzUPtLnst4SHWXI5zXUkG1cqvtuqButVYVphIyh\nXqYV32d7m2tiZffDiOedZwcoHzLQyTPn5Dw+9lqN/qZdVcbKdjUWhhNypdFmtqYkK8rLL3ENjZQ6\nidkRDne8Wlib9VSH+fjirxxfWg011TyhLZFTtSEriZBeFxmVzMZXP9LiCk79SJO2rAxi+vemdAOw\neSJh6UBQ48qG3hZaGyvKdlaK5vcpLRH7NdLvh4kmAP65tfY5AD8B4L8zxjwH4EsA/tJa+zSAv+S+\nw6MDJ9fBhJPrEWNPJmqtnQUwy/+vG2NeA3AOwGcB/DRP+zKArwP4vb3aM70I8y3Jmko6RVOcIvKs\nSSWrnupUPRSzx+hxZYh55JJOpIyNzyNb8rmkeN/cT5QzZl7zxRSzxuQ6n21U2exy/PhxmHK1WYLO\n+gJaGpNORuDz9WqT+cXM1jQUUGfJeuMBdWghM8lvtIXZtMg8VUc2NnW+cN8klXZPnZKsTSPM5lQb\nKlaZBBlNlgjjbW0yX6lhvklfzk8Ttd7z71JrvL4/eaQccyicwGqfhzpeLQCbIaV3QouMLww0Z4Hs\nJ11dWejzkb1II/s43gJWYW2vynsQMrJJbR5KcdurzNJUzs+ruTK4EkjYH/W+yRnoDlWVgB4DzQMI\nSyvhB8WBDEvGmMsAPgjgmwCmKTAAuAdZPux0zRcAfIH/f9B+OjxE9CtXDcNzOFlw4/VosO+PqDFm\nCMBXAHzRWru29QFba60xOztdWWtfAPACAPi+XzqnGMu+W/5QnV+8Usy8Mky1EvZ0q8oclTHSWq+R\nRuVM3Hsa7fT84nW7Z4UptXSC684fhlyrtYZNIiBOWZtKvSBoVfe4H1L5nEealZhcrtOi7nJkVMa5\ndqA5fUHvDgCIeb7HTOZeWhSo7uv7s7p0V+7vq7WZNZfoJxyp7ttGW5vZwoBkuOwSmHaicBhyDfzA\nFt9ljjfqQnP/bC7o8khAtUXQy6GtcmKuhJT14uNtA65oW1BKa+iFo0purZG20aG1vpysovcUdtw1\nvQu47c++vq+rjdRl+AqAP7TW/jEP3zfGzPD3GQBzffXE4cjh5DqYcHI9WuzHOm8A/AGA16y1/2rL\nT38K4DcA/D63f7KfG5bmmh32thE+eFo/vsxcbTH/Z5rn+yxuPb9Yf94wKDcj80hYlTKnGL72o5Th\nvjzTbZsB94jEOEE4TLnaLEPa3UDGFYFGgOXpc9Sa6ml2JfkhYJXNdVpzMz745rjEzvujoqs0gUYy\nyfn62KuhMKGY74E2XzFaxpPdSClH5qFs8X5eKO1Gib5w4reaR6Kpf2jujUFd7wkurXTY4xWFShTK\nRNVmIEe1NlJ+hfpVM9Ioz4rVJcPP/a5LfuK2+HXwasyFkY9/9Z7ge6Yx81rZIreFqL+3tBNoVjd9\nD3TFWvpLPe/B3Ob3c9UnAPw6gFeMMd/jsX8BEcZ/MMb8FoAbAH71gXrgcFxwch1MOLkeMfZjnf8b\n7E6ufu5Bbtqzxm//BejFsPdUiaWIhzyIXZlfMRO5MgWPmdDzuvW5/1gpEsWW2tHM9rT+5VMuZzS1\nCu5Ww+VRiFQ6TLlam6G71fczr7WjzEJu0+oIg2hU5DmGpI4aOZZpJFGNOlRPdKNBXa3tiqIuNSyt\nCFJGPHUZa99lpMvGuqxgPRrCNlQXGhdjW/LYbr4/6jWQJUX/1jQ9eTWXDn+82i3jVccJnzNXHm3q\nvCvUGcd5JQh5fhUWU9USVTYomcdzoqu5MOhPHOv7o/7Y3a2n5QO72pTIM8NxmabqZZEW2os00kqZ\nNJvJc2k8YHouF7Hk4ODg0AeOJTPi9pjZvS6gv5laSTXSgcQyY0SLIUPxA9axLk0RqqvxS/dV/0+b\nql9qsXZSHiNf1tXmx9/ZP23QYa1FkkSwqWaEZxYt/T2v0VO6Ts/QmlX00+xuSl7SlVmJXBk+dRoA\n0Jw8K+2rH2pT8oN26FcYagZ1tr9wVzLXqy4zYNYhhW/oL6xU1sr1Q40JtlPs9ybrn1vNL3uCvS4O\nC+KBUl5bFf1B1Y8zj+DrGRFkn58ZtU3o+Iu5AvBM703ZCvXWUOQ2Ceouw1AorrrYqXxtwsz3ud8v\ndfL0L41i0c3G+UqiPzk6Jurg4ODQB44pi1P5y//OQfE6saV5DDytcZrXUymi6iqZ/cXjn6e6zvwu\nZEppqjoS1YHu0i+dcdO9qj6WMueXqfCgwspiIS3pQNNtbnqMcOHzqVEHFXDrp8JI2sviT5hQTusr\nEmGkbpwZvSnqIyK3uTvXAfSquOa8hipMX3WlgcTQZwGZKc9vMlYgpW58Yuqi9IM61eW1Of5dqnSn\nl0CeAX9AYQCYXlW0cvVTWwr96fnPFiONIo5HozWQ1LagXhHQfL56veosi5GMvTSg9MZhe90Oxxkj\n36pVWYlWNLsUa3rFmjvhkPGYjHIHBweHh4NjYqJlFLOz9NL+Fa3dNleC8vfc79MvnJ8yVttui2TS\n+6k/mdZoKvWD8Mk4Uqg1Xq3Cau5HcV91qGTIj8sMZWGFNZas5OWIroQcsR3r8xeGMMJY+hSkhKXq\nqElHmMd6LFb3mP6HK8vzcjtLP8U2/RL1/WmILhU+Y6wD9Rcmg6Q/4dnzV9gfue8QvQG0ZlBqRPe2\nypVK3FnmnznYxZasFY+WnjjKaY+KK7Oef23R+t0bCfTLVl2n2jh2yzFR8s7p7Z4sv5fHZZw7ODg4\nPBQcw1TqoecfVgpeR1HHsmtseq4jzYNhZWOKfqBpKTa7N6Gqla9oFfRKkQzKeHaNkDclxqlWxty6\n/5ghz22gfrv6BIq60lxXRmbXodwMKxD4udlXGKA1ko/SkME0Q9aT15VLKNb0oMRkO9Vq4f4zU5Kn\n1CuVkUyiRQBAoyF16ZO8YgKtvOut8h9aaHeQYfN/0KuyWyaku9o0yuO5BF0JaiUKZabqJ54VV6YP\nD+qdo/lOD5ZQxzFRBwcHhz5wPDrR8tSyS/am7TN+0V8z16Fyhgw1Rt7fn85EY2lTtu9rvtFtutKd\nUXKH21a//nFCIcJ6l9pZ+ljUOy/ggTWtI84H2QxlW2fMfFet8ZRPh4lhPV8YqqqqY2Y61+xdE9Nj\ncl925+4d8RtVv8R6XV7/alPOH6rPAAAirdGzS1XPsCbtxp2VHX8fHFgYWJTrt+fvd75Rhlo6r5wm\nrRyhtAfU5pHXu8/zCxe9a7CL0b3KUKmw0tj5BCLuM/LMMVEHBweHPnD0THQrSyv5heWHt6mcdrb2\nllMiJrS25uXiy1UZy5nyeXm2baIsz6zFmOGde40eMy4ffxxQkOvOOQVMqbhNnuTJbj0KbNJ6n1jG\nzpORxqrzrkisdMZ8lqoKnZqW7E8ZdVxv374hPzDSqMIbLC9LXtFOJAemhiRPaVySXKUqjHViaqJw\nfGPpDh4n9IZpSVD6u44bzU1hTHFbvkB10rpg4bjV98FXbxvzaFgVHBN1cHBw6AMn0tHNlhjhDmcU\nzitHSPSqg9LqvkvkQ9knQBlpr1pncWvLjEp1M+WiLY8jtkS2qO7KlKz1KHlLaB7YvHIBGYhGmqgf\nodccKpx37uLTbE+tuHK/+UXx3+wwNnp58T4AwGeEi9YECpiHNKIubW1Ri15K5NPYqScAACmG9/GH\nDzKMsMmS/+9j/Z7vAMdEHRwcHPrA8TLR8oS2JwNVlLPI8Kj6bRqNYFLdp1r3Sq2UVTXlyAxuvbza\nZ9E62JuXD2Z1HES8k0dC/rzKmcu1/njp/KCqVlWJgT5/TpinKWWUT1kjKaJ1f31dMtNvtCT7UzWn\nCIw8UyLMPJVB2F/ki1rpsT77zic6PBQ8LCt9nqVrn3BM1MHBwaEPmKP0aTTGzAPYBLBwZDc9OE7h\n4fXvkrV26iG1fWxwcnVyPUYcu1yP9CMKAMaYb1trP3ykNz0ATnr/TipO+nM76f07qTjpz+0k9M8t\n5x0cHBz6gPuIOjg4OPSB4/iIvnAM9zwITnr/TipO+nM76f07qTjpz+3Y+3fkOlEHBweHQYJbzjs4\nODj0AfcRdXBwcOgDR/YRNcZ8xhjzI2PMNWPMl47qvu/QnwvGmL8yxvzQGPOqMeZ3eXzCGPM1Y8xV\nbsePu68nGU6ugwsn23326yh0osYYH8AbAD4F4DaAFwF83lr7w4d+8937NANgxlr7kjFmGMB3APwy\ngN8EsGSt/X2+OOPW2t87rn6eZDi5Di6cbPePvpjoAWaq5wFcs9Zet9ZGAP4IwGf7uXe/sNbOWmtf\n4v/XAbwG4Bz79WWe9mWIkB4rOLkOLpxsDx8P/BHlTPVvAPwCgOcAfN4Y89wup58DcGvL/m0eOxEw\nxlwG8EEA3wQwba3VjBL3AEwfU7eOBU6ugwsn24eDfpjoiZupHgTGmCEAXwHwRWvt2tbfrOg6Hjcf\nMCfXwYWT7cPoz4PqRI0xvwLgM9ba3+b+rwP4qLX2d3Y492MA/kff9z4dhjuUIy0ne33AR2BKhe2C\nvBQrU9kxF5qeFValL2GNBc+lYSldAAAgAElEQVSMpEzb2JSUanHc5fm20H6ve+W0zu+MTqe7cNIT\nVTyIXOv1yqdHRpt5Bbq6Vq7uSnLktUhSi3VYPsJjSdqwyuceyHPfvUxhOQl38bmb0juc7VJy2+Ql\neYvlXsopuFMmdY66LJWs/Q747rKiYRTJeUkSn3i5AgeXre/7f1etVrbLxRSf27b3vySPSijjMAiZ\n+pDjMmYJ7DRmCjpeFwbye1Kq26PyykplfnL55akwi/3KU2Zu6/c7Y3llbV9yfej5RI0xXwDwBQDv\nM56Hy09d2naOx6p+WZ5yXh9escZK76XXeu98HHzYeXVACmdiRGrjNDh4OmtSnbHCvJQzl6W645ln\n3g8ASINJAMDffefvAAD3b18FAATMW+izfc2knlcJZft5zUC/lPiSg/D1H169se2Pf0SxVa5hGOA3\nfuvTyBbkebyH3CB96yYA4Gu3pabRG23J+1kdkdXWmad+TE4clfc0oTh1EOgyKcvkI5wkzAvL98Lj\nGV4aFfrW4e8RazTpaxKwOmg1kMz5AROU+jyhwvdufe42AODmte+ywTYAoD4h/TSjkmn/xh0R59y9\nu4MoV/i+h2ff80w+CvOPHPPA+iooq5ULeKLWOuOBczMjAIDTU6fk+obs31lYBACscZt2Rc4zU2Jc\nX+T7Ykl+fMqrm0i7nY7WzpJ+VKvSrwo/wvqx9lkdNvEC9n9/z+I/fuXP9yXXfj6idwBc2LJ/nscK\nsNa+AOAFY8wvBr7/Z33cD0jJMMkEtERyTDF3ExGC4f4qmUTE8xKW9aiSDbcDSdZqWI7C8CHrDGih\nH3cRYpiXvQDvY7Z2C4YvlX5kdTJIdykhfEJxYLnWG9X+5HpA6MdTP6bwJTlz+WPqsA17ylblCgCN\nZsOm1uYfx20MTuvSadkVnkBxoFoROX3yCSErzcZw4YSnajLeNsckWfJSSya9iZr8rgWp51ZlZbi6\nIR/NmEmTldSAH/V2Uiw3U+HXLcikYzVPydA+v6L7RD860RcBPG2MuWKMqQD4NQB/utvJ1tqv9nEv\nh6ODk+vg4kCyddgfHpiJWmsTY8zvAPgLAD6Af2utffUdrwGQJVtYWa4L5TItLepAdJN/6XXZnzNA\nOUFLJadM76+6r1YsM1fqcflWl221Jgw0aMrMGFZlJkxjuS6OU/0b5Ty2Vy6gp8v5DJzZVK1gimUw\nttUlOcF4ELk+bHgsjRwExWX9UcNGsuxsePL+NIeax9KPB8UDydbm2qh8+a6r+Hoo4+IUbQrnh2Qc\njY7Ifq1G9dvsEgBgNZGCgKq7rFIXPsXl9hjHJe7MyT67cGlY2l2ZliM32vIe3GtRJ83xtbImeqSq\nqvt6S0beV/6QwD9cLWZfrZGFOCYyYHByHVw42R4+jrZQnS0b7sjkWEBMGadRhb9PxpkWmYdaX7sJ\nGSOnykqVujFVKNMaWBsSxllriEFgbEgU283xGbYnMyf12kgtrcVWFdQ6lWnhu+I27xdnxF7J5eLf\n5bA/5MS9ZGA6MrREB6deBu1IDEsbK11uhVkNTZw+6p4dPYzZMmhlO1OXcXH5lDDDp06fBwBMWVkx\nJF15Xq0NqdqxubJRaLKTiURTNfiw+VALxdF7w3boHUPD3tCQjO/Lw2J4CoZlJbCRyHlRTNuG2kZU\nZ2rKXjWCXqnz/uDGt4ODg0MfOFImakzR+0cZh0dre1Dy49KSqL6fm78B9GYQZYiVisxQE+NiePSr\n4tqUkclGtNpt0iUipkvGWkfsf3fvi26lUhem2hgVV6f2uvxuk1X2SEupUudCq58tWS/LjDSfER0K\nuHftZQA9V6eg5Or0sGHn7wHo6djb1LXeff0HhfMCiK4u5QonbMj5G+sFP+8Bhc2ZFo3t+Ni0MPBL\nw9Raqr/shjyPbGVdtp1NAIB/ehQA0FLzfVAsbR5wpel3ZJxOPydBVEtvi4dRZ4F16NZl/DaoW52k\nTrpJRhsNCRNdo3uvl1vvi1v9fthDGpdudDs4ODj0gaPViRoUqKhXikDJmSmt9UnJqs2JC1Va14YC\nYaB2hM65zIDVmqd1nJEn6k/o+6rjFEaZJhG7JTouU5U5ZWhSZs7J0xelHyviStfqCCNNM/oj0n80\nj4zgTJdHOKkvsqseAAB4F70j3li7D6DndP+wMSIqNWSZRiAJU7rxvX8AsPU9FHT9YmSbRrqMnZOV\nTpoKw/I6fA8WVzC4MAD9QPMVVizjJyYDTZfluaZrwhCtBkfURbd5/h9JMc4Ow1E8T66r8sEP+TW2\nK+fPvO9DAIDWbRmXs6+8BgCYe/UVAEB7VY6Psod1Oteno2LraHEFm3XlfqGOS/WyIQOtlYNiHhCO\niTo4ODj0gaNlojA9SzcAqC6Eu57RWFh+2z2ZAZO0aK6t1UR3aavCPFdborNabAvl8DOGpeX+YrKf\nKnHM49gybuS8pCX7nS5n1GG5bmLiAn+Xua+zKmGMHshINFZcdS1knl7u76o3fjzwqhACvOeKMPlP\n8biGfz4sVBirry+1+u/e+O435MC2GHux6ia5X6/Ib/IcwzvzN1MY0iaZVEQ5p5pzYUBhYOAbD2Eo\nz+fSkIyzKfrtdu6LrjLpCBOtMwKpTp3p5OUzAIDmu68AAOymrOQ8K8+xEZLhD4luNazL+BqblGRR\nE9NPAABOcf96Xc679m1ZQWB9HgAQDYucGrGM/5pGQnGlkEa8H1dCARlomh7OCtExUQcHB4c+cLTW\nefQsfMAWP62sGBMfaEQSZ4pqoAlG6L/JRCF3N6g7ZZagGn+HUarJrC/G27qb68CyTO/P43o+95fo\nFxjxwKkJYcDwhQFvLjFBgmYNijV2v5hdxjdHZG4+LngesnoV3il5XpqI5LBRJbMI6E+YaYQab3f1\nu38j3dELeDxKhCl5JTmMTQtT6mX5ka0/IowlY0aZOGZOBTpndHmHDv0TBxXGWFS8FJeHhXE/NyZM\nMNmkNb4j1vj6iNgmzrz7KQDA2HvEuu6dFz/sDUYmNRoaWSjXh4x4qtZl6VIdFq+aP/yaJAA605T7\nfvziuwAAl97/CQBAa0MEM/sdWWHEm7IijDnuG0Ny3VomzFRzalRLWaRWeV2/cEzUwcHBoQ8cKRO1\n1iLuJnlqKk0hlxPF3L9S9kPmrAvqwvw6Vrazq0yNxe5XaU1tMla30SzWqWoz8mh1jdb1qMvrVfda\n6mfOUOW6zjojMDiDDjUYmRFITG8r3iz8HWoNDDXQyfmJHgiVtsgnyFOtiXyvf08YSpopE6XVmL9H\nlENQXOBglP6n6j2hfsnZsOjg9DZhhanwqBPnAgdxV3SBp04JU4qWae5PD4fJnFQExsNEvYp3jwsD\nvdKQv7+78BYAoD4mfpqn3vu07D/zrFw4LTrMLr1o/vab3wEAPHtZdKWjo8IUYyPj6IeL8jxnX3sD\nAPDKD6SM05t1ee6r98Sf9xMf+Kg0/x6x9q+9Kf3ozsv5YSRyHeGKdJ7+42srwphTvi+VOiOjVMB9\nwo1uBwcHhz5wxNZ58TXzmMlaIwhyYzkZmybPNU1J4rpiZUZa21BrvmybgcwsM6dEp3Lhouhgqrzu\n7XnxB5x/600AQNIlU8mtrvQnI/PMI4z4q0f/uCZn1BoVuimEkXYZU22szGhD/F0zrevfFaXHk3Xo\npOPsOZFXGApjiFuiu7z5w2/JCXnaLHl+bVpjk1Ky7pRLgOaQrEAq+gMF2ZgQHbpl/tiEcl2LVYfG\ndiA67kzzVUJ18NJ+u6v+oLRG1498+BwpKr7BxWYdM3xA6aqMH6occfZ97wYAeB/6SQDAa5TTrZfF\nr7NLb5lbdyTJtdcS/853PSu606QpDPevX/4eAKB9b5F3lhXeL/7cLwAAbqxIVqe3jaxQnrp4Vs46\nI8x2ZV7Gd8jPyhDfjyqzL+v4TjU7m08/1vhwxqVjog4ODg594Min0iDwoGbTbl7WQ2YK5Yc2lKmu\nk1DnuEndF62xZ4fk23/2nDDOCxel5EiN+UHbifxZnTuiS+lGkkXG84qRKGlWzBOaK9HUep9bYWXG\nCjlzDTHvYaVKKy5j6vVhqleo+h0OuG1+T7xcEYZ4N7oGAGgyR8HdOxIJltwThqnutBvL4k9afm7j\nY6KTC4yc2GH5D1sRuY8NSQSURrxp7oW5ZWnfGxIdaEy/T7SEKaWaRiyQ90v9i1PN2O4z0oY6+jq5\n7qAHooWeh/NDDTQi5pxYF13xxHmR55kPiW7y5qlnAADf+M//l1x4X5hnHMtzevrdHwQALN2UiKN7\nq7JCjFscfwvC8D93Xsbx+3/iHwMA6s+IVf45PuhOd53ni22jOi3yNK/Ld6LCiLQa5Vah9d/mb5J6\nWch7ER9Snl/HRB0cHBz6wNHrRGFz/zudBwKqJkIWmtJsOa1IdVTUgTKb00feJ1bAkdPCTBLWTLK+\nMkeNmRemMsbIozBR/1OZIdc7jIjSvm2LYJD7pvQTbUfMW8q8iZWazIBZzNjhtFjjRTPce4cUGXHS\ncaUuz2NtSHRWixCG+LevfwUAsNGVJzNCnXJrQ5hgXuCPb8TwsFynz10LlL33o2KdrfhyfH5RGM/S\nsrwns3elMJ76fWotnc6S6NrMhjAZE4ouzsayn0KYcTBEuWqhAo4Oy3bafB81gsn4gy3XwHiYCCrA\nOr1ZaN2euiwRSJ0nhCm+fU2ey2XWSPrpH/+AXH9eCkC+TWeGH9wRa7rh+FQ3ipjjaXJK3pv6U9Iu\n6N87RC8YrcqbNKmbnWThu1DeF8OIpbqnOTKkGa2AYbnWjbnyiNLDiSR0TNTBwcGhDxy5n2i6xSKm\nfqJVZmFpMnJhk/5jGWNsRxmzOz0mM86pCzJjZaZoBbdqTa/IDHdxWtqbqstMOca64T51dHdWZUZ8\n+7ZY/9bX6PdX1o1yq04FWoupQqtylzqXmDNdXtqF29Qf7Nj5hg98cDjAcCQPqj4mOuKX26KTXrwh\n/n9NQ+s4s+sMNcXPUHXPWnl37FlZadSMPN8GyzaePi+M8eb16wCAO3dmAQAbZEoR/QHzeuQUQC2U\n+2YJdaAsYaBW+vYa++3L++UxO9jwlFb5EaR8Edqb9DscbCIKzxjUgwBpW8aFWrsnLl0GAKyOig56\nvSW67XGOhyffI/ILz0vk0tvfFj/OzIoNIWRo2CgrTlQZO//1N0Rnnk7+NQDg8o+LLrXG/lS5AqjS\nv7fGKr2+fi+0hDkjpOJI5NtlPmEtiW0Yspg6Jurg4OBw/DhSJuoZg5rn5dZrMPN8wBksrIjOo0rG\nMjYlM86TUxJxcnpGdCAZs8ikjJX1NOM9qYHPfKFjozJTjTA7TJOxs2HIGkun5T71Ydl/5Q3R2Wwu\ni64soE4uU4pki0HWYUAvAjKXOKXuyKoXgIZiDfZcFWQZJjc38XpH80zK3//ay98HAIxrvXHCp1fE\nzMc/AgDo0vthc1O8KBLqsEfGJYvPKHWgL/2tlLe/d0OYT7stzMimIo+hpvidaiLzJJEVSG1GGFNn\nXXSjCRlrSF1bRl1na062G1zbbDBSbfzK2WL/U+ajrAy2n6gBUAXQ5TioMoKoOiYrOcNsaknM6p0c\nHgn9pf08FEzGl2U2tRptGFMaSZZdBQC8tipeFLVbPwIAPPMT/2jnjtGPPOEK0GoEG78nSUi/1pJ/\ntnphmEN2qxjs0e3g4ODwkHHEme0NvCDIY53zwxXqNAJqP1jXe3iUGeanJDKhMSQzDEslIWCsfKwz\nIHWoHeYPXN9kvkhm2m5zJlQrf9OT885MyP3bT0hWn2uvy/H2WrGGjk5gHmPqbSb9jBLWy9b8lJrF\nyWo10sFWnm0mKV5cWMb1RWEcs9dFBwbqqt738ecBAGsttcaLwM59SBhqTGb30rdEx/lsVRhiuiz+\ngNdmJSLl5uu3AAAUG0K+L0FN2hl94j0AgFqN+S7bsqKoTwoTTbm/NidW/KFp8UvcuC+1fFZm5bif\nilU4icV/cXWV3h+88ShXHl4y4JFo1iJJkrwcr1/le86Vo8k/H1wJao00rU+v7VRE913TJQIjoNZp\nW6hznPzkuy4DAH7mZ/5xoRta4azOcVXpUtfJWk42kRVM1mRe0bsizw9cEv9VpHJ89p54c1S4gj0s\nS4Vjog4ODg594Mit8504gdrTfVrJMs5QLUYaLW8I1UxSsZqDsa+nN0XnFa1K5MTp88IwwjFmhaH5\nfJExvm9eE+ayvC4xu5aRRmcnxb/0XROyVSvx9IhsV0aF2dxRK2xuhmV/1dFVs06VrPGWfo2av1Jj\nsQcVkTW4mVRhqsLELzwlsdSmK8/9Ez8nNXO+9crfAwBmN0U3efW1vwQANOjnF1In2l4X5re6LMxw\ngRFHimBY3oMRyjEcFt1a7bSsWFQFPUTdXaR1x31hUEM1WeEsLktm9tFxqZs+1JX7MzAGcVfeQ4/+\npY0R6ec4Y77XWH9+oGFM7v3ilbKRZQkZKpldnVbxSsiaSTwvYAx9SCafUie6QkapNob3XnpSbnlK\n/FD/03dfKdzvOiOh3jstcnj+kz8LAFi7LSuY+Ze/BgDYnBb5LKifN63zDfqhW41oOyQq6piog4OD\nQx84cvOiRS9zvSoZU5SyrTCyaGFJmM1yS6pDLjAyZa0l26kVYQ7vuiKMtDJEXQ2VpH5GXeumzDwR\n64TfX5V26xuyvXiF2Z9Yw2Vykkx0Vq7TyJRepnqNpNGYeWmnrYwzLwmlVwx29LzNgLQDvPtdwiQu\nTf0MAGCsKc/j//1P/xEAsDT/OgAgDmWlEK3LcxxlNqQLTdFRYlkowjJj6DdYSNwakc/MFYmEaVJO\nquuOMvXzlU0Qim51eUUYpzLHlBSktSr9y8hoh8bF+yOJRYCtTfFz9enXWqXfasqY/NbAZ7Y3qAWV\nXoVerrwS2hzQlfEUMJtaTCt+wMglMK1vANY+s8x+VdXaVDKu3/OTnwQA/O8vSmWCj90XuX/9hmx9\nfg+ePycrhqcuSu2uG6+JVX/ttqxYIuYP7TSk/eWWaFO7zDPLZGxImV/YMD9wv3BM1MHBwaEPHC0T\nteKjlWpRHK26lwcIqXKRMwX9M9OOnDCnVjh2+/6y7G+0ZEYcY17RkIyyGxWrhPqcgZKutHtvTmaq\nkQnR4UzRalitq5WRlCbW/rI5+qNpJFJqZUbTSJmKr9ljGLM74EbcWq2Kd7/rSUyeFSZXaQoDXG0L\n04jb8gCePCf5J99cFH/cDv0C66zZM9YQJnFzUxjGOr0jgorkA601pepqldm6EvXSqDCP5R1huhVG\nuA2Pywrj5tsS4ZTSj7dSob8jGdFaW6zwQ+OiK/WHqLuj7p0qNSTUAbY36LUx4JFoxhh4YQhDL4uY\nOsz2sjD6gDrlJqto4tlPAwD+9Z/8OQDgZ58SeVy6LCuHr7Ma65Wbott89qPitbFeE/levS0ryj9/\nS+T1U8/J+3KqKfL68MX3AQBaS/KevPbyNwEAGyuyYmhNiE1j1cg4XliS/kXUdYeM1ddIydyhuE84\nJurg4ODQB47YTxSwxkCZYUglhWbbyf3MNBIhv0wjkpg5noQ1oq4kIkNY2pSZJ6iINTWLyBDzP5PW\ncm41pnaNutFJJZy0MhpuNWhevQnga316oUJt1mqpkKHWqhr7b3l5L0/RICIIA0yePYWFRbGS1gNh\noLfekljoT/30ZwAA7SXRXd25LRFHaUuYwwwjik6NyHvwrZekHePRv5C1fWBFt7W+IX6AiyvCIL1Q\nzlu+I94YVTISvyIrk05LmMgTF6UWUIUMdHVedG4d1WUzFtsLiu9LQvlZLpmGWNfcY6TboMJ6Hmyt\njtozsgJovyXj69v//v8GAIz9jegwP/mL/wUA4O5p8cL4/qisAP7dq7Li8H4g78FnfkqY59PnxR97\n7JR4VYzS3/fTZKaz0xIh9rOMaANEnva63P+t78t97/1QmKi1Mt4XavIe3VqXFWbESgmaV1Zrpilz\nTI0uLfvDnkzUGHPBGPNXxpgfGmNeNcb8Lo9PGGO+Zoy5yu34Xm05nBw4uQ4mnFyPHvthogmAf26t\nfckYMwzgO8aYrwH4TQB/aa39fWPMlwB8CcDv7dmaMVus3KyKSSYa0A+txQyTKYo6TeR5AemvqRFE\nmj1JO9whc7DKbKmz0Xr0uZKSmctVRZJb0+ktwK1mxK8GcsOA+TCViWosbsoIli4zoee6l5MZO39o\nck2SGAuLs3jzW1IbqXWRtYuawjTmZoVBLL4skUzzt4QptFLRXS1Z0aEuzQqTXI2EeSSMwVe33OFT\nonOdpf9om7HtdRZVCgPRrSFlFh/mi2VgG4bJQKuBtL9Reo+0KqvHkDgTC0M1NY0Fl/NW2vQ7xOEw\nmUPGocl1I+7iG/fexvNnJYdBwHyxUVv8fFfnRWc8/6r4c04PS5XPX/3YTwMAbt9jjoOO6EA//omf\nl3ZZKyujg/UQK0Q8e5bbGcqxLQ98+XXJAvbmi/J+3fqO+BunXWknGpLr5vmebHTkvQg47jJGmCWa\ntemQh+OezVlrZ621L/H/6wBeA3AOwGcBfJmnfRnALx9u1xweJpxcBxNOrkePA+lEjTGXAXwQwDcB\nTFtrZ/nTPQDT+2ujxzg9MkKfse0+a9xUaPXsJMW8nlDdKa8zjIntJUtijLNekDNO1aWyVo7VuvWC\nKlOZ+1qvXKkJL9fM6g1mp6kwg32ny0iMQLsnM57qQjPN+pT/AScT/cq1u7mJN7/1LXSW5bm83X4b\nAHDxCWF83/p7yQ8ZZaJ7nu/I89Q8rC/eEqY6lAmjWGcugoCVA4amLsv+KLN3dcQ6PDUt/oK1QFam\n126JrvTCtOjkYvpxql9yQDmEXFn0nCbUD5grikj9GkltmJl9bn6e/Rll/0/kCiNHv3JNUov59Q5a\nkTzH0xOycog2RD4t+oveeFVWGJVR8Yp56qekSudTH/8gW5JtrH6lue2jOL5tnnNCfn+FutfZa5LV\naW1WrPZttpM05H1Yo7fGEnMztDpdti8DU0to5d8FZncyOBy3mX2/BcaYIQBfAfBFa20hM4cV354d\nvxTGmC8YY75tjPl2ckhJUB0OD4ch1053sA1njyIORa7RYIcrHxb2xUSNOIp9BcAfWmv/mIfvG2Nm\nrLWzxpgZAHM7XWutfQHACwBQr9es73kwvmZ94cyTiY6xSl1jhZnpO/lHl9b6ksOlzWsgkdnmZxcj\nhWyp+qO+P6qzDHzWzFGmm/DloTKOBBRVMugurf4a+RIwY3431ftQd6o1lrwTqTs7NLlOjjVsZznB\nffrzDpEpvP4PYj2da28Uru3wua62xL9vqCG6zvVI26YOuik6zMa4MKCYb+v0WYmtblTJQBYYqVSR\nBoaGhZlurAhzzBkPdeDKSPJPP3XlN26LNXn6lOjk6m3R3bZZ5dIPtQYYI+DikxmxdFhyHR0eskub\nEW7TC2LqjOg8m6dl2yLz21gR3ecb33oRABBxPF7+0MekP0+Lv+fi338DADAyof64stKg6hptWtPT\nFWn31f8s/qYdPmefK9SYuS5arMW1SN3q6sYK/wbNM5wU9vO4Qe4nhxRJuB/rvAHwBwBes9b+qy0/\n/SmA3+D/fwPAnxxKjxyOBE6ugwkn16PHfpjoJwD8OoBXjDHf47F/AeD3AfwHY8xvAbgB4Ff3bMkY\nRilRR8kZvaP+nCH9ugLRuVjVndii9V0z2duytT7TGahc712t8sVsTGAsdBKxjjipjtbg8RiJVGds\ntx/I8Q1GuCRqNvY05r/452oNH88cju7lkHFock0y4H7HIs5rXjHTONNPJvSzVXfZDqujqjtmCNY8\nog4z5Eqg5ulKgc+PVtx6QxiMz9jnKBFVX6MhNwxDXVnIZZlmulctOHVukzMSi313VvxFUZJTFLHC\nAbP/hHWt8kp3gJNZeP7Q5JpmFqudFD9Ykvd9nPl8nxwTderokMhrle/56qLotq/+tTDO+eviHzr+\npDDRt16SSgf1IfGv9bR6J/10o7Z4RUQbsrJoxbKCadHv2taFeW74Iud5+ncvszKB5XumK92sbI3v\nfUBkmx0OE93zI2qt/RtgV977c4fSC4cjh5PrYMLJ9ehxDHXnt2gQGAGSsPB8kMnM06jSTzMRxtFm\nrLtau43G1m/TjRd1p1lu9isyUPU/zWs1JbJdus1Y75bMhHXmRTTUec5vCGOJ2i02xwzedDRVHWuu\ngaPXQZINtkHNAoiNzeuJr1G33e6IHGZUd8UaTOtteUKjGuHFIPgNrSqpVTpJVQN10GSEW6D1wzdE\nZ7nG+vNTFySzvc92LZllhxL5wfWXAQCXrkhd87t35TqtUDDF/KNJhzHXZDKNpjAnQyZcZc4HUzmZ\nuu7DgoVUaZjbFB3lKwviFVE9L1b4lN4Wp6dER2oon/ai6KLvXxW/3/lbsp/S/7bLdvIFIoreEpqq\nYmFMnvsqp4RVjuO1Ba4E6UWh3jrnzohfstZEW29L/zapu7X0tvA5Xr30iK3zDg4ODg7bccRZnAyy\n1EeSyczgUSdaVX9MrX3kU+dF3VbMvKBRWrS6m9KqRWsZlXWliqz0n422MKZX3xT/wirvX2EG7hHO\nWC0yniwu+h3mrZNoap7RlExIk0Ad0oR3giEstEvdlKJO3fbzXFn8fUzmOM6sWSJWdOflAYZ8TtpK\nRoZhs6K/rzpZrK7LyoWqMDSawiRbXVkpTJyW/KQ1ZqS/ztpPN29JlqgJVptsMJKpTvmv3GdMPnXm\n9apYgxuMfKlS5zvHjO0DCwPAM8i4MrjJ7FXB3NsAgMW2yO2D4+LNcJH5XUNaz+2iRDalHXmeG8yy\nZuhfatSbRW0TXPFtxKyhNCTttllhotulzpwrgEZN2guYnekj75fY/U1S2bfuysry9qx4D3Q4jnUF\nvH0l+2BwTNTBwcGhDxyxTtTAg4+U1jg1nsW0lrVoFc9oBQ+atM56zFzOvJRMZA66l8JXZuipTpS3\nyycaWu01G5TVan+sQ51ofXlmMB9hFibIDGi7oiPr1UoqRiLpjKax9jYt6mJNNtjO6FmWoRt1kNaH\nCsff2xAmceVp0ZkttqlAEpIAACAASURBVEVHViHVTO4xw71P6yqKfpdxLAJW674azyM+12XGsI+M\ni9/orVvCeK48eZHtyXn1qvTrwnk5b5V+j2enxDqfdMRlcpURMZ0V8QsdYl31EVZMmGAM/Sq9BzaW\nl9/psTzyMMagUgnztGktMsTX5oRhJtQ9dmJh5Ddj0ZVenJQsTKeM5jKgm8ak/F4N1OuCukl6YbQz\nec7rq/I+XD4jNbOu6IqE40913aqDNRx3/+TTkk3q2qzIdx7flvusMNaAtbKMPdzx6Jiog4ODQx84\n2mqfSJEmq1v8PJUByu+xMtOWzCQ1X5jIqVFhMt2GRDrYKq10S2SWLfolan5AX2PrVXdK634+g1EH\nR4baDOW8iWH6rfnCiO4tk4ky/C1g+6pzNSVHkjSPlNBgfv7gDfhc5flI60M4M3O5cPjCuNRc+p6R\n57iUan5Wkev8EuWWaiQQI8Z4fcJM6Km+GEti5fWHRPc5Pil5Ka++LVmdrjwl+UJ9el2A/qcBszZN\njAnTGW2ILnR9UZhxixE3rQVpv5pJf549L3k0x0fo50pr9L37rALaOZkRS4cKY3IvF0/zq3K8nh6T\n8RjSW6FDb5V7a2J9zyryHGsjYuMYmZFsUNbT8cOVoHphsNrvyIjI7dOf/IT8nnvdpIXrtEKBTy8a\ntI/H+DDgo9vBwcHh4eJImagB4HlBnpcxKyckIWOrULdZ55QXav5OMtBRzcJUle0aY5p9Mst4U6yz\nIWdCVaJ6jFAYDZklKGRm+1h0JfO03iVqhdf7MLZezey5nym7rbWhqryfMuyuWgEHnImGYQVnZi7j\n7OSpwvH7njCEr39LYujTlNZs6hTXloTR1emNEWhFAT7PuCPyuPmDfwAAnDr3LIBevk+vJgzz1Iys\nVMaHNJ2WSiYo3NenbjphLP+9t8Rar1mbQivnjw7J33FpWJXvoru925H3aoFeHQ1m1AdWd302jzKM\nMQh9Dw2tJ18t5ph4/lmRR43eDSGt64aRaypPj8c/8nGp6pklmmUp1hvJcXu44+QXP/RhAMBXuX9N\nxI0NVjQI8uwJ/VnpB3t0Ozg4ODxkHDETNQg8L89qFOS6UY0xl/0amaLGwm4wIqU2yhjdluhcbCjW\nvlEymWZDmE86LDNfksrW08gjVi2sMkJqaV78Q1trYmW1jMXW2k8KSwaa0Y+wZ9wjU1YGpZm01T90\nsAOVcoRBgLOTp3DzzdcKx1dpjd9YFaZvGTzfZUWAJCJjJ5Nr0h9zaEqYYGtddOPoiFw2FhnjTutu\nY+opAMDZabHiok1rOZmRT7/Opbek6mREJtlhdiaffq2VmrwXAVcSZybF/zAaUgGKlb5OnXork35t\ndgbb6yL0PZyZGMGlMVZbZcy7oQ3h5z8lmepDrRjAiC5wPGtouqX1vBsfzoCwzKURMc8pMxnAZ9XR\np2bku6BW+ocNx0QdHBwc+sCRV/sUdVcxr6Na/zIyz03NxpTn8aTVtiX+aRGZTEa/0ogMJ1tjFh/W\nj2+MiBV2jFZEnzNll9VB52ZpvddM14xxT/PsS2peL1n9eNjYonleIypUJxoOdmh1jqjbwc03X8Py\nwkLheCsWOYVkLhvUVedPjc9b67/XKsIpPHpXBFbkmCRi3e90mB+Uuss8j6wVneb8sjDfgNl+lIl2\nb13j7ZhfkvKtsWZTc1TOazSk3U3qyt6id4Bm67q3IP1Y65IJJYO91KgGAS6Pj+H5pyXXwPRTz8gP\nzPN62NBsZ6objfg9qIS60lPvmJP13B0TdXBwcOgDR+snai2sjfNaSVqPPUuLeUB9UtBhMokadVYd\nWnV9Vu/083ZlqwxHa+lMDouudGqc9cFDmUHvrzHTugYWkVGUVKEwebtar577jI03ue6TzJXnhaSg\nefahAUeSJFheWMAMI4LKuM2qkJlhVh7u5zWpoMyOD5Q65rQu8grIBLurwgQNmWSyJnlE4y7b4woF\nvjDimO1upGTA9GcMGNNf5fvR8VTwIvCRupy3zryn6+vS7vy69G9kXK5fWn8M/ET7gKelyujXWQ21\ntpY8Pw3kM7ZopX/U8HiMcgcHB4eHhCNnolGU5tmbPM0wXbJqqz9nlUyiQvvbJn83ZJQRI1861L2p\n1TZgJEmFVLJOq11aoT+iFuCqFGs9aT5Q9V+1JYpbTmTu59xXmeqJzHT+0FGpVDBz/gqmZmZ2/P3e\nisSma02qjK9drhOnX25K74gOVxoaytaoFJmj51H+a2SY1EVnNeazjLUGllCd0SefLvQnLWWbqvla\nZVLeizb9RjdpTfbIYMcn5f7TobTfHZXIqW/cWdzx7x5YML/objmsylb6QYdjog4ODg59wNgjrBNj\njJkHsAlgYa9zjxGn8PD6d8laO/WQ2j42OLk6uR4jjl2uR/oRBQBjzLettR8+0pseACe9fycVJ/25\nnfT+nVSc9Od2EvrnlvMODg4OfcB9RB0cHBz6wHF8RF84hnseBCe9fycVJ/25nfT+nVSc9Od27P07\ncp2og4ODwyDBLecdHBwc+sCRfUSNMZ8xxvzIGHPNGPOlo7rvO/TngjHmr4wxPzTGvGqM+V0enzDG\nfM0Yc5Xb8ePu60mGk+vgwsl2n/06iuW8kfQrbwD4FIDbAF4E8Hlr7Q8f+s1379MMgBlr7UvGmGEA\n3wHwywB+E8CStfb3+eKMW2t/77j6eZLh5Dq4cLLdP46KiT4P4Jq19rq1NgLwRwA+e0T33hHW2llr\n7Uv8/zqA1wCcY7++zNO+DBGSw85wch1cONnuE319RA9A988BuLVl/zaPnQgYYy4D+CCAbwKYttbO\n8qd7AKaPqVvHBifXwYWT7eHjgT+ipPv/BsAvAHgOwOeNMc8dVseOCsaYIQBfAfBFa+3a1t+s6Doe\nK/cFJ9fBhZPtw0E/TPQgdP8OgAtb9s/z2LHCSNGlrwD4Q2vtH/PwfepeVAczd1z9OyY4uQ4unGwf\nRp8e1LBkjPkVAJ+x1v42938dwEettb+zw7kBgDcC37tSrYZ5TjmbV93QPhRTymnX8j5yoyn0QiZv\nDpmqzNfMdEyGHLFkccLkz1q2Q5M5a4ozvXvG+zADG9KsWECv3D/D87U/ez3KdjdaOOmJKh5Err7v\nXwkrlfLPuyIvu5KLlc/T6O/6vMtzfFY4P38Pwgq3WpBQMzyyRDJT7PkeCyDCL7STMtl3T3x8D/KU\niJqDkcm4tV+8YHV14cTLFTi4bIPAj2vV6gPfL3+eVuXL55fnXtZxoyknWTKdqQ9rFUnGnrIgXRpT\nTpozs/eBKN6ndN/8PtyrjDBJO8WYMBm4x/dIO3h/9u6+5PrQ84kaY74A4AsAUs/38L5nzsPjxy1q\nMbM9H4phavkOX14tDthhfkgwA/0IM91PM7/k2RGpoTTKfKLpiBy/uSLnLy9LbZ6oI1UnRwIZRMN8\nmG0+5BZrurR5n/UNOb/OwZl58pAjrZvNwTdSF6FHaakWUwnfu3rzxjue8AihIFfPw1NPP73XJTkq\nmm8y4ccqYdVN1mKqVaW2koXkDbWc1YzH2lqsV16ry/syc0ZUdWfOSA2gSk1qJ2VWMt4vsfbS0Mgl\nAEAK8YCJrbxXK6wimupYpBw7m7JS7HZk64esCupJ/1IOvj/70z8YRLnC93x84L3PPnBblh8j/fhp\nHfpKWJwkM463pCLvxYVLTwAAnjsncl25fRUAsH73npzXlvGXsF2fJMkknCz5/TAUqOX4Tvh1Pccq\npX5FDsyvyDhvnpHKDBn7+b/8z/9yX3Lt5yO6L7pvrX0BDM1qNmo2TYGYH03LmSdN+ZD5EVWmGLF+\nQJV/fIVJlCeG5KM1PSIldk81WOCMhcU2WRBtrCYf0zYLXSWRtLzCUrdpKOc1KLwKZ66Yc1aNgybR\npMFMJq0zXRgUK9FVBqMcyIHlWq83bE7f9wGPgyemfJXZZZw1UyvPO2A5CcPnmulKgAUCW22R9+KC\nTJKNmpTKPTXNsiNkpCNj8vH0grp2AAAwe19WfesrMjiDjMm7OXlryV94ZLi55Pn+JI9ceZA9ZbtV\nrqPDw7ZZ7aMoXSDPK461xDFXBsr8tP4Ol5ATk2cAAFfOnAcArF79AQBg/vVX5fyOfCc8js8q5VFO\n/axUpvxGqvTuf/tbcr+n3wsAmDo9AQBoteQ9mp09mDagn1H/IoCnjTFXjDEVAL8G4E/7aM/hZMDJ\ndXDhZPsQ8MBM1FqbGGN+B8BfQMjjv7XWvrrXdSl8xPlUoXNIUTdpfGGAHk/MtMQtZ6yxhjCDiWFZ\nxofUWUWk92nEmWpEGElQk/brCQuMcUbcbMv5VZZiVeZpSUWaDWGyLc6cm5GqH7SUK+egogrukcaD\nyNXAwhzAIKrPV0tOe7kOs1gQMOYyX18TXR72GKI8/822FKq4d/9tacUXuY5PyvKsG4scLeW2sirn\nrSwICYs3Zdlfh5xXqYuaJ6H6Jk64ItFlZyYrmJTLx0cFB5ethbV9vNQUZMyVXIOsNtHS1SxYOXpa\nyso8c0VUQvb+TQDA3NXXZZ8rjrxQZImB2tI2fxdz5astXJcsSQ7nude+CwAY74iDQuOseEZN1A/2\nWexLJ2qt/SqAr/bThsPJg5Pr4MLJ9vBxpIXqMmsRJRESGhRSMk2fVnBwYs/5KWcqLSDWoI5lsi6M\noaHW1q4wg4wM1bCQVtqW0sjDTTk/ieR+o1TzRJwhO2SwdVp7KwGZB5lLk4pmj4xVK/tWqLPzqRtt\n49FiJocGYw5U7jZNlN2oNqlkAEjzioV6A+7L+ZaXKZMlQcRGSwwE8/fFkGSpizN10bVtkNl2WyvS\nHAvcaanrdiQGpJRvYNAQA5RPnajN1LpMhmwGuxBbZjNE0W7l6PZGQp1nQl232octjzcmJwEAly6L\nIam6KbrI269+R07caOkFhXZ1d/e1D5lqUeXau4IrV7sm8l564xUAQESbysjl83v+bVsxEJYQBwcH\nh+PCkTJRA7Gs+WQtyijUpUT9/dS3yVILUicDmWrKzDUWiFW+mgmTaE6Jy0k4LTPIwi3qPO6/BQCo\nkdF6DWGuHplnaOXPzzgzdeji5HmcQamMC9SfjL8rQ1XXmyqnxnp1/76SgwUDo2WO94Xc/wJAT9fl\n9RxFZWtV98UzsmIpa0PvjqCkU/d5fpi1eVyY58baqrSnvnOZni9yMyq+UP0XSZ0yXREVvTPUm2RQ\nYYyBFz743xhC/Xhlf2NTxl2lLkvBp59+NwBgwoqc7nz3mwCAaElWFGq87+k4i+2rQ4iWLjemuKIp\nn6/Hc09EjnOvvQkAWLv+GgAgPiD7dkzUwcHBoQ8c7VRqAS8BbJdWcc4MjYbMTDGZxjCJSjNXZshU\ndmZYzhumbrQqhBTTT4lOpX5RnK39ijhPryxKTgKfjLIxLFRjY5V+iGy/4jMqg4xV/VO7CecYMlBr\nZApLOPd0O8JULPtdOxAbGxwYL0Q4vP+cD56V5xy3FwEAPnXLnjI9W7S3qk485VatrB4ZJKgbV2fu\nGlcqdfqFZj6t7F1a19XrQ/0VqQP1KH+fDIXNIaXSNeZ7AI2YGXRYwKYPzrOMLjXJZofHRE6Xrog1\n/MKw7M9+8xsAgPbcSn7fYkNFSpmVmGbvdH44TDEyMff+sSW5Zer9I/BoW+nevPpOf9Y2OCbq4ODg\n0AeOXCcaohfrXgllJvKhfplyfKwm3/YaGcsTk+K3V2OY1sg5CWeded/HpN0xCQ/buHsfAHDvTYl0\nCHyGbzbJQNfEH1BnLq8putQqdZmr66KL6ZAJ0V0VNMoji6mDYyxvWCvq4jqdzr6fxSDBCyuon7m8\n7/ODTJ5ja1Hkb7gi8LgC8BjrHlA32Unluaa0nisvMZ4wThMOA+iF5a53yQ2WGQHVkuujSH7XFYQh\nE6mGEe9PRsxh4VHnqd4Y6q+sutpBL09mjEEY+nufWII+t9qIRJA9+cx7AABDZLUr3xfmefXrElVp\nNTyTOm5NnaCPN2V7FXrB+PTPrnhyXL090hKFHT4lXhm1pqwQ777+Bhumdd5TNw/el0uPAziayN97\nsNMdHBwcHLbiSJmo5wH1igE0AUhdmIFPB7JhJhSZbAizCI1GGlBHNSQRSiPnZGbrhMIku2SGjWHZ\nP3vuSQDA7XWxxrZElYaqkXZXPdF9JB3xI03oT0giglpVdajCdNI8u5NMUc2aMOEoKfoJLt96HLOr\nAX5YwcTpi/s+v8aVyHweEcTEEYyZb5AJVBkxth7TD3hDYpuzrjBLzYUQd+mnS13ncqrMkpFFhro2\n3s+SWtaZi6HGlYjlddbqsKBV3hSzRoErpCQebN1oZi2iKD7wdSaQcVE3RWa4ckP8MZdu3JbzqKse\nPSfvTv00EwIxoiilDnvkabF1NKeE2YbMLOW3xKp++3XRYQ6dOg2gZyNJaYbfnBcbSfi2MN/atNxv\n9Gk5rzbERDf8XixdEys99oy/FDgm6uDg4NAHjtzRLQNQ07RMmcz8VeoeR2vC/CrMphNCmGK1JjPP\n+DnJxlObFh3oXcbU3pwVXehYS3Sel0ekwfVxYTKdVWEik4zdXQiY9Yn+ghowpSqSgHkMfabcU6Oe\nZqEJRjSVW5GJjF84/c5//Ou33/n3RxSeBWoH0A/qSzc+JTqrFiPLKkae7xgZKDxZOUSb8ruJ5T2I\nO8JUuhEjkKKi9T4gA6p4RevwxsYm7yP7qtMMmAuVbsR5jL56Cah/KBhHntJBMcsOztIeKViLJD14\nVJbmDY1iyoHPqUubhKFXxdjTPwYAOP+hDwIAUiPjOVmVrFprt2W8pAkjED0yUfqHz70hjLHblvuc\nn5YV4uxbPwIArC7J+3H2nPiXN0/L742zkshqbEoi0jbn5X5+Q1ayY+cv8S/5h339vY6JOjg4OPSB\nI2Wi1kqUTxZrqIF8wxuMeR+qiq4pBGNmu3Le0IwwvJErlwEA3qgw1pov18V3JFb6zZsyA2FcdCuN\nUzJzNVdlBowTabdO6pvHcCvTJLEIGCud0hrf6TAJLM9bWV4rXk+Mjo/t4ykMHpI4wuLs9X2f36Eu\n8Qx11wGZXqBZsRi51olkf31D3oONdTmvzf2EOjOtNBAw58GwrlyYMwH0wniDeSI7XMGAWbxSxsQ3\npjUzvtJqZu2i4DW22qbyPmTpAKTtegdY9OLPDwL1943oVRGRq6UBdZlcaYxdkgjD5fui05y7ITrv\n09SRxm25fvGq/D5JP16MSDstMkhUxEdZ8xSnS+J/3F7kd+TKZQBAOCLvQcxxq8x48aow2mBcGOrE\nqdGD/b0HOtvBwcHBoYCjtc4bg5ofIGAkgzLPqVHRfTXITE2V/phDwizGr0heSH9MdGht6lRiCCOo\nUYe6GkuG6uqIxOTWa9R93hGdSrghTGScOrJ5qmZj6ro48SH16AdKpqNZfTKNfFiTE41fdChbXV7Z\n/8MYIPg+MDp0gFeJTPDurTd5PRkf5Ll6gXpFyHuxviny2FyXFUBC67zHMiEVRrANNeX8qVHRdc1M\nyrZFHfcUOcMSdehRmxErbeaXbcl+s6kx32Q2ZJwxdbBZKu+T2ZZTfbDg+T4aw8MHvi4Mhb7WOa47\nmdYworcDXxV9jjYSRuhxfC69IjpvzfNqyRwTutlkZJTaUMZcGJodzuOKxKPc8txqofqVslaTkfcs\nCzSvLSPigoN9Fh0TdXBwcOgDR8pEfc/DyFADFUOdVV2+4XWtocOZw2e3Jp8S6503Kf5cLch1yxtk\nMndlxrr1lujjwpboQE5Nig61Qr/RzkVhsptvLAEARugvuFjVbEwSqRTRXzUjI4lTRq7Qj9DPo2zJ\nYKKidX6wNWS7w/cMRur7n4/9GpmEpf8nmUSrLUx+sy0MpJuIN0WbummtzWPoTlFlBNsE/XknRkWX\nNTUpVtiRUdGNG+aNvTTBlUoq192bE+YTcQmyskbOwnROSkgsrf7KnOKYS5YD1JV6FOEHAYaZ8/Mg\nCOjV4mtEolaCZTqnjONsY1F0l5OXxBrebNJb4574c67cFG+KZE3zDTPSjCvAfCVIXXWmuQ0ouCzP\ntsZtoDkvqAtnBYOJ594PABitC3NuHWGNJQcHB4fHHkcbO2+AigdM0zo3FDDmuS1MsMG8oNUxmSHs\n1GUAwE2m97t7Q6zvN6+LNX7hpmxX774NALh0Sq5bXZbaOWfGpGbL6GlhprcZOjvG6qATVdGZLc6p\nbktmKM3Wo1UmwQzmhtoVz1M/w1J2mfaA+w3ugiSOsHDv5r7Pj7kSGJ8Qa6hm1dok46jk+UHl+XbV\nkTfVbEuyW68IA50cEbY0NSbvk1ZkjhiBFFJedcpxfFS8KDZJXJbui450XZnOBrOM6QpEg6lpdU41\n7+wBY6wfNXS7Xbx1/a2DX8hkE5U6c2GwimdA3WNGHadaxdvUdY+fk/eheeFdhXaSN+j5QfcZJaCa\nz7VFP9SI8sl03PK90Qoant6fKwmtnFGpy/swxxXt/NU3DvTnOibq4ODg0AeOVicKgyG/hpFQGETQ\nlRmISXgweUX8w/wRYY5tRhB8//svAwCusl705oLozhj6jNFhoR7Vpsws9xalSuDIGdGRNU6LH1nI\n+vJJR5hvvSY3jlMyH858GRmMIeXxNLIFmn+Sfw91LxFnVi8ZcGqyC+I4wtzsttL0u0L9bdO4GBmU\nRPSCIFONMq3Ro8V5mCfSiG7NBCLPLvOGLpF5xHPSlxnG83eoa5+dEx26R//iafqPxqG8Ty1GxnQY\nmdZmWfm6VoHNWNNLM+Z7RZ34oMFYizDdf90ww5HhVWQcPfnchwAA1fsyHle+Lwwv63K8UFUazctK\nANOM+NsUOS5dF91oeEoY6ulnZLv08ksAgNVV6dvMRz8KALAd8TO9f0NWqDMf+Ijcb5MrDUYwTT/7\nrLTzqkQkrVyTyCjVjb9T9aad4Jiog4ODQx84Yuu8wWitgoonM01Ef8zhM8IYahdFFxLUZUa6J0QV\na8vCHMfpl/fRD0is7RQjHobHhXF6Wg2SVt+KZudhhNLQpMxkK9dfYY/on2iEmbRjYSoVqBVYmQ/P\npjXeDzmTQquDDnatnb1grUUc718frPkiF+5e1wYAAB3NZM+8sm2qQpOsuALQDPbVmshtjSuBt+mt\nUY2FKU6ekhwLbfr7Lq+JNXh8SFZC0Spj7VOVZ1Bo3yaMkNGqsx4z5UPeR88MuHXe9zE+uv/onYwr\ngYheNJbPVZ9nwEi0sfPCBCtNOW9uTvSuCcejz5IVeVVPytewggG0EkUq1vtolTaVGVlxTv+Y6MgN\nVzImUK8A+pNym9FfFBp5qMkUDphQ1DFRBwcHhz5wtBFLAJq+ha2I7uHyR34WABBekvygq6wzPfuW\n1Ea6eesaAODpmjCRD/7qfw0AqE+znk+uq6JVTvNI+soQ5M9bvSv+obdviXV/k7/6zB400pS5ZIER\nKiBz8bRGTFr2OwP3iwzpcYWBgfH2/yrZTGslsX47GUBABhAwm1NC+WmqBbXWay6De7MSOx1zhWJZ\nyaAxIt4fGSNYMuaVXF8X/1Mk/3971xIbyXVdz63q6g/Z7CYpcjgUZ2SN5SgOZBmxEWQTLQLk52Rj\nIwsjWQQyEMBbG/DCgvcBtDKyFuAAWhgIDNiJtchGTrxIYmAw0iCJLI2lkQJNJA3nQ4747W9Vvyzu\nuTVkz4zZ7eY0m817Nk12V1dV16vPeefee676Rs6WNW80Na2V+2d5hC326inT9SvmeWVM2hjqtCLL\nMmxt7wy8vF11vdh6j5lTPTVs+sj26JEww/zQlYt6Pddq+tq4odppi6J0hZ0HsGfMkcyS0fqtG+9z\nO7reOvOEy/QfvfOuauQp88hj5pVGMJ/gvi6hQ+KsX/8Oh8MxEsbKRHu9gP3dFs4tqlY1W6TGxL7P\n0b6+loJqW5+/pNroMqP15aoyjDTvK07tUqw2lz1x+GTJo+pkJnFdn1DJbc1p3Cez2NtklgCZS7mi\nmoxVyGTMD63QHci01zw2e1a6Pz4CIoJiXDx6QaLH8epQk5Kcgeprl+dBj91WY4ZxrctjL2/CYwyS\nTJYaWMG6gVptNd8PQRnM9q5qpjs7G/ycvXXyy4HuXWQsKWcq/Dp6FMlLI/RkPw0IIeQeukOBx8f8\nW8OiXjfWo2rzI+aeMs+ztqQa5vbN/wYA3Lmu2Tgpe5Z1RCuIbrIvfXNHZ5BgJVr7Lj+/pzPO28yy\nufC7WvG4wd5KKXs83bumPdg62/q9XBulCNsbcobhTNThcDhGwHiZaJahubuPm8z/2tlUbUqYN5qR\nQSx/WfPLanTlSZgQ2qXmaTXsUdes6PmkY95eYGVLywqR2NNpYU0rmNp3VEsr8cm4wJ7pZjOZkClZ\npYxF4YXr7fVLJ+LPolgGZ+PWOytwXFJziudxtayKXldf8x5YZjBv49HHSA35+5Fp5tari+5fjBpL\nroXxf/t+3tWTTJc11wnzSsuBJ0q7OfBvPq2QIcqyIh73iNpnsETbWJlmmYxz/6Zefxu/0nzPDYuK\nc1x6eRtVzkwaepxN00Q49GJt5hGzU4a9/+EbV/g5F+BMZ5szHUuusLMn8LdGS4y5fPDhAL/amajD\n4XCMhPEy0UjQKiXoktF1P2XPlUgrRgKjqJ2r+vk6fSAjdvcr8LU0o1pqiUwjoedhXGQ0l9FbMB8w\nYXR1kV3+tqqqjTZ2tbKhWqEj+qy+Wv/4gtUAm6sPn3FZH/PshbOtiQIBvXB0ZUtszC8x7wE9vllq\njE9zErs9Hc8mo7vdfuZB7hBCn15n+X2WL8y3M6upDodXFA5wT/0/PfQ9oyq9kHH/bWmuf8pdnKIo\nQoXX3EAwf14Oy86WVhBt0NNi6RnNwomZ37t/40Ndnhp11nc847zbL3F/YA69EfqGIb8arZ99/v1w\n6HO7invWU2tevTTWvvgF/eDy5Yf8yAdxJBMVkYsi8nMReUdE3haRb/H9RRF5XUSu83VhoC06JgI+\nrtMJH9fxYxAmmgL4TgjhqojMAXhTRF4H8A0A/xpCeFlEXgLwEoDv/to1xTGkXkPBHOz7mACDn9i6\nqZqpVar0KJaZWo2kKQAADb1JREFUZgpWIOTMwIJpJpFa9JyVEjOsWHruD/8SADB3Ts+fPTLRAisY\nZqmtNmD+keaMTb9CIzp9z54oSXAKcXzjGsIBHevRkNgMOvWlTL/JPMjNfNx+zTNI79D3+lU6Kxh7\nYkHzPp/9vDKJu5saxX33Pa3dTtM212suUaxEogZqWnu9wvUtcsYzo+PbZG11uk8tNJ1I165jG9c4\nijBnfaoGgJ0Be9QwQ1eP/61PPtQPLqgv8LlLrFjiDPDT9zVa39ui5mmxB67QZFnrpdXPIC3PuP+8\nuH9G8nwy5mpZIJbFQ6f8leeUKSezQ7BvDMBEQwjrIYSr/HsXwDUAawC+CuBVLvYqgK8NtWXHicLH\ndTrh4zp+DKWJisjTAL4E4DKAlRDCOj+6BWDlqO93swzr2zvalAdAl5VBLYooJdbUf3FlVT8nE02N\nEVIcS5nPlWtSZCrCWvy29cTh+jvsyfN/l38G4D7DpO0kWmSiyzWtcGjSZaad6ZM06stHfOB3DVE3\nPokYdVwHhfk3ZjZ+1EITY4TUVSNq4W2endZXPCe7wdIuLHuC4PljPZGyB7px2pSFeaFiFTX6WmIU\nvlK2/3XpGVLlOFONvcWat3Zmrj+TiVHHVSJBZbZ81GIHitzZZ94mZoxFFCvK6Atmz0smGCe6buvS\n2iXlzCyYztXkGia/l1cYmbadU1A5/P6hd+8z0Iy915afex4AUH9KPTjubWnecGN7uF5pA0fnRaQK\n4McAvh1COFQLFpRPP/QOIyLfFJE3ROSNTne6G3udRhzHuHbT3yAh2/FYcSzXa+d0k4NxYSAmKiIJ\ndEB+GEL4Cd++LSKrIYR1EVkF8NDGJCGEVwC8AgDVSins7O/lw1eb0WisVSLNVfWJdWNX3XZiPsmy\nvidMlOhyIc7VEV2ePpGBddx5l04y2vXbmp9mDLZLn8qUpU0x+9LP8OEb6HdZ4HYTim/t/vMvO53R\n+WMb15mZMNDTmAyyQO25Rf/QjJ4E0iOzE30tx6wcK7FXFg+7VZKZi1LPel5x/W3rK87lelbXT0pi\nNdwRtVErUInJhMr0nS0YwWI+spDyxHRzQtgd5FePHcc1rkuL86Fenz1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f186e5c05c0>"
      ]
     },
     "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": {},
   "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": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 0, loss = 4.74837, acc = 0.1\n",
      "[Validation] Batch ID = 0, loss = 1.30745, acc = 0.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 1.66561 Best loss: None\n",
      "[TOTAL Validation] Batch ID = 0, loss = 1.66561, acc = 0.0362811791383\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.891\n",
      "[Train] Batch ID = 10, loss = 0.772367, acc = 0.0\n",
      "[Validation] Batch ID = 10, loss = 0.761044, acc = 0.0\n",
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      "[Validation] Batch ID = 20, loss = 0.707551, acc = 0.1\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 770, loss = 0.549116, acc = 0.15\n",
      "[Validation] Batch ID = 770, loss = 0.515125, acc = 0.3\n",
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      "[Validation] Batch ID = 930, loss = 0.583206, acc = 0.05\n",
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      "[Validation] Batch ID = 940, loss = 0.505639, acc = 0.2\n",
      "[Train] Batch ID = 950, loss = 0.542394, acc = 0.2\n",
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      "[Validation] Batch ID = 990, loss = 0.543452, acc = 0.35\n",
      "[Train] Batch ID = 1000, loss = 0.572497, acc = 0.1\n",
      "[Validation] Batch ID = 1000, loss = 0.5463, acc = 0.25\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.527947 Best loss: 1.66561\n",
      "[TOTAL Validation] Batch ID = 1000, loss = 0.527947, acc = 0.274376417234\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.8019000000000001\n",
      "[Train] Batch ID = 1010, loss = 0.529695, acc = 0.35\n",
      "[Validation] Batch ID = 1010, loss = 0.537693, acc = 0.2\n",
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      "[Validation] Batch ID = 1020, loss = 0.508547, acc = 0.35\n",
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      "[Validation] Batch ID = 1030, loss = 0.447802, acc = 0.45\n",
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      "[Validation] Batch ID = 1040, loss = 0.468095, acc = 0.4\n",
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      "[Validation] Batch ID = 1050, loss = 0.513724, acc = 0.4\n",
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      "[Validation] Batch ID = 1060, loss = 0.562233, acc = 0.2\n",
      "[Train] Batch ID = 1070, loss = 0.525321, acc = 0.15\n",
      "[Validation] Batch ID = 1070, loss = 0.560865, acc = 0.25\n",
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      "[Validation] Batch ID = 1080, loss = 0.535219, acc = 0.15\n",
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      "[Validation] Batch ID = 1090, loss = 0.524199, acc = 0.05\n",
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      "[Validation] Batch ID = 1360, loss = 0.458349, acc = 0.5\n",
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      "[Validation] Batch ID = 1750, loss = 0.404977, acc = 0.65\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 1760, loss = 0.477901, acc = 0.4\n",
      "[Validation] Batch ID = 1760, loss = 0.376641, acc = 0.45\n",
      "[Train] Batch ID = 1770, loss = 0.547231, acc = 0.1\n",
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      "[Train] Batch ID = 1800, loss = 0.549237, acc = 0.1\n",
      "[Validation] Batch ID = 1800, loss = 0.406275, acc = 0.65\n",
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      "[Validation] Batch ID = 1810, loss = 0.317451, acc = 0.6\n",
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      "[Validation] Batch ID = 1820, loss = 0.469135, acc = 0.4\n",
      "[Train] Batch ID = 1830, loss = 0.467463, acc = 0.25\n",
      "[Validation] Batch ID = 1830, loss = 0.495475, acc = 0.3\n",
      "[Train] Batch ID = 1840, loss = 0.567575, acc = 0.1\n",
      "[Validation] Batch ID = 1840, loss = 0.383043, acc = 0.6\n",
      "[Train] Batch ID = 1850, loss = 0.537977, acc = 0.25\n",
      "[Validation] Batch ID = 1850, loss = 0.427498, acc = 0.4\n",
      "[Train] Batch ID = 1860, loss = 0.490099, acc = 0.3\n",
      "[Validation] Batch ID = 1860, loss = 0.433397, acc = 0.3\n",
      "[Train] Batch ID = 1870, loss = 0.548094, acc = 0.25\n",
      "[Validation] Batch ID = 1870, loss = 0.410377, acc = 0.5\n",
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      "[Train] Batch ID = 1890, loss = 0.548793, acc = 0.15\n",
      "[Validation] Batch ID = 1890, loss = 0.450093, acc = 0.3\n",
      "[Train] Batch ID = 1900, loss = 0.534697, acc = 0.3\n",
      "[Validation] Batch ID = 1900, loss = 0.430505, acc = 0.6\n",
      "[Train] Batch ID = 1910, loss = 0.548138, acc = 0.25\n",
      "[Validation] Batch ID = 1910, loss = 0.423873, acc = 0.45\n",
      "[Train] Batch ID = 1920, loss = 0.487987, acc = 0.3\n",
      "[Validation] Batch ID = 1920, loss = 0.419398, acc = 0.55\n",
      "[Train] Batch ID = 1930, loss = 0.594496, acc = 0.1\n",
      "[Validation] Batch ID = 1930, loss = 0.367215, acc = 0.5\n",
      "[Train] Batch ID = 1940, loss = 0.560458, acc = 0.25\n",
      "[Validation] Batch ID = 1940, loss = 0.40906, acc = 0.5\n",
      "[Train] Batch ID = 1950, loss = 0.536118, acc = 0.2\n",
      "[Validation] Batch ID = 1950, loss = 0.436624, acc = 0.45\n",
      "[Train] Batch ID = 1960, loss = 0.513906, acc = 0.15\n",
      "[Validation] Batch ID = 1960, loss = 0.439801, acc = 0.45\n",
      "[Train] Batch ID = 1970, loss = 0.510395, acc = 0.25\n",
      "[Validation] Batch ID = 1970, loss = 0.42038, acc = 0.65\n",
      "[Train] Batch ID = 1980, loss = 0.581206, acc = 0.2\n",
      "[Validation] Batch ID = 1980, loss = 0.450052, acc = 0.25\n",
      "[Train] Batch ID = 1990, loss = 0.316904, acc = 0.65\n",
      "[Validation] Batch ID = 1990, loss = 0.437588, acc = 0.5\n",
      "[Train] Batch ID = 2000, loss = 0.567819, acc = 0.25\n",
      "[Validation] Batch ID = 2000, loss = 0.410551, acc = 0.55\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.40183 Best loss: 0.527947\n",
      "[TOTAL Validation] Batch ID = 2000, loss = 0.40183, acc = 0.52335600907\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.7217100000000001\n",
      "[Train] Batch ID = 2010, loss = 0.501406, acc = 0.25\n",
      "[Validation] Batch ID = 2010, loss = 0.403945, acc = 0.55\n",
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      "[Validation] Batch ID = 2750, loss = 0.397645, acc = 0.5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 2760, loss = 0.442885, acc = 0.45\n",
      "[Validation] Batch ID = 2760, loss = 0.344452, acc = 0.7\n",
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      "[Validation] Batch ID = 2980, loss = 0.294864, acc = 0.7\n",
      "[Train] Batch ID = 2990, loss = 0.472628, acc = 0.55\n",
      "[Validation] Batch ID = 2990, loss = 0.345315, acc = 0.55\n",
      "[Train] Batch ID = 3000, loss = 0.329227, acc = 0.7\n",
      "[Validation] Batch ID = 3000, loss = 0.338029, acc = 0.65\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.322056 Best loss: 0.40183\n",
      "[TOTAL Validation] Batch ID = 3000, loss = 0.322056, acc = 0.666666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.6495390000000001\n",
      "[Train] Batch ID = 3010, loss = 0.50714, acc = 0.3\n",
      "[Validation] Batch ID = 3010, loss = 0.354684, acc = 0.55\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 3760, loss = 0.285774, acc = 0.8\n",
      "[Validation] Batch ID = 3760, loss = 0.1918, acc = 0.95\n",
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      "[Validation] Batch ID = 3970, loss = 0.227198, acc = 0.9\n",
      "[Train] Batch ID = 3980, loss = 0.249163, acc = 0.8\n",
      "[Validation] Batch ID = 3980, loss = 0.256923, acc = 0.9\n",
      "[Train] Batch ID = 3990, loss = 0.518256, acc = 0.35\n",
      "[Validation] Batch ID = 3990, loss = 0.204005, acc = 0.75\n",
      "[Train] Batch ID = 4000, loss = 0.471972, acc = 0.3\n",
      "[Validation] Batch ID = 4000, loss = 0.277755, acc = 0.8\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.270844 Best loss: 0.322056\n",
      "[TOTAL Validation] Batch ID = 4000, loss = 0.270844, acc = 0.732199546485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.5845851000000001\n",
      "[Train] Batch ID = 4010, loss = 0.512168, acc = 0.35\n",
      "[Validation] Batch ID = 4010, loss = 0.200947, acc = 0.9\n",
      "[Train] Batch ID = 4020, loss = 0.456527, acc = 0.3\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 4760, loss = 0.225972, acc = 0.9\n",
      "[Validation] Batch ID = 4760, loss = 0.257425, acc = 0.7\n",
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      "[Validation] Batch ID = 4930, loss = 0.222711, acc = 0.9\n",
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      "[Validation] Batch ID = 4940, loss = 0.208333, acc = 0.85\n",
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      "[Train] Batch ID = 4960, loss = 0.418091, acc = 0.45\n",
      "[Validation] Batch ID = 4960, loss = 0.214065, acc = 0.8\n",
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      "[Validation] Batch ID = 4970, loss = 0.221743, acc = 0.85\n",
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      "[Validation] Batch ID = 4980, loss = 0.228242, acc = 0.8\n",
      "[Train] Batch ID = 4990, loss = 0.200832, acc = 0.95\n",
      "[Validation] Batch ID = 4990, loss = 0.236842, acc = 0.85\n",
      "[Train] Batch ID = 5000, loss = 0.159624, acc = 0.9\n",
      "[Validation] Batch ID = 5000, loss = 0.259498, acc = 0.8\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.234846 Best loss: 0.270844\n",
      "[TOTAL Validation] Batch ID = 5000, loss = 0.234846, acc = 0.787074829932\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.5261265900000001\n",
      "[Train] Batch ID = 5010, loss = 0.499492, acc = 0.25\n",
      "[Validation] Batch ID = 5010, loss = 0.259705, acc = 0.75\n",
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      "[Validation] Batch ID = 5750, loss = 0.185408, acc = 0.85\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 5760, loss = 0.473519, acc = 0.35\n",
      "[Validation] Batch ID = 5760, loss = 0.198936, acc = 0.75\n",
      "[Train] Batch ID = 5770, loss = 0.418619, acc = 0.5\n",
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      "[Validation] Batch ID = 5820, loss = 0.236578, acc = 0.85\n",
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      "[Validation] Batch ID = 5830, loss = 0.227691, acc = 0.8\n",
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      "[Train] Batch ID = 5860, loss = 0.127475, acc = 0.95\n",
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      "[Validation] Batch ID = 5900, loss = 0.208702, acc = 0.65\n",
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      "[Validation] Batch ID = 5910, loss = 0.287956, acc = 0.65\n",
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      "[Validation] Batch ID = 5920, loss = 0.164909, acc = 0.95\n",
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      "[Validation] Batch ID = 5930, loss = 0.236799, acc = 0.8\n",
      "[Train] Batch ID = 5940, loss = 0.466363, acc = 0.2\n",
      "[Validation] Batch ID = 5940, loss = 0.141701, acc = 0.95\n",
      "[Train] Batch ID = 5950, loss = 0.520491, acc = 0.2\n",
      "[Validation] Batch ID = 5950, loss = 0.150782, acc = 0.95\n",
      "[Train] Batch ID = 5960, loss = 0.154216, acc = 0.9\n",
      "[Validation] Batch ID = 5960, loss = 0.228778, acc = 0.85\n",
      "[Train] Batch ID = 5970, loss = 0.447326, acc = 0.35\n",
      "[Validation] Batch ID = 5970, loss = 0.153006, acc = 0.85\n",
      "[Train] Batch ID = 5980, loss = 0.191163, acc = 0.9\n",
      "[Validation] Batch ID = 5980, loss = 0.230932, acc = 0.75\n",
      "[Train] Batch ID = 5990, loss = 0.458766, acc = 0.4\n",
      "[Validation] Batch ID = 5990, loss = 0.198265, acc = 0.8\n",
      "[Train] Batch ID = 6000, loss = 0.389869, acc = 0.55\n",
      "[Validation] Batch ID = 6000, loss = 0.12, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.195813 Best loss: 0.234846\n",
      "[TOTAL Validation] Batch ID = 6000, loss = 0.195813, acc = 0.844897959184\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.47351393100000005\n",
      "[Train] Batch ID = 6010, loss = 0.192418, acc = 0.9\n",
      "[Validation] Batch ID = 6010, loss = 0.198678, acc = 0.8\n",
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      "[Validation] Batch ID = 6750, loss = 0.159419, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 6760, loss = 0.130471, acc = 1.0\n",
      "[Validation] Batch ID = 6760, loss = 0.210766, acc = 0.8\n",
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      "[Train] Batch ID = 6990, loss = 0.162811, acc = 1.0\n",
      "[Validation] Batch ID = 6990, loss = 0.203997, acc = 0.85\n",
      "[Train] Batch ID = 7000, loss = 0.454905, acc = 0.45\n",
      "[Validation] Batch ID = 7000, loss = 0.179048, acc = 0.85\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.179093 Best loss: 0.195813\n",
      "[TOTAL Validation] Batch ID = 7000, loss = 0.179093, acc = 0.878911564626\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.4261625379000001\n",
      "[Train] Batch ID = 7010, loss = 0.398574, acc = 0.55\n",
      "[Validation] Batch ID = 7010, loss = 0.194709, acc = 0.9\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 7760, loss = 0.455377, acc = 0.4\n",
      "[Validation] Batch ID = 7760, loss = 0.106932, acc = 0.95\n",
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      "[Train] Batch ID = 7970, loss = 0.475356, acc = 0.25\n",
      "[Validation] Batch ID = 7970, loss = 0.227979, acc = 0.75\n",
      "[Train] Batch ID = 7980, loss = 0.389929, acc = 0.5\n",
      "[Validation] Batch ID = 7980, loss = 0.12287, acc = 1.0\n",
      "[Train] Batch ID = 7990, loss = 0.394402, acc = 0.55\n",
      "[Validation] Batch ID = 7990, loss = 0.13836, acc = 0.85\n",
      "[Train] Batch ID = 8000, loss = 0.138918, acc = 0.9\n",
      "[Validation] Batch ID = 8000, loss = 0.153268, acc = 0.95\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.156227 Best loss: 0.179093\n",
      "[TOTAL Validation] Batch ID = 8000, loss = 0.156227, acc = 0.900453514739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.3835462841100001\n",
      "[Train] Batch ID = 8010, loss = 0.378981, acc = 0.65\n",
      "[Validation] Batch ID = 8010, loss = 0.143862, acc = 0.95\n",
      "[Train] Batch ID = 8020, loss = 0.112697, acc = 0.95\n",
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      "[Validation] Batch ID = 8750, loss = 0.143952, acc = 0.85\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 8760, loss = 0.380403, acc = 0.55\n",
      "[Validation] Batch ID = 8760, loss = 0.15336, acc = 0.85\n",
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      "[Validation] Batch ID = 8930, loss = 0.128461, acc = 0.9\n",
      "[Train] Batch ID = 8940, loss = 0.409026, acc = 0.6\n",
      "[Validation] Batch ID = 8940, loss = 0.112625, acc = 0.95\n",
      "[Train] Batch ID = 8950, loss = 0.0592221, acc = 1.0\n",
      "[Validation] Batch ID = 8950, loss = 0.13545, acc = 0.9\n",
      "[Train] Batch ID = 8960, loss = 0.0747377, acc = 1.0\n",
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      "[Train] Batch ID = 8970, loss = 0.463144, acc = 0.4\n",
      "[Validation] Batch ID = 8970, loss = 0.158997, acc = 0.95\n",
      "[Train] Batch ID = 8980, loss = 0.0871961, acc = 1.0\n",
      "[Validation] Batch ID = 8980, loss = 0.126439, acc = 0.95\n",
      "[Train] Batch ID = 8990, loss = 0.0446739, acc = 1.0\n",
      "[Validation] Batch ID = 8990, loss = 0.175125, acc = 0.9\n",
      "[Train] Batch ID = 9000, loss = 0.0630365, acc = 1.0\n",
      "[Validation] Batch ID = 9000, loss = 0.142462, acc = 0.9\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.138203 Best loss: 0.156227\n",
      "[TOTAL Validation] Batch ID = 9000, loss = 0.138203, acc = 0.914285714286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.34519165569900007\n",
      "[Train] Batch ID = 9010, loss = 0.0737849, acc = 0.95\n",
      "[Validation] Batch ID = 9010, loss = 0.155062, acc = 0.85\n",
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      "[Validation] Batch ID = 9040, loss = 0.112452, acc = 1.0\n",
      "[Train] Batch ID = 9050, loss = 0.0749303, acc = 1.0\n",
      "[Validation] Batch ID = 9050, loss = 0.187996, acc = 0.85\n",
      "[Train] Batch ID = 9060, loss = 0.346644, acc = 0.6\n",
      "[Validation] Batch ID = 9060, loss = 0.0927557, acc = 1.0\n",
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      "[Validation] Batch ID = 9080, loss = 0.174754, acc = 0.8\n",
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      "[Validation] Batch ID = 9090, loss = 0.170861, acc = 0.85\n",
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      "[Validation] Batch ID = 9350, loss = 0.203201, acc = 0.75\n",
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      "[Validation] Batch ID = 9740, loss = 0.198652, acc = 0.8\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 9750, loss = 0.398089, acc = 0.5\n",
      "[Validation] Batch ID = 9750, loss = 0.10577, acc = 0.95\n",
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      "[Validation] Batch ID = 9910, loss = 0.0899762, acc = 0.95\n",
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      "[Validation] Batch ID = 9930, loss = 0.131944, acc = 0.95\n",
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      "[Validation] Batch ID = 9940, loss = 0.164353, acc = 0.8\n",
      "[Train] Batch ID = 9950, loss = 0.0698167, acc = 1.0\n",
      "[Validation] Batch ID = 9950, loss = 0.126318, acc = 0.9\n",
      "[Train] Batch ID = 9960, loss = 0.0984273, acc = 0.95\n",
      "[Validation] Batch ID = 9960, loss = 0.0843749, acc = 1.0\n",
      "[Train] Batch ID = 9970, loss = 0.173904, acc = 0.85\n",
      "[Validation] Batch ID = 9970, loss = 0.0869391, acc = 1.0\n",
      "[Train] Batch ID = 9980, loss = 0.100436, acc = 0.95\n",
      "[Validation] Batch ID = 9980, loss = 0.14568, acc = 0.9\n",
      "[Train] Batch ID = 9990, loss = 0.412257, acc = 0.45\n",
      "[Validation] Batch ID = 9990, loss = 0.127824, acc = 0.9\n",
      "[Train] Batch ID = 10000, loss = 0.362713, acc = 0.7\n",
      "[Validation] Batch ID = 10000, loss = 0.13494, acc = 0.85\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.122456 Best loss: 0.138203\n",
      "[TOTAL Validation] Batch ID = 10000, loss = 0.122456, acc = 0.916553287982\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.3106724901291001\n",
      "[Train] Batch ID = 10010, loss = 0.0823528, acc = 1.0\n",
      "[Validation] Batch ID = 10010, loss = 0.111079, acc = 0.95\n",
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      "[Train] Batch ID = 10720, loss = 0.0812816, acc = 1.0\n",
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      "[Train] Batch ID = 10730, loss = 0.0766214, acc = 1.0\n",
      "[Validation] Batch ID = 10730, loss = 0.128134, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 10740, loss = 0.0729083, acc = 1.0\n",
      "[Validation] Batch ID = 10740, loss = 0.127203, acc = 0.95\n",
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      "[Train] Batch ID = 10950, loss = 0.0631646, acc = 1.0\n",
      "[Validation] Batch ID = 10950, loss = 0.118808, acc = 0.9\n",
      "[Train] Batch ID = 10960, loss = 0.0892981, acc = 1.0\n",
      "[Validation] Batch ID = 10960, loss = 0.100498, acc = 1.0\n",
      "[Train] Batch ID = 10970, loss = 0.0801287, acc = 0.95\n",
      "[Validation] Batch ID = 10970, loss = 0.101499, acc = 0.9\n",
      "[Train] Batch ID = 10980, loss = 0.102281, acc = 0.9\n",
      "[Validation] Batch ID = 10980, loss = 0.139717, acc = 0.9\n",
      "[Train] Batch ID = 10990, loss = 0.0808437, acc = 0.95\n",
      "[Validation] Batch ID = 10990, loss = 0.147765, acc = 0.9\n",
      "[Train] Batch ID = 11000, loss = 0.436226, acc = 0.25\n",
      "[Validation] Batch ID = 11000, loss = 0.11211, acc = 0.95\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.112468 Best loss: 0.122456\n",
      "[TOTAL Validation] Batch ID = 11000, loss = 0.112468, acc = 0.934693877551\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.27960524111619006\n",
      "[Train] Batch ID = 11010, loss = 0.0596427, acc = 1.0\n",
      "[Validation] Batch ID = 11010, loss = 0.0632493, acc = 1.0\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 11740, loss = 0.0565066, acc = 1.0\n",
      "[Validation] Batch ID = 11740, loss = 0.124051, acc = 0.9\n",
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      "[Train] Batch ID = 11940, loss = 0.0559251, acc = 1.0\n",
      "[Validation] Batch ID = 11940, loss = 0.0998011, acc = 0.95\n",
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      "[Validation] Batch ID = 11950, loss = 0.0954872, acc = 0.95\n",
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      "[Validation] Batch ID = 11970, loss = 0.0692794, acc = 1.0\n",
      "[Train] Batch ID = 11980, loss = 0.43341, acc = 0.45\n",
      "[Validation] Batch ID = 11980, loss = 0.135483, acc = 0.9\n",
      "[Train] Batch ID = 11990, loss = 0.385238, acc = 0.65\n",
      "[Validation] Batch ID = 11990, loss = 0.132456, acc = 0.9\n",
      "[Train] Batch ID = 12000, loss = 0.0811419, acc = 1.0\n",
      "[Validation] Batch ID = 12000, loss = 0.0931817, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.101578 Best loss: 0.112468\n",
      "[TOTAL Validation] Batch ID = 12000, loss = 0.101578, acc = 0.945351473923\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.2516447170045711\n",
      "[Train] Batch ID = 12010, loss = 0.0876267, acc = 0.95\n",
      "[Validation] Batch ID = 12010, loss = 0.146934, acc = 0.95\n",
      "[Train] Batch ID = 12020, loss = 0.0643046, acc = 1.0\n",
      "[Validation] Batch ID = 12020, loss = 0.0727709, acc = 0.95\n",
      "[Train] Batch ID = 12030, loss = 0.445391, acc = 0.45\n",
      "[Validation] Batch ID = 12030, loss = 0.0811681, acc = 1.0\n",
      "[Train] Batch ID = 12040, loss = 0.40899, acc = 0.55\n",
      "[Validation] Batch ID = 12040, loss = 0.105822, acc = 0.9\n",
      "[Train] Batch ID = 12050, loss = 0.087554, acc = 1.0\n",
      "[Validation] Batch ID = 12050, loss = 0.102147, acc = 0.95\n",
      "[Train] Batch ID = 12060, loss = 0.485265, acc = 0.35\n",
      "[Validation] Batch ID = 12060, loss = 0.0492594, acc = 1.0\n",
      "[Train] Batch ID = 12070, loss = 0.401569, acc = 0.5\n",
      "[Validation] Batch ID = 12070, loss = 0.0555281, acc = 1.0\n",
      "[Train] Batch ID = 12080, loss = 0.425015, acc = 0.55\n",
      "[Validation] Batch ID = 12080, loss = 0.114615, acc = 0.9\n",
      "[Train] Batch ID = 12090, loss = 0.0292646, acc = 1.0\n",
      "[Validation] Batch ID = 12090, loss = 0.081599, acc = 0.95\n",
      "[Train] Batch ID = 12100, loss = 0.39357, acc = 0.4\n",
      "[Validation] Batch ID = 12100, loss = 0.0952682, acc = 0.9\n",
      "[Train] Batch ID = 12110, loss = 0.0738033, acc = 0.95\n",
      "[Validation] Batch ID = 12110, loss = 0.0997258, acc = 0.95\n",
      "[Train] Batch ID = 12120, loss = 0.0852137, acc = 0.9\n",
      "[Validation] Batch ID = 12120, loss = 0.0843021, acc = 0.9\n",
      "[Train] Batch ID = 12130, loss = 0.444795, acc = 0.3\n",
      "[Validation] Batch ID = 12130, loss = 0.0909521, acc = 0.95\n",
      "[Train] Batch ID = 12140, loss = 0.0437487, acc = 1.0\n",
      "[Validation] Batch ID = 12140, loss = 0.0963377, acc = 0.95\n",
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      "[Validation] Batch ID = 12170, loss = 0.126509, acc = 0.9\n",
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      "[Train] Batch ID = 12220, loss = 0.0477301, acc = 1.0\n",
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      "[Train] Batch ID = 12240, loss = 0.0290143, acc = 1.0\n",
      "[Validation] Batch ID = 12240, loss = 0.127088, acc = 0.85\n",
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      "[Validation] Batch ID = 12730, loss = 0.0639744, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 12740, loss = 0.040156, acc = 1.0\n",
      "[Validation] Batch ID = 12740, loss = 0.103962, acc = 1.0\n",
      "[Train] Batch ID = 12750, loss = 0.0808132, acc = 1.0\n",
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      "[Validation] Batch ID = 12890, loss = 0.104221, acc = 0.95\n",
      "[Train] Batch ID = 12900, loss = 0.0489218, acc = 1.0\n",
      "[Validation] Batch ID = 12900, loss = 0.105083, acc = 0.9\n",
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      "[Validation] Batch ID = 12910, loss = 0.109913, acc = 0.95\n",
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      "[Validation] Batch ID = 12920, loss = 0.0715672, acc = 1.0\n",
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      "[Validation] Batch ID = 12930, loss = 0.138669, acc = 0.9\n",
      "[Train] Batch ID = 12940, loss = 0.312011, acc = 0.7\n",
      "[Validation] Batch ID = 12940, loss = 0.0466437, acc = 1.0\n",
      "[Train] Batch ID = 12950, loss = 0.0820448, acc = 0.95\n",
      "[Validation] Batch ID = 12950, loss = 0.0837557, acc = 1.0\n",
      "[Train] Batch ID = 12960, loss = 0.0265126, acc = 1.0\n",
      "[Validation] Batch ID = 12960, loss = 0.142968, acc = 0.85\n",
      "[Train] Batch ID = 12970, loss = 0.053896, acc = 1.0\n",
      "[Validation] Batch ID = 12970, loss = 0.0832964, acc = 1.0\n",
      "[Train] Batch ID = 12980, loss = 0.0549353, acc = 1.0\n",
      "[Validation] Batch ID = 12980, loss = 0.0709919, acc = 1.0\n",
      "[Train] Batch ID = 12990, loss = 0.055643, acc = 1.0\n",
      "[Validation] Batch ID = 12990, loss = 0.0611367, acc = 0.95\n",
      "[Train] Batch ID = 13000, loss = 0.0214957, acc = 1.0\n",
      "[Validation] Batch ID = 13000, loss = 0.0978501, acc = 0.9\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0955421 Best loss: 0.101578\n",
      "[TOTAL Validation] Batch ID = 13000, loss = 0.0955421, acc = 0.949206349206\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.22648024530411398\n",
      "[Train] Batch ID = 13010, loss = 0.0503791, acc = 1.0\n",
      "[Validation] Batch ID = 13010, loss = 0.0982536, acc = 0.9\n",
      "[Train] Batch ID = 13020, loss = 0.0544654, acc = 1.0\n",
      "[Validation] Batch ID = 13020, loss = 0.0777491, acc = 0.95\n",
      "[Train] Batch ID = 13030, loss = 0.0657435, acc = 1.0\n",
      "[Validation] Batch ID = 13030, loss = 0.0902024, acc = 0.95\n",
      "[Train] Batch ID = 13040, loss = 0.394499, acc = 0.65\n",
      "[Validation] Batch ID = 13040, loss = 0.0829911, acc = 0.9\n",
      "[Train] Batch ID = 13050, loss = 0.408284, acc = 0.6\n",
      "[Validation] Batch ID = 13050, loss = 0.0376954, acc = 1.0\n",
      "[Train] Batch ID = 13060, loss = 0.0659685, acc = 1.0\n",
      "[Validation] Batch ID = 13060, loss = 0.0923961, acc = 0.95\n",
      "[Train] Batch ID = 13070, loss = 0.075504, acc = 0.95\n",
      "[Validation] Batch ID = 13070, loss = 0.0481456, acc = 1.0\n",
      "[Train] Batch ID = 13080, loss = 0.0403052, acc = 1.0\n",
      "[Validation] Batch ID = 13080, loss = 0.0710325, acc = 1.0\n",
      "[Train] Batch ID = 13090, loss = 0.058802, acc = 1.0\n",
      "[Validation] Batch ID = 13090, loss = 0.104143, acc = 0.95\n",
      "[Train] Batch ID = 13100, loss = 0.0626557, acc = 1.0\n",
      "[Validation] Batch ID = 13100, loss = 0.0645552, acc = 1.0\n",
      "[Train] Batch ID = 13110, loss = 0.0409879, acc = 1.0\n",
      "[Validation] Batch ID = 13110, loss = 0.0984864, acc = 0.95\n",
      "[Train] Batch ID = 13120, loss = 0.0498187, acc = 0.95\n",
      "[Validation] Batch ID = 13120, loss = 0.103355, acc = 0.95\n",
      "[Train] Batch ID = 13130, loss = 0.025531, acc = 1.0\n",
      "[Validation] Batch ID = 13130, loss = 0.124994, acc = 0.95\n",
      "[Train] Batch ID = 13140, loss = 0.0687485, acc = 1.0\n",
      "[Validation] Batch ID = 13140, loss = 0.0981277, acc = 1.0\n",
      "[Train] Batch ID = 13150, loss = 0.429531, acc = 0.45\n",
      "[Validation] Batch ID = 13150, loss = 0.160649, acc = 0.85\n",
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      "[Train] Batch ID = 13170, loss = 0.0711966, acc = 1.0\n",
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      "[Train] Batch ID = 13200, loss = 0.0663745, acc = 1.0\n",
      "[Validation] Batch ID = 13200, loss = 0.123579, acc = 0.9\n",
      "[Train] Batch ID = 13210, loss = 0.0534089, acc = 1.0\n",
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      "[Validation] Batch ID = 13220, loss = 0.122976, acc = 0.95\n",
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      "[Validation] Batch ID = 13230, loss = 0.0822079, acc = 1.0\n",
      "[Train] Batch ID = 13240, loss = 0.0530172, acc = 1.0\n",
      "[Validation] Batch ID = 13240, loss = 0.105477, acc = 0.95\n",
      "[Train] Batch ID = 13250, loss = 0.0461064, acc = 1.0\n",
      "[Validation] Batch ID = 13250, loss = 0.122394, acc = 0.85\n",
      "[Train] Batch ID = 13260, loss = 0.0663768, acc = 1.0\n",
      "[Validation] Batch ID = 13260, loss = 0.104133, acc = 0.95\n",
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      "[Train] Batch ID = 13300, loss = 0.0479358, acc = 1.0\n",
      "[Validation] Batch ID = 13300, loss = 0.0947019, acc = 0.95\n",
      "[Train] Batch ID = 13310, loss = 0.403448, acc = 0.45\n",
      "[Validation] Batch ID = 13310, loss = 0.0517848, acc = 1.0\n",
      "[Train] Batch ID = 13320, loss = 0.404894, acc = 0.6\n",
      "[Validation] Batch ID = 13320, loss = 0.0891838, acc = 0.95\n",
      "[Train] Batch ID = 13330, loss = 0.0356257, acc = 1.0\n",
      "[Validation] Batch ID = 13330, loss = 0.0737491, acc = 1.0\n",
      "[Train] Batch ID = 13340, loss = 0.0604875, acc = 1.0\n",
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      "[Validation] Batch ID = 13730, loss = 0.0733664, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 13740, loss = 0.0476083, acc = 1.0\n",
      "[Validation] Batch ID = 13740, loss = 0.138904, acc = 0.9\n",
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      "[Validation] Batch ID = 13930, loss = 0.130362, acc = 0.9\n",
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      "[Validation] Batch ID = 13950, loss = 0.0504628, acc = 1.0\n",
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      "[Validation] Batch ID = 13960, loss = 0.058778, acc = 1.0\n",
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      "[Validation] Batch ID = 13970, loss = 0.107968, acc = 0.85\n",
      "[Train] Batch ID = 13980, loss = 0.0357456, acc = 1.0\n",
      "[Validation] Batch ID = 13980, loss = 0.0665084, acc = 0.9\n",
      "[Train] Batch ID = 13990, loss = 0.0345792, acc = 1.0\n",
      "[Validation] Batch ID = 13990, loss = 0.0617387, acc = 1.0\n",
      "[Train] Batch ID = 14000, loss = 0.04424, acc = 1.0\n",
      "[Validation] Batch ID = 14000, loss = 0.119151, acc = 0.85\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0834171 Best loss: 0.0955421\n",
      "[TOTAL Validation] Batch ID = 14000, loss = 0.0834171, acc = 0.956009070295\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.20383222077370258\n",
      "[Train] Batch ID = 14010, loss = 0.0528335, acc = 1.0\n",
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      "[Validation] Batch ID = 14680, loss = 0.0823599, acc = 0.95\n",
      "[Train] Batch ID = 14690, loss = 0.0250269, acc = 1.0\n",
      "[Validation] Batch ID = 14690, loss = 0.0687871, acc = 0.95\n",
      "[Train] Batch ID = 14700, loss = 0.417856, acc = 0.55\n",
      "[Validation] Batch ID = 14700, loss = 0.0692025, acc = 1.0\n",
      "[Train] Batch ID = 14710, loss = 0.462304, acc = 0.4\n",
      "[Validation] Batch ID = 14710, loss = 0.0358768, acc = 1.0\n",
      "[Train] Batch ID = 14720, loss = 0.0783204, acc = 0.95\n",
      "[Validation] Batch ID = 14720, loss = 0.0616635, acc = 1.0\n",
      "[Train] Batch ID = 14730, loss = 0.0336201, acc = 1.0\n",
      "[Validation] Batch ID = 14730, loss = 0.0748586, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 14740, loss = 0.0378892, acc = 1.0\n",
      "[Validation] Batch ID = 14740, loss = 0.0574536, acc = 0.95\n",
      "[Train] Batch ID = 14750, loss = 0.0247226, acc = 1.0\n",
      "[Validation] Batch ID = 14750, loss = 0.0605167, acc = 1.0\n",
      "[Train] Batch ID = 14760, loss = 0.0350982, acc = 1.0\n",
      "[Validation] Batch ID = 14760, loss = 0.0686769, acc = 1.0\n",
      "[Train] Batch ID = 14770, loss = 0.0371263, acc = 1.0\n",
      "[Validation] Batch ID = 14770, loss = 0.0758147, acc = 0.95\n",
      "[Train] Batch ID = 14780, loss = 0.0496349, acc = 1.0\n",
      "[Validation] Batch ID = 14780, loss = 0.084246, acc = 1.0\n",
      "[Train] Batch ID = 14790, loss = 0.429585, acc = 0.45\n",
      "[Validation] Batch ID = 14790, loss = 0.104273, acc = 0.95\n",
      "[Train] Batch ID = 14800, loss = 0.0379683, acc = 1.0\n",
      "[Validation] Batch ID = 14800, loss = 0.057981, acc = 1.0\n",
      "[Train] Batch ID = 14810, loss = 0.09043, acc = 0.9\n",
      "[Validation] Batch ID = 14810, loss = 0.160156, acc = 0.8\n",
      "[Train] Batch ID = 14820, loss = 0.425455, acc = 0.55\n",
      "[Validation] Batch ID = 14820, loss = 0.0844297, acc = 0.95\n",
      "[Train] Batch ID = 14830, loss = 0.0265374, acc = 1.0\n",
      "[Validation] Batch ID = 14830, loss = 0.142876, acc = 0.9\n",
      "[Train] Batch ID = 14840, loss = 0.0337708, acc = 1.0\n",
      "[Validation] Batch ID = 14840, loss = 0.0656639, acc = 0.95\n",
      "[Train] Batch ID = 14850, loss = 0.0603046, acc = 1.0\n",
      "[Validation] Batch ID = 14850, loss = 0.0707767, acc = 1.0\n",
      "[Train] Batch ID = 14860, loss = 0.032899, acc = 1.0\n",
      "[Validation] Batch ID = 14860, loss = 0.0331612, acc = 1.0\n",
      "[Train] Batch ID = 14870, loss = 0.0440619, acc = 1.0\n",
      "[Validation] Batch ID = 14870, loss = 0.0766489, acc = 0.95\n",
      "[Train] Batch ID = 14880, loss = 0.418947, acc = 0.5\n",
      "[Validation] Batch ID = 14880, loss = 0.0483161, acc = 1.0\n",
      "[Train] Batch ID = 14890, loss = 0.0460016, acc = 1.0\n",
      "[Validation] Batch ID = 14890, loss = 0.0750105, acc = 1.0\n",
      "[Train] Batch ID = 14900, loss = 0.436554, acc = 0.45\n",
      "[Validation] Batch ID = 14900, loss = 0.0659338, acc = 1.0\n",
      "[Train] Batch ID = 14910, loss = 0.378112, acc = 0.45\n",
      "[Validation] Batch ID = 14910, loss = 0.0554683, acc = 1.0\n",
      "[Train] Batch ID = 14920, loss = 0.0636516, acc = 1.0\n",
      "[Validation] Batch ID = 14920, loss = 0.0599263, acc = 1.0\n",
      "[Train] Batch ID = 14930, loss = 0.0393334, acc = 0.95\n",
      "[Validation] Batch ID = 14930, loss = 0.0959402, acc = 0.95\n",
      "[Train] Batch ID = 14940, loss = 0.0336742, acc = 1.0\n",
      "[Validation] Batch ID = 14940, loss = 0.0881732, acc = 1.0\n",
      "[Train] Batch ID = 14950, loss = 0.0414142, acc = 1.0\n",
      "[Validation] Batch ID = 14950, loss = 0.0486474, acc = 1.0\n",
      "[Train] Batch ID = 14960, loss = 0.0291049, acc = 1.0\n",
      "[Validation] Batch ID = 14960, loss = 0.106727, acc = 0.95\n",
      "[Train] Batch ID = 14970, loss = 0.0399049, acc = 1.0\n",
      "[Validation] Batch ID = 14970, loss = 0.0679982, acc = 0.95\n",
      "[Train] Batch ID = 14980, loss = 0.0226859, acc = 1.0\n",
      "[Validation] Batch ID = 14980, loss = 0.0817084, acc = 1.0\n",
      "[Train] Batch ID = 14990, loss = 0.0452021, acc = 1.0\n",
      "[Validation] Batch ID = 14990, loss = 0.111505, acc = 0.9\n",
      "[Train] Batch ID = 15000, loss = 0.375013, acc = 0.45\n",
      "[Validation] Batch ID = 15000, loss = 0.0988057, acc = 0.95\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0839379 Best loss: 0.0834171\n",
      "[TOTAL Validation] Batch ID = 15000, loss = 0.0839379, acc = 0.959410430839\n",
      "Augmented Factor = 0.18344899869633233\n",
      "[Train] Batch ID = 15010, loss = 0.0766517, acc = 0.95\n",
      "[Validation] Batch ID = 15010, loss = 0.125541, acc = 0.9\n",
      "[Train] Batch ID = 15020, loss = 0.472335, acc = 0.3\n",
      "[Validation] Batch ID = 15020, loss = 0.0399391, acc = 1.0\n",
      "[Train] Batch ID = 15030, loss = 0.0365719, acc = 1.0\n",
      "[Validation] Batch ID = 15030, loss = 0.106482, acc = 0.95\n",
      "[Train] Batch ID = 15040, loss = 0.415267, acc = 0.45\n",
      "[Validation] Batch ID = 15040, loss = 0.0771407, acc = 0.9\n",
      "[Train] Batch ID = 15050, loss = 0.047137, acc = 1.0\n",
      "[Validation] Batch ID = 15050, loss = 0.0546042, acc = 1.0\n",
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      "[Validation] Batch ID = 15060, loss = 0.0918036, acc = 0.95\n",
      "[Train] Batch ID = 15070, loss = 0.304234, acc = 0.75\n",
      "[Validation] Batch ID = 15070, loss = 0.0763188, acc = 1.0\n",
      "[Train] Batch ID = 15080, loss = 0.0774152, acc = 0.95\n",
      "[Validation] Batch ID = 15080, loss = 0.0842965, acc = 0.9\n",
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      "[Validation] Batch ID = 15090, loss = 0.0724352, acc = 1.0\n",
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      "[Validation] Batch ID = 15120, loss = 0.0908767, acc = 0.95\n",
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      "[Validation] Batch ID = 15170, loss = 0.0920487, acc = 0.95\n",
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      "[Validation] Batch ID = 15330, loss = 0.144838, acc = 0.9\n",
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      "[Validation] Batch ID = 15440, loss = 0.0945817, acc = 0.9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 15450, loss = 0.0877656, acc = 0.95\n",
      "[Validation] Batch ID = 15450, loss = 0.0627259, acc = 1.0\n",
      "[Train] Batch ID = 15460, loss = 0.0408119, acc = 1.0\n",
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      "[Validation] Batch ID = 15470, loss = 0.0569105, acc = 1.0\n",
      "[Train] Batch ID = 15480, loss = 0.0223103, acc = 1.0\n",
      "[Validation] Batch ID = 15480, loss = 0.0422785, acc = 1.0\n",
      "[Train] Batch ID = 15490, loss = 0.0273107, acc = 1.0\n",
      "[Validation] Batch ID = 15490, loss = 0.0762473, acc = 1.0\n",
      "[Train] Batch ID = 15500, loss = 0.0430729, acc = 1.0\n",
      "[Validation] Batch ID = 15500, loss = 0.125852, acc = 0.9\n",
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      "[Validation] Batch ID = 15510, loss = 0.0488519, acc = 1.0\n",
      "[Train] Batch ID = 15520, loss = 0.525935, acc = 0.25\n",
      "[Validation] Batch ID = 15520, loss = 0.169346, acc = 0.85\n",
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      "[Validation] Batch ID = 15530, loss = 0.0618779, acc = 1.0\n",
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      "[Validation] Batch ID = 15540, loss = 0.0960047, acc = 0.95\n",
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      "[Validation] Batch ID = 15580, loss = 0.0495173, acc = 1.0\n",
      "[Train] Batch ID = 15590, loss = 0.0284758, acc = 1.0\n",
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      "[Validation] Batch ID = 15600, loss = 0.104387, acc = 0.9\n",
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      "[Train] Batch ID = 15650, loss = 0.0379017, acc = 1.0\n",
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      "[Train] Batch ID = 15680, loss = 0.0352837, acc = 1.0\n",
      "[Validation] Batch ID = 15680, loss = 0.0894183, acc = 0.9\n",
      "[Train] Batch ID = 15690, loss = 0.449138, acc = 0.45\n",
      "[Validation] Batch ID = 15690, loss = 0.0519698, acc = 1.0\n",
      "[Train] Batch ID = 15700, loss = 0.342331, acc = 0.7\n",
      "[Validation] Batch ID = 15700, loss = 0.0551495, acc = 0.95\n",
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      "[Validation] Batch ID = 15710, loss = 0.0466299, acc = 1.0\n",
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      "[Validation] Batch ID = 15770, loss = 0.105208, acc = 0.9\n",
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      "[Validation] Batch ID = 15800, loss = 0.0617532, acc = 0.95\n",
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      "[Validation] Batch ID = 15830, loss = 0.0512135, acc = 1.0\n",
      "[Train] Batch ID = 15840, loss = 0.0710461, acc = 1.0\n",
      "[Validation] Batch ID = 15840, loss = 0.133734, acc = 0.8\n",
      "[Train] Batch ID = 15850, loss = 0.0312496, acc = 1.0\n",
      "[Validation] Batch ID = 15850, loss = 0.0731832, acc = 0.95\n",
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      "[Validation] Batch ID = 15860, loss = 0.0971709, acc = 0.95\n",
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      "[Validation] Batch ID = 15870, loss = 0.0605031, acc = 1.0\n",
      "[Train] Batch ID = 15880, loss = 0.351953, acc = 0.7\n",
      "[Validation] Batch ID = 15880, loss = 0.0825284, acc = 1.0\n",
      "[Train] Batch ID = 15890, loss = 0.0389394, acc = 1.0\n",
      "[Validation] Batch ID = 15890, loss = 0.0418103, acc = 1.0\n",
      "[Train] Batch ID = 15900, loss = 0.0325468, acc = 1.0\n",
      "[Validation] Batch ID = 15900, loss = 0.0721928, acc = 1.0\n",
      "[Train] Batch ID = 15910, loss = 0.425795, acc = 0.4\n",
      "[Validation] Batch ID = 15910, loss = 0.0943444, acc = 0.95\n",
      "[Train] Batch ID = 15920, loss = 0.0274401, acc = 1.0\n",
      "[Validation] Batch ID = 15920, loss = 0.0468634, acc = 1.0\n",
      "[Train] Batch ID = 15930, loss = 0.411603, acc = 0.55\n",
      "[Validation] Batch ID = 15930, loss = 0.0456557, acc = 1.0\n",
      "[Train] Batch ID = 15940, loss = 0.304872, acc = 0.65\n",
      "[Validation] Batch ID = 15940, loss = 0.0676315, acc = 1.0\n",
      "[Train] Batch ID = 15950, loss = 0.0291504, acc = 1.0\n",
      "[Validation] Batch ID = 15950, loss = 0.129334, acc = 0.9\n",
      "[Train] Batch ID = 15960, loss = 0.0311697, acc = 1.0\n",
      "[Validation] Batch ID = 15960, loss = 0.0780279, acc = 1.0\n",
      "[Train] Batch ID = 15970, loss = 0.0577008, acc = 1.0\n",
      "[Validation] Batch ID = 15970, loss = 0.0648485, acc = 1.0\n",
      "[Train] Batch ID = 15980, loss = 0.383355, acc = 0.6\n",
      "[Validation] Batch ID = 15980, loss = 0.0501795, acc = 1.0\n",
      "[Train] Batch ID = 15990, loss = 0.0371123, acc = 1.0\n",
      "[Validation] Batch ID = 15990, loss = 0.0455013, acc = 1.0\n",
      "[Train] Batch ID = 16000, loss = 0.0255777, acc = 1.0\n",
      "[Validation] Batch ID = 16000, loss = 0.0461783, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0789337 Best loss: 0.0834171\n",
      "[TOTAL Validation] Batch ID = 16000, loss = 0.0789337, acc = 0.95873015873\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.1651040988266991\n",
      "[Train] Batch ID = 16010, loss = 0.0353077, acc = 1.0\n",
      "[Validation] Batch ID = 16010, loss = 0.0595721, acc = 1.0\n",
      "[Train] Batch ID = 16020, loss = 0.0261542, acc = 1.0\n",
      "[Validation] Batch ID = 16020, loss = 0.0876716, acc = 0.95\n",
      "[Train] Batch ID = 16030, loss = 0.0350816, acc = 1.0\n",
      "[Validation] Batch ID = 16030, loss = 0.0949218, acc = 0.9\n",
      "[Train] Batch ID = 16040, loss = 0.0408158, acc = 1.0\n",
      "[Validation] Batch ID = 16040, loss = 0.0991571, acc = 0.95\n",
      "[Train] Batch ID = 16050, loss = 0.0462519, acc = 1.0\n",
      "[Validation] Batch ID = 16050, loss = 0.13255, acc = 0.9\n",
      "[Train] Batch ID = 16060, loss = 0.0210365, acc = 1.0\n",
      "[Validation] Batch ID = 16060, loss = 0.0455196, acc = 1.0\n",
      "[Train] Batch ID = 16070, loss = 0.0480521, acc = 1.0\n",
      "[Validation] Batch ID = 16070, loss = 0.0765302, acc = 0.95\n",
      "[Train] Batch ID = 16080, loss = 0.442003, acc = 0.35\n",
      "[Validation] Batch ID = 16080, loss = 0.0560619, acc = 1.0\n",
      "[Train] Batch ID = 16090, loss = 0.037409, acc = 1.0\n",
      "[Validation] Batch ID = 16090, loss = 0.103272, acc = 0.8\n",
      "[Train] Batch ID = 16100, loss = 0.0411937, acc = 1.0\n",
      "[Validation] Batch ID = 16100, loss = 0.0620341, acc = 0.95\n",
      "[Train] Batch ID = 16110, loss = 0.0383068, acc = 0.95\n",
      "[Validation] Batch ID = 16110, loss = 0.0607852, acc = 1.0\n",
      "[Train] Batch ID = 16120, loss = 0.0294262, acc = 1.0\n",
      "[Validation] Batch ID = 16120, loss = 0.0958229, acc = 0.95\n",
      "[Train] Batch ID = 16130, loss = 0.422433, acc = 0.45\n",
      "[Validation] Batch ID = 16130, loss = 0.0743623, acc = 1.0\n",
      "[Train] Batch ID = 16140, loss = 0.0228588, acc = 1.0\n",
      "[Validation] Batch ID = 16140, loss = 0.109262, acc = 0.9\n",
      "[Train] Batch ID = 16150, loss = 0.06605, acc = 0.95\n",
      "[Validation] Batch ID = 16150, loss = 0.0309544, acc = 1.0\n",
      "[Train] Batch ID = 16160, loss = 0.057612, acc = 1.0\n",
      "[Validation] Batch ID = 16160, loss = 0.143432, acc = 0.9\n",
      "[Train] Batch ID = 16170, loss = 0.0526586, acc = 1.0\n",
      "[Validation] Batch ID = 16170, loss = 0.0922498, acc = 0.95\n",
      "[Train] Batch ID = 16180, loss = 0.0463491, acc = 1.0\n",
      "[Validation] Batch ID = 16180, loss = 0.0514451, acc = 1.0\n",
      "[Train] Batch ID = 16190, loss = 0.0487543, acc = 1.0\n",
      "[Validation] Batch ID = 16190, loss = 0.129633, acc = 0.8\n",
      "[Train] Batch ID = 16200, loss = 0.0700651, acc = 1.0\n",
      "[Validation] Batch ID = 16200, loss = 0.0695771, acc = 1.0\n",
      "[Train] Batch ID = 16210, loss = 0.0295396, acc = 1.0\n",
      "[Validation] Batch ID = 16210, loss = 0.0983573, acc = 0.95\n",
      "[Train] Batch ID = 16220, loss = 0.385177, acc = 0.45\n",
      "[Validation] Batch ID = 16220, loss = 0.0749544, acc = 0.95\n",
      "[Train] Batch ID = 16230, loss = 0.047466, acc = 1.0\n",
      "[Validation] Batch ID = 16230, loss = 0.0954246, acc = 0.95\n",
      "[Train] Batch ID = 16240, loss = 0.032279, acc = 1.0\n",
      "[Validation] Batch ID = 16240, loss = 0.0875464, acc = 0.95\n",
      "[Train] Batch ID = 16250, loss = 0.0355562, acc = 1.0\n",
      "[Validation] Batch ID = 16250, loss = 0.0671687, acc = 1.0\n",
      "[Train] Batch ID = 16260, loss = 0.0531773, acc = 1.0\n",
      "[Validation] Batch ID = 16260, loss = 0.0416006, acc = 1.0\n",
      "[Train] Batch ID = 16270, loss = 0.0288898, acc = 1.0\n",
      "[Validation] Batch ID = 16270, loss = 0.0390441, acc = 0.95\n",
      "[Train] Batch ID = 16280, loss = 0.0418203, acc = 1.0\n",
      "[Validation] Batch ID = 16280, loss = 0.0591247, acc = 1.0\n",
      "[Train] Batch ID = 16290, loss = 0.0247623, acc = 1.0\n",
      "[Validation] Batch ID = 16290, loss = 0.114241, acc = 0.9\n",
      "[Train] Batch ID = 16300, loss = 0.0257312, acc = 1.0\n",
      "[Validation] Batch ID = 16300, loss = 0.103088, acc = 0.9\n",
      "[Train] Batch ID = 16310, loss = 0.0328302, acc = 1.0\n",
      "[Validation] Batch ID = 16310, loss = 0.0584158, acc = 0.95\n",
      "[Train] Batch ID = 16320, loss = 0.257869, acc = 0.7\n",
      "[Validation] Batch ID = 16320, loss = 0.0670368, acc = 1.0\n",
      "[Train] Batch ID = 16330, loss = 0.0345477, acc = 1.0\n",
      "[Validation] Batch ID = 16330, loss = 0.0613693, acc = 1.0\n",
      "[Train] Batch ID = 16340, loss = 0.038227, acc = 1.0\n",
      "[Validation] Batch ID = 16340, loss = 0.0750116, acc = 0.9\n",
      "[Train] Batch ID = 16350, loss = 0.0564016, acc = 0.95\n",
      "[Validation] Batch ID = 16350, loss = 0.0760409, acc = 0.95\n",
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      "[Validation] Batch ID = 16360, loss = 0.0970162, acc = 0.9\n",
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      "[Validation] Batch ID = 16420, loss = 0.0409783, acc = 1.0\n",
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      "[Validation] Batch ID = 16430, loss = 0.0623502, acc = 1.0\n",
      "[Train] Batch ID = 16440, loss = 0.389791, acc = 0.55\n",
      "[Validation] Batch ID = 16440, loss = 0.137976, acc = 0.9\n",
      "[Train] Batch ID = 16450, loss = 0.0286302, acc = 1.0\n",
      "[Validation] Batch ID = 16450, loss = 0.129021, acc = 0.9\n",
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      "[Validation] Batch ID = 16460, loss = 0.0867867, acc = 1.0\n",
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      "[Validation] Batch ID = 16480, loss = 0.0497487, acc = 1.0\n",
      "[Train] Batch ID = 16490, loss = 0.0586644, acc = 1.0\n",
      "[Validation] Batch ID = 16490, loss = 0.063651, acc = 1.0\n",
      "[Train] Batch ID = 16500, loss = 0.3549, acc = 0.55\n",
      "[Validation] Batch ID = 16500, loss = 0.0751724, acc = 1.0\n",
      "[Train] Batch ID = 16510, loss = 0.0327242, acc = 1.0\n",
      "[Validation] Batch ID = 16510, loss = 0.104413, acc = 0.95\n",
      "[Train] Batch ID = 16520, loss = 0.0132981, acc = 1.0\n",
      "[Validation] Batch ID = 16520, loss = 0.0763815, acc = 0.95\n",
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      "[Validation] Batch ID = 16530, loss = 0.0807147, acc = 0.95\n",
      "[Train] Batch ID = 16540, loss = 0.0250935, acc = 1.0\n",
      "[Validation] Batch ID = 16540, loss = 0.0763775, acc = 0.95\n",
      "[Train] Batch ID = 16550, loss = 0.033944, acc = 1.0\n",
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      "[Train] Batch ID = 16600, loss = 0.0250793, acc = 1.0\n",
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      "[Validation] Batch ID = 16630, loss = 0.0638098, acc = 1.0\n",
      "[Train] Batch ID = 16640, loss = 0.386698, acc = 0.55\n",
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      "[Validation] Batch ID = 16730, loss = 0.0909823, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 16740, loss = 0.315317, acc = 0.6\n",
      "[Validation] Batch ID = 16740, loss = 0.0591392, acc = 0.95\n",
      "[Train] Batch ID = 16750, loss = 0.0428586, acc = 1.0\n",
      "[Validation] Batch ID = 16750, loss = 0.056072, acc = 1.0\n",
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      "[Validation] Batch ID = 16770, loss = 0.0660429, acc = 1.0\n",
      "[Train] Batch ID = 16780, loss = 0.0225444, acc = 1.0\n",
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      "[Validation] Batch ID = 16790, loss = 0.067933, acc = 0.95\n",
      "[Train] Batch ID = 16800, loss = 0.349192, acc = 0.6\n",
      "[Validation] Batch ID = 16800, loss = 0.0477667, acc = 1.0\n",
      "[Train] Batch ID = 16810, loss = 0.0212368, acc = 1.0\n",
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      "[Train] Batch ID = 16840, loss = 0.387559, acc = 0.55\n",
      "[Validation] Batch ID = 16840, loss = 0.0647686, acc = 1.0\n",
      "[Train] Batch ID = 16850, loss = 0.375307, acc = 0.6\n",
      "[Validation] Batch ID = 16850, loss = 0.0973263, acc = 1.0\n",
      "[Train] Batch ID = 16860, loss = 0.0331754, acc = 1.0\n",
      "[Validation] Batch ID = 16860, loss = 0.0343838, acc = 1.0\n",
      "[Train] Batch ID = 16870, loss = 0.381365, acc = 0.55\n",
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      "[Train] Batch ID = 16890, loss = 0.0197988, acc = 1.0\n",
      "[Validation] Batch ID = 16890, loss = 0.0951911, acc = 0.9\n",
      "[Train] Batch ID = 16900, loss = 0.037736, acc = 1.0\n",
      "[Validation] Batch ID = 16900, loss = 0.0753266, acc = 0.95\n",
      "[Train] Batch ID = 16910, loss = 0.359804, acc = 0.65\n",
      "[Validation] Batch ID = 16910, loss = 0.0732763, acc = 1.0\n",
      "[Train] Batch ID = 16920, loss = 0.0275128, acc = 1.0\n",
      "[Validation] Batch ID = 16920, loss = 0.0722603, acc = 0.95\n",
      "[Train] Batch ID = 16930, loss = 0.0203153, acc = 1.0\n",
      "[Validation] Batch ID = 16930, loss = 0.0207213, acc = 1.0\n",
      "[Train] Batch ID = 16940, loss = 0.337572, acc = 0.7\n",
      "[Validation] Batch ID = 16940, loss = 0.0488883, acc = 1.0\n",
      "[Train] Batch ID = 16950, loss = 0.335014, acc = 0.7\n",
      "[Validation] Batch ID = 16950, loss = 0.0743098, acc = 0.95\n",
      "[Train] Batch ID = 16960, loss = 0.0298646, acc = 1.0\n",
      "[Validation] Batch ID = 16960, loss = 0.0847393, acc = 0.95\n",
      "[Train] Batch ID = 16970, loss = 0.0372676, acc = 1.0\n",
      "[Validation] Batch ID = 16970, loss = 0.0330958, acc = 1.0\n",
      "[Train] Batch ID = 16980, loss = 0.0392484, acc = 1.0\n",
      "[Validation] Batch ID = 16980, loss = 0.074444, acc = 1.0\n",
      "[Train] Batch ID = 16990, loss = 0.0238873, acc = 1.0\n",
      "[Validation] Batch ID = 16990, loss = 0.0537983, acc = 1.0\n",
      "[Train] Batch ID = 17000, loss = 0.381534, acc = 0.55\n",
      "[Validation] Batch ID = 17000, loss = 0.114423, acc = 0.9\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0719007 Best loss: 0.0789337\n",
      "[TOTAL Validation] Batch ID = 17000, loss = 0.0719007, acc = 0.959863945578\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.1485936889440292\n",
      "[Train] Batch ID = 17010, loss = 0.0273065, acc = 1.0\n",
      "[Validation] Batch ID = 17010, loss = 0.15855, acc = 0.85\n",
      "[Train] Batch ID = 17020, loss = 0.0319995, acc = 1.0\n",
      "[Validation] Batch ID = 17020, loss = 0.0332954, acc = 1.0\n",
      "[Train] Batch ID = 17030, loss = 0.0260435, acc = 1.0\n",
      "[Validation] Batch ID = 17030, loss = 0.03969, acc = 1.0\n",
      "[Train] Batch ID = 17040, loss = 0.0277735, acc = 1.0\n",
      "[Validation] Batch ID = 17040, loss = 0.0671851, acc = 0.95\n",
      "[Train] Batch ID = 17050, loss = 0.0294385, acc = 1.0\n",
      "[Validation] Batch ID = 17050, loss = 0.0643661, acc = 1.0\n",
      "[Train] Batch ID = 17060, loss = 0.360151, acc = 0.65\n",
      "[Validation] Batch ID = 17060, loss = 0.0831838, acc = 1.0\n",
      "[Train] Batch ID = 17070, loss = 0.0375042, acc = 1.0\n",
      "[Validation] Batch ID = 17070, loss = 0.108511, acc = 0.9\n",
      "[Train] Batch ID = 17080, loss = 0.0414867, acc = 1.0\n",
      "[Validation] Batch ID = 17080, loss = 0.0575123, acc = 1.0\n",
      "[Train] Batch ID = 17090, loss = 0.0333895, acc = 1.0\n",
      "[Validation] Batch ID = 17090, loss = 0.110666, acc = 0.9\n",
      "[Train] Batch ID = 17100, loss = 0.0334517, acc = 1.0\n",
      "[Validation] Batch ID = 17100, loss = 0.106065, acc = 0.9\n",
      "[Train] Batch ID = 17110, loss = 0.0161343, acc = 1.0\n",
      "[Validation] Batch ID = 17110, loss = 0.0828568, acc = 0.95\n",
      "[Train] Batch ID = 17120, loss = 0.0425977, acc = 1.0\n",
      "[Validation] Batch ID = 17120, loss = 0.0303661, acc = 1.0\n",
      "[Train] Batch ID = 17130, loss = 0.327566, acc = 0.55\n",
      "[Validation] Batch ID = 17130, loss = 0.10823, acc = 0.9\n",
      "[Train] Batch ID = 17140, loss = 0.0280466, acc = 1.0\n",
      "[Validation] Batch ID = 17140, loss = 0.0341099, acc = 1.0\n",
      "[Train] Batch ID = 17150, loss = 0.321418, acc = 0.65\n",
      "[Validation] Batch ID = 17150, loss = 0.0217722, acc = 1.0\n",
      "[Train] Batch ID = 17160, loss = 0.0312786, acc = 1.0\n",
      "[Validation] Batch ID = 17160, loss = 0.0789797, acc = 0.95\n",
      "[Train] Batch ID = 17170, loss = 0.0482306, acc = 1.0\n",
      "[Validation] Batch ID = 17170, loss = 0.0632197, acc = 1.0\n",
      "[Train] Batch ID = 17180, loss = 0.365908, acc = 0.65\n",
      "[Validation] Batch ID = 17180, loss = 0.0437711, acc = 1.0\n",
      "[Train] Batch ID = 17190, loss = 0.0334021, acc = 1.0\n",
      "[Validation] Batch ID = 17190, loss = 0.103503, acc = 0.95\n",
      "[Train] Batch ID = 17200, loss = 0.0468718, acc = 1.0\n",
      "[Validation] Batch ID = 17200, loss = 0.0477148, acc = 1.0\n",
      "[Train] Batch ID = 17210, loss = 0.026405, acc = 1.0\n",
      "[Validation] Batch ID = 17210, loss = 0.0780059, acc = 1.0\n",
      "[Train] Batch ID = 17220, loss = 0.0328804, acc = 1.0\n",
      "[Validation] Batch ID = 17220, loss = 0.0716727, acc = 0.95\n",
      "[Train] Batch ID = 17230, loss = 0.0457033, acc = 1.0\n",
      "[Validation] Batch ID = 17230, loss = 0.083605, acc = 0.95\n",
      "[Train] Batch ID = 17240, loss = 0.0362824, acc = 1.0\n",
      "[Validation] Batch ID = 17240, loss = 0.0233049, acc = 1.0\n",
      "[Train] Batch ID = 17250, loss = 0.0621164, acc = 0.95\n",
      "[Validation] Batch ID = 17250, loss = 0.0590741, acc = 0.95\n",
      "[Train] Batch ID = 17260, loss = 0.0183561, acc = 1.0\n",
      "[Validation] Batch ID = 17260, loss = 0.102331, acc = 0.9\n",
      "[Train] Batch ID = 17270, loss = 0.032724, acc = 1.0\n",
      "[Validation] Batch ID = 17270, loss = 0.06783, acc = 0.95\n",
      "[Train] Batch ID = 17280, loss = 0.0362738, acc = 1.0\n",
      "[Validation] Batch ID = 17280, loss = 0.164909, acc = 0.9\n",
      "[Train] Batch ID = 17290, loss = 0.036713, acc = 1.0\n",
      "[Validation] Batch ID = 17290, loss = 0.0614372, acc = 1.0\n",
      "[Train] Batch ID = 17300, loss = 0.0266447, acc = 1.0\n",
      "[Validation] Batch ID = 17300, loss = 0.0676663, acc = 1.0\n",
      "[Train] Batch ID = 17310, loss = 0.0414553, acc = 1.0\n",
      "[Validation] Batch ID = 17310, loss = 0.035709, acc = 1.0\n",
      "[Train] Batch ID = 17320, loss = 0.407823, acc = 0.55\n",
      "[Validation] Batch ID = 17320, loss = 0.054164, acc = 1.0\n",
      "[Train] Batch ID = 17330, loss = 0.0668108, acc = 1.0\n",
      "[Validation] Batch ID = 17330, loss = 0.0462152, acc = 1.0\n",
      "[Train] Batch ID = 17340, loss = 0.0314256, acc = 1.0\n",
      "[Validation] Batch ID = 17340, loss = 0.0492794, acc = 1.0\n",
      "[Train] Batch ID = 17350, loss = 0.0365556, acc = 1.0\n",
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      "[Validation] Batch ID = 17630, loss = 0.0799962, acc = 0.95\n",
      "[Train] Batch ID = 17640, loss = 0.0206912, acc = 1.0\n",
      "[Validation] Batch ID = 17640, loss = 0.0196065, acc = 1.0\n",
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      "[Validation] Batch ID = 17670, loss = 0.0604884, acc = 1.0\n",
      "[Train] Batch ID = 17680, loss = 0.303756, acc = 0.75\n",
      "[Validation] Batch ID = 17680, loss = 0.0643017, acc = 0.95\n",
      "[Train] Batch ID = 17690, loss = 0.0186255, acc = 1.0\n",
      "[Validation] Batch ID = 17690, loss = 0.155591, acc = 0.8\n",
      "[Train] Batch ID = 17700, loss = 0.0162926, acc = 1.0\n",
      "[Validation] Batch ID = 17700, loss = 0.0546361, acc = 1.0\n",
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      "[Validation] Batch ID = 17710, loss = 0.0787747, acc = 1.0\n",
      "[Train] Batch ID = 17720, loss = 0.0358227, acc = 1.0\n",
      "[Validation] Batch ID = 17720, loss = 0.0565716, acc = 1.0\n",
      "[Train] Batch ID = 17730, loss = 0.0303005, acc = 1.0\n",
      "[Validation] Batch ID = 17730, loss = 0.0871816, acc = 0.9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 17740, loss = 0.0314106, acc = 1.0\n",
      "[Validation] Batch ID = 17740, loss = 0.0644276, acc = 0.95\n",
      "[Train] Batch ID = 17750, loss = 0.0474717, acc = 1.0\n",
      "[Validation] Batch ID = 17750, loss = 0.0820564, acc = 0.95\n",
      "[Train] Batch ID = 17760, loss = 0.0341341, acc = 1.0\n",
      "[Validation] Batch ID = 17760, loss = 0.0414012, acc = 1.0\n",
      "[Train] Batch ID = 17770, loss = 0.0247169, acc = 1.0\n",
      "[Validation] Batch ID = 17770, loss = 0.0824983, acc = 0.95\n",
      "[Train] Batch ID = 17780, loss = 0.0292585, acc = 1.0\n",
      "[Validation] Batch ID = 17780, loss = 0.0327708, acc = 1.0\n",
      "[Train] Batch ID = 17790, loss = 0.0322887, acc = 1.0\n",
      "[Validation] Batch ID = 17790, loss = 0.097951, acc = 0.95\n",
      "[Train] Batch ID = 17800, loss = 0.0535966, acc = 0.95\n",
      "[Validation] Batch ID = 17800, loss = 0.060702, acc = 0.95\n",
      "[Train] Batch ID = 17810, loss = 0.0189311, acc = 1.0\n",
      "[Validation] Batch ID = 17810, loss = 0.115004, acc = 0.9\n",
      "[Train] Batch ID = 17820, loss = 0.028964, acc = 1.0\n",
      "[Validation] Batch ID = 17820, loss = 0.0805397, acc = 0.95\n",
      "[Train] Batch ID = 17830, loss = 0.0236659, acc = 1.0\n",
      "[Validation] Batch ID = 17830, loss = 0.0581435, acc = 0.95\n",
      "[Train] Batch ID = 17840, loss = 0.430743, acc = 0.55\n",
      "[Validation] Batch ID = 17840, loss = 0.0714098, acc = 0.9\n",
      "[Train] Batch ID = 17850, loss = 0.318578, acc = 0.6\n",
      "[Validation] Batch ID = 17850, loss = 0.0427139, acc = 1.0\n",
      "[Train] Batch ID = 17860, loss = 0.329705, acc = 0.6\n",
      "[Validation] Batch ID = 17860, loss = 0.107295, acc = 0.9\n",
      "[Train] Batch ID = 17870, loss = 0.0472684, acc = 0.95\n",
      "[Validation] Batch ID = 17870, loss = 0.050019, acc = 1.0\n",
      "[Train] Batch ID = 17880, loss = 0.0489014, acc = 1.0\n",
      "[Validation] Batch ID = 17880, loss = 0.0493645, acc = 1.0\n",
      "[Train] Batch ID = 17890, loss = 0.0278144, acc = 1.0\n",
      "[Validation] Batch ID = 17890, loss = 0.0903923, acc = 1.0\n",
      "[Train] Batch ID = 17900, loss = 0.0298431, acc = 1.0\n",
      "[Validation] Batch ID = 17900, loss = 0.0670674, acc = 1.0\n",
      "[Train] Batch ID = 17910, loss = 0.0402405, acc = 1.0\n",
      "[Validation] Batch ID = 17910, loss = 0.0579323, acc = 1.0\n",
      "[Train] Batch ID = 17920, loss = 0.02882, acc = 1.0\n",
      "[Validation] Batch ID = 17920, loss = 0.0798093, acc = 0.95\n",
      "[Train] Batch ID = 17930, loss = 0.0239792, acc = 1.0\n",
      "[Validation] Batch ID = 17930, loss = 0.109043, acc = 0.95\n",
      "[Train] Batch ID = 17940, loss = 0.318559, acc = 0.6\n",
      "[Validation] Batch ID = 17940, loss = 0.0634367, acc = 0.95\n",
      "[Train] Batch ID = 17950, loss = 0.026183, acc = 1.0\n",
      "[Validation] Batch ID = 17950, loss = 0.0550755, acc = 1.0\n",
      "[Train] Batch ID = 17960, loss = 0.0327054, acc = 1.0\n",
      "[Validation] Batch ID = 17960, loss = 0.0572528, acc = 0.95\n",
      "[Train] Batch ID = 17970, loss = 0.0372645, acc = 1.0\n",
      "[Validation] Batch ID = 17970, loss = 0.0686954, acc = 0.95\n",
      "[Train] Batch ID = 17980, loss = 0.0144088, acc = 1.0\n",
      "[Validation] Batch ID = 17980, loss = 0.0575788, acc = 1.0\n",
      "[Train] Batch ID = 17990, loss = 0.0275876, acc = 1.0\n",
      "[Validation] Batch ID = 17990, loss = 0.0457519, acc = 1.0\n",
      "[Train] Batch ID = 18000, loss = 0.0257816, acc = 1.0\n",
      "[Validation] Batch ID = 18000, loss = 0.087523, acc = 0.95\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0658299 Best loss: 0.0719007\n",
      "[TOTAL Validation] Batch ID = 18000, loss = 0.0658299, acc = 0.966666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.13373432004962627\n",
      "[Train] Batch ID = 18010, loss = 0.0465346, acc = 0.95\n",
      "[Validation] Batch ID = 18010, loss = 0.0563226, acc = 0.95\n",
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      "[Validation] Batch ID = 18020, loss = 0.048101, acc = 0.95\n",
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      "[Validation] Batch ID = 18030, loss = 0.0768241, acc = 0.95\n",
      "[Train] Batch ID = 18040, loss = 0.0242516, acc = 1.0\n",
      "[Validation] Batch ID = 18040, loss = 0.073717, acc = 1.0\n",
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      "[Validation] Batch ID = 18050, loss = 0.0438641, acc = 1.0\n",
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      "[Validation] Batch ID = 18070, loss = 0.0768941, acc = 0.95\n",
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      "[Train] Batch ID = 18680, loss = 0.0126921, acc = 1.0\n",
      "[Validation] Batch ID = 18680, loss = 0.0256377, acc = 1.0\n",
      "[Train] Batch ID = 18690, loss = 0.0215801, acc = 1.0\n",
      "[Validation] Batch ID = 18690, loss = 0.0605209, acc = 1.0\n",
      "[Train] Batch ID = 18700, loss = 0.0314722, acc = 1.0\n",
      "[Validation] Batch ID = 18700, loss = 0.0944133, acc = 0.95\n",
      "[Train] Batch ID = 18710, loss = 0.379751, acc = 0.55\n",
      "[Validation] Batch ID = 18710, loss = 0.0423924, acc = 1.0\n",
      "[Train] Batch ID = 18720, loss = 0.0169265, acc = 1.0\n",
      "[Validation] Batch ID = 18720, loss = 0.0940471, acc = 0.9\n",
      "[Train] Batch ID = 18730, loss = 0.0382592, acc = 1.0\n",
      "[Validation] Batch ID = 18730, loss = 0.0576724, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 18740, loss = 0.015381, acc = 1.0\n",
      "[Validation] Batch ID = 18740, loss = 0.0883818, acc = 0.95\n",
      "[Train] Batch ID = 18750, loss = 0.0212053, acc = 1.0\n",
      "[Validation] Batch ID = 18750, loss = 0.0673498, acc = 1.0\n",
      "[Train] Batch ID = 18760, loss = 0.0430443, acc = 1.0\n",
      "[Validation] Batch ID = 18760, loss = 0.0464193, acc = 1.0\n",
      "[Train] Batch ID = 18770, loss = 0.0287668, acc = 1.0\n",
      "[Validation] Batch ID = 18770, loss = 0.077454, acc = 0.9\n",
      "[Train] Batch ID = 18780, loss = 0.386508, acc = 0.5\n",
      "[Validation] Batch ID = 18780, loss = 0.14664, acc = 0.9\n",
      "[Train] Batch ID = 18790, loss = 0.0364632, acc = 1.0\n",
      "[Validation] Batch ID = 18790, loss = 0.190424, acc = 0.85\n",
      "[Train] Batch ID = 18800, loss = 0.030452, acc = 1.0\n",
      "[Validation] Batch ID = 18800, loss = 0.0959069, acc = 0.95\n",
      "[Train] Batch ID = 18810, loss = 0.388694, acc = 0.55\n",
      "[Validation] Batch ID = 18810, loss = 0.0611176, acc = 0.95\n",
      "[Train] Batch ID = 18820, loss = 0.0188242, acc = 1.0\n",
      "[Validation] Batch ID = 18820, loss = 0.0545211, acc = 1.0\n",
      "[Train] Batch ID = 18830, loss = 0.0288639, acc = 1.0\n",
      "[Validation] Batch ID = 18830, loss = 0.0494217, acc = 1.0\n",
      "[Train] Batch ID = 18840, loss = 0.0312834, acc = 1.0\n",
      "[Validation] Batch ID = 18840, loss = 0.0754053, acc = 0.95\n",
      "[Train] Batch ID = 18850, loss = 0.0371242, acc = 1.0\n",
      "[Validation] Batch ID = 18850, loss = 0.0472485, acc = 1.0\n",
      "[Train] Batch ID = 18860, loss = 0.0115143, acc = 1.0\n",
      "[Validation] Batch ID = 18860, loss = 0.0381266, acc = 1.0\n",
      "[Train] Batch ID = 18870, loss = 0.0242519, acc = 1.0\n",
      "[Validation] Batch ID = 18870, loss = 0.0380129, acc = 1.0\n",
      "[Train] Batch ID = 18880, loss = 0.0477796, acc = 1.0\n",
      "[Validation] Batch ID = 18880, loss = 0.0833435, acc = 0.95\n",
      "[Train] Batch ID = 18890, loss = 0.0250389, acc = 1.0\n",
      "[Validation] Batch ID = 18890, loss = 0.0505219, acc = 0.95\n",
      "[Train] Batch ID = 18900, loss = 0.521987, acc = 0.3\n",
      "[Validation] Batch ID = 18900, loss = 0.101279, acc = 0.9\n",
      "[Train] Batch ID = 18910, loss = 0.026744, acc = 1.0\n",
      "[Validation] Batch ID = 18910, loss = 0.0859974, acc = 0.9\n",
      "[Train] Batch ID = 18920, loss = 0.422156, acc = 0.5\n",
      "[Validation] Batch ID = 18920, loss = 0.0589617, acc = 1.0\n",
      "[Train] Batch ID = 18930, loss = 0.0254454, acc = 1.0\n",
      "[Validation] Batch ID = 18930, loss = 0.0907798, acc = 0.95\n",
      "[Train] Batch ID = 18940, loss = 0.0266715, acc = 1.0\n",
      "[Validation] Batch ID = 18940, loss = 0.0638264, acc = 0.95\n",
      "[Train] Batch ID = 18950, loss = 0.0472053, acc = 1.0\n",
      "[Validation] Batch ID = 18950, loss = 0.0520192, acc = 0.95\n",
      "[Train] Batch ID = 18960, loss = 0.0256693, acc = 1.0\n",
      "[Validation] Batch ID = 18960, loss = 0.0370073, acc = 1.0\n",
      "[Train] Batch ID = 18970, loss = 0.0324202, acc = 1.0\n",
      "[Validation] Batch ID = 18970, loss = 0.0634451, acc = 0.95\n",
      "[Train] Batch ID = 18980, loss = 0.0349643, acc = 1.0\n",
      "[Validation] Batch ID = 18980, loss = 0.0452116, acc = 1.0\n",
      "[Train] Batch ID = 18990, loss = 0.0175144, acc = 1.0\n",
      "[Validation] Batch ID = 18990, loss = 0.0613471, acc = 1.0\n",
      "[Train] Batch ID = 19000, loss = 0.0117237, acc = 1.0\n",
      "[Validation] Batch ID = 19000, loss = 0.0834245, acc = 0.95\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0658808 Best loss: 0.0658299\n",
      "[TOTAL Validation] Batch ID = 19000, loss = 0.0658808, acc = 0.961904761905\n",
      "Augmented Factor = 0.12036088804466365\n",
      "[Train] Batch ID = 19010, loss = 0.0305123, acc = 1.0\n",
      "[Validation] Batch ID = 19010, loss = 0.0632799, acc = 1.0\n",
      "[Train] Batch ID = 19020, loss = 0.037585, acc = 0.95\n",
      "[Validation] Batch ID = 19020, loss = 0.0771329, acc = 1.0\n",
      "[Train] Batch ID = 19030, loss = 0.0232085, acc = 1.0\n",
      "[Validation] Batch ID = 19030, loss = 0.0703891, acc = 0.95\n",
      "[Train] Batch ID = 19040, loss = 0.0148782, acc = 1.0\n",
      "[Validation] Batch ID = 19040, loss = 0.0380411, acc = 1.0\n",
      "[Train] Batch ID = 19050, loss = 0.0338117, acc = 1.0\n",
      "[Validation] Batch ID = 19050, loss = 0.0957877, acc = 0.9\n",
      "[Train] Batch ID = 19060, loss = 0.0384796, acc = 1.0\n",
      "[Validation] Batch ID = 19060, loss = 0.0456693, acc = 1.0\n",
      "[Train] Batch ID = 19070, loss = 0.0245643, acc = 1.0\n",
      "[Validation] Batch ID = 19070, loss = 0.0456392, acc = 1.0\n",
      "[Train] Batch ID = 19080, loss = 0.0205276, acc = 1.0\n",
      "[Validation] Batch ID = 19080, loss = 0.0778906, acc = 1.0\n",
      "[Train] Batch ID = 19090, loss = 0.0359699, acc = 1.0\n",
      "[Validation] Batch ID = 19090, loss = 0.0406276, acc = 1.0\n",
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      "[Train] Batch ID = 19110, loss = 0.0259432, acc = 1.0\n",
      "[Validation] Batch ID = 19110, loss = 0.046648, acc = 1.0\n",
      "[Train] Batch ID = 19120, loss = 0.0237435, acc = 1.0\n",
      "[Validation] Batch ID = 19120, loss = 0.06367, acc = 0.95\n",
      "[Train] Batch ID = 19130, loss = 0.0412505, acc = 1.0\n",
      "[Validation] Batch ID = 19130, loss = 0.0353038, acc = 1.0\n",
      "[Train] Batch ID = 19140, loss = 0.0464893, acc = 1.0\n",
      "[Validation] Batch ID = 19140, loss = 0.0427196, acc = 1.0\n",
      "[Train] Batch ID = 19150, loss = 0.0149743, acc = 1.0\n",
      "[Validation] Batch ID = 19150, loss = 0.0774428, acc = 0.95\n",
      "[Train] Batch ID = 19160, loss = 0.0198449, acc = 1.0\n",
      "[Validation] Batch ID = 19160, loss = 0.0661803, acc = 0.9\n",
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      "[Validation] Batch ID = 19170, loss = 0.0953038, acc = 0.95\n",
      "[Train] Batch ID = 19180, loss = 0.0282671, acc = 1.0\n",
      "[Validation] Batch ID = 19180, loss = 0.0511551, acc = 0.95\n",
      "[Train] Batch ID = 19190, loss = 0.03496, acc = 1.0\n",
      "[Validation] Batch ID = 19190, loss = 0.157717, acc = 0.85\n",
      "[Train] Batch ID = 19200, loss = 0.027865, acc = 1.0\n",
      "[Validation] Batch ID = 19200, loss = 0.0899012, acc = 0.85\n",
      "[Train] Batch ID = 19210, loss = 0.028662, acc = 1.0\n",
      "[Validation] Batch ID = 19210, loss = 0.0664787, acc = 1.0\n",
      "[Train] Batch ID = 19220, loss = 0.0338225, acc = 1.0\n",
      "[Validation] Batch ID = 19220, loss = 0.0705077, acc = 0.95\n",
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      "[Train] Batch ID = 19240, loss = 0.0171188, acc = 1.0\n",
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      "[Validation] Batch ID = 19250, loss = 0.0792543, acc = 0.95\n",
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      "[Validation] Batch ID = 19300, loss = 0.0644514, acc = 0.95\n",
      "[Train] Batch ID = 19310, loss = 0.0247818, acc = 1.0\n",
      "[Validation] Batch ID = 19310, loss = 0.0432641, acc = 1.0\n",
      "[Train] Batch ID = 19320, loss = 0.0262991, acc = 1.0\n",
      "[Validation] Batch ID = 19320, loss = 0.0287341, acc = 1.0\n",
      "[Train] Batch ID = 19330, loss = 0.0126364, acc = 1.0\n",
      "[Validation] Batch ID = 19330, loss = 0.112231, acc = 0.85\n",
      "[Train] Batch ID = 19340, loss = 0.021055, acc = 1.0\n",
      "[Validation] Batch ID = 19340, loss = 0.0362112, acc = 1.0\n",
      "[Train] Batch ID = 19350, loss = 0.315932, acc = 0.65\n",
      "[Validation] Batch ID = 19350, loss = 0.0363074, acc = 1.0\n",
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      "[Validation] Batch ID = 19390, loss = 0.0662568, acc = 0.95\n",
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      "[Validation] Batch ID = 19420, loss = 0.0359285, acc = 1.0\n",
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      "[Validation] Batch ID = 19440, loss = 0.0456017, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 19450, loss = 0.0110858, acc = 1.0\n",
      "[Validation] Batch ID = 19450, loss = 0.0437946, acc = 1.0\n",
      "[Train] Batch ID = 19460, loss = 0.0257605, acc = 1.0\n",
      "[Validation] Batch ID = 19460, loss = 0.0481316, acc = 1.0\n",
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      "[Validation] Batch ID = 19470, loss = 0.0427366, acc = 0.95\n",
      "[Train] Batch ID = 19480, loss = 0.307393, acc = 0.65\n",
      "[Validation] Batch ID = 19480, loss = 0.056776, acc = 1.0\n",
      "[Train] Batch ID = 19490, loss = 0.0374341, acc = 1.0\n",
      "[Validation] Batch ID = 19490, loss = 0.0566837, acc = 1.0\n",
      "[Train] Batch ID = 19500, loss = 0.444774, acc = 0.25\n",
      "[Validation] Batch ID = 19500, loss = 0.0417969, acc = 1.0\n",
      "[Train] Batch ID = 19510, loss = 0.414127, acc = 0.5\n",
      "[Validation] Batch ID = 19510, loss = 0.107848, acc = 0.95\n",
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      "[Validation] Batch ID = 19520, loss = 0.0588328, acc = 1.0\n",
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      "[Validation] Batch ID = 19530, loss = 0.0425116, acc = 1.0\n",
      "[Train] Batch ID = 19540, loss = 0.0416455, acc = 1.0\n",
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      "[Validation] Batch ID = 19560, loss = 0.0874152, acc = 0.9\n",
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      "[Validation] Batch ID = 19570, loss = 0.029335, acc = 1.0\n",
      "[Train] Batch ID = 19580, loss = 0.0225536, acc = 1.0\n",
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      "[Validation] Batch ID = 19590, loss = 0.0715056, acc = 0.95\n",
      "[Train] Batch ID = 19600, loss = 0.0387456, acc = 1.0\n",
      "[Validation] Batch ID = 19600, loss = 0.109164, acc = 0.9\n",
      "[Train] Batch ID = 19610, loss = 0.0462732, acc = 0.95\n",
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      "[Validation] Batch ID = 19650, loss = 0.147452, acc = 0.9\n",
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      "[Validation] Batch ID = 19660, loss = 0.050895, acc = 1.0\n",
      "[Train] Batch ID = 19670, loss = 0.0344242, acc = 1.0\n",
      "[Validation] Batch ID = 19670, loss = 0.036364, acc = 1.0\n",
      "[Train] Batch ID = 19680, loss = 0.0124874, acc = 1.0\n",
      "[Validation] Batch ID = 19680, loss = 0.0900084, acc = 0.95\n",
      "[Train] Batch ID = 19690, loss = 0.34223, acc = 0.5\n",
      "[Validation] Batch ID = 19690, loss = 0.049863, acc = 0.95\n",
      "[Train] Batch ID = 19700, loss = 0.0409645, acc = 0.95\n",
      "[Validation] Batch ID = 19700, loss = 0.054104, acc = 1.0\n",
      "[Train] Batch ID = 19710, loss = 0.287474, acc = 0.6\n",
      "[Validation] Batch ID = 19710, loss = 0.0169899, acc = 1.0\n",
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      "[Validation] Batch ID = 19720, loss = 0.0641918, acc = 0.95\n",
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      "[Validation] Batch ID = 19730, loss = 0.11118, acc = 0.9\n",
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      "[Validation] Batch ID = 19740, loss = 0.0721625, acc = 1.0\n",
      "[Train] Batch ID = 19750, loss = 0.015586, acc = 1.0\n",
      "[Validation] Batch ID = 19750, loss = 0.0528666, acc = 1.0\n",
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      "[Validation] Batch ID = 19780, loss = 0.0372233, acc = 1.0\n",
      "[Train] Batch ID = 19790, loss = 0.356004, acc = 0.6\n",
      "[Validation] Batch ID = 19790, loss = 0.0530107, acc = 1.0\n",
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      "[Validation] Batch ID = 19800, loss = 0.0419022, acc = 1.0\n",
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      "[Validation] Batch ID = 19840, loss = 0.119504, acc = 0.9\n",
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      "[Validation] Batch ID = 19860, loss = 0.0756691, acc = 0.95\n",
      "[Train] Batch ID = 19870, loss = 0.0323807, acc = 1.0\n",
      "[Validation] Batch ID = 19870, loss = 0.0843912, acc = 0.95\n",
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      "[Validation] Batch ID = 19880, loss = 0.0360775, acc = 1.0\n",
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      "[Validation] Batch ID = 19890, loss = 0.0476574, acc = 0.95\n",
      "[Train] Batch ID = 19900, loss = 0.0246869, acc = 1.0\n",
      "[Validation] Batch ID = 19900, loss = 0.0481125, acc = 1.0\n",
      "[Train] Batch ID = 19910, loss = 0.0232277, acc = 1.0\n",
      "[Validation] Batch ID = 19910, loss = 0.0396936, acc = 1.0\n",
      "[Train] Batch ID = 19920, loss = 0.0277433, acc = 1.0\n",
      "[Validation] Batch ID = 19920, loss = 0.0507294, acc = 1.0\n",
      "[Train] Batch ID = 19930, loss = 0.025735, acc = 1.0\n",
      "[Validation] Batch ID = 19930, loss = 0.112215, acc = 0.9\n",
      "[Train] Batch ID = 19940, loss = 0.0242246, acc = 1.0\n",
      "[Validation] Batch ID = 19940, loss = 0.0359433, acc = 1.0\n",
      "[Train] Batch ID = 19950, loss = 0.0249987, acc = 1.0\n",
      "[Validation] Batch ID = 19950, loss = 0.0585637, acc = 1.0\n",
      "[Train] Batch ID = 19960, loss = 0.026218, acc = 1.0\n",
      "[Validation] Batch ID = 19960, loss = 0.0339989, acc = 1.0\n",
      "[Train] Batch ID = 19970, loss = 0.00984799, acc = 1.0\n",
      "[Validation] Batch ID = 19970, loss = 0.115759, acc = 0.9\n",
      "[Train] Batch ID = 19980, loss = 0.0239194, acc = 1.0\n",
      "[Validation] Batch ID = 19980, loss = 0.07325, acc = 1.0\n",
      "[Train] Batch ID = 19990, loss = 0.0176902, acc = 1.0\n",
      "[Validation] Batch ID = 19990, loss = 0.0445095, acc = 1.0\n",
      "[Train] Batch ID = 20000, loss = 0.0273727, acc = 1.0\n",
      "[Validation] Batch ID = 20000, loss = 0.0914863, acc = 0.95\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0622949 Best loss: 0.0658299\n",
      "[TOTAL Validation] Batch ID = 20000, loss = 0.0622949, acc = 0.963492063492\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.10832479924019728\n",
      "[Train] Batch ID = 20010, loss = 0.0453177, acc = 1.0\n",
      "[Validation] Batch ID = 20010, loss = 0.0591397, acc = 0.95\n",
      "[Train] Batch ID = 20020, loss = 0.0307904, acc = 1.0\n",
      "[Validation] Batch ID = 20020, loss = 0.0662045, acc = 0.9\n",
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      "[Validation] Batch ID = 20030, loss = 0.0435713, acc = 1.0\n",
      "[Train] Batch ID = 20040, loss = 0.0223292, acc = 1.0\n",
      "[Validation] Batch ID = 20040, loss = 0.032996, acc = 1.0\n",
      "[Train] Batch ID = 20050, loss = 0.0195444, acc = 1.0\n",
      "[Validation] Batch ID = 20050, loss = 0.0342348, acc = 1.0\n",
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      "[Validation] Batch ID = 20060, loss = 0.0749149, acc = 0.9\n",
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      "[Validation] Batch ID = 20070, loss = 0.0425389, acc = 1.0\n",
      "[Train] Batch ID = 20080, loss = 0.344922, acc = 0.55\n",
      "[Validation] Batch ID = 20080, loss = 0.0248932, acc = 1.0\n",
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      "[Validation] Batch ID = 20090, loss = 0.0928941, acc = 0.95\n",
      "[Train] Batch ID = 20100, loss = 0.0394395, acc = 1.0\n",
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      "[Train] Batch ID = 20130, loss = 0.0295296, acc = 1.0\n",
      "[Validation] Batch ID = 20130, loss = 0.127907, acc = 0.85\n",
      "[Train] Batch ID = 20140, loss = 0.404643, acc = 0.5\n",
      "[Validation] Batch ID = 20140, loss = 0.0349011, acc = 1.0\n",
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      "[Validation] Batch ID = 20730, loss = 0.107072, acc = 0.9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 20740, loss = 0.0321098, acc = 1.0\n",
      "[Validation] Batch ID = 20740, loss = 0.0614613, acc = 1.0\n",
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      "[Train] Batch ID = 20900, loss = 0.0366183, acc = 0.95\n",
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      "[Validation] Batch ID = 20910, loss = 0.10096, acc = 0.9\n",
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      "[Validation] Batch ID = 20930, loss = 0.0531952, acc = 0.95\n",
      "[Train] Batch ID = 20940, loss = 0.0265412, acc = 1.0\n",
      "[Validation] Batch ID = 20940, loss = 0.052583, acc = 0.95\n",
      "[Train] Batch ID = 20950, loss = 0.0222897, acc = 1.0\n",
      "[Validation] Batch ID = 20950, loss = 0.0179667, acc = 1.0\n",
      "[Train] Batch ID = 20960, loss = 0.0135677, acc = 1.0\n",
      "[Validation] Batch ID = 20960, loss = 0.0682399, acc = 0.95\n",
      "[Train] Batch ID = 20970, loss = 0.0218806, acc = 1.0\n",
      "[Validation] Batch ID = 20970, loss = 0.036647, acc = 1.0\n",
      "[Train] Batch ID = 20980, loss = 0.00934182, acc = 1.0\n",
      "[Validation] Batch ID = 20980, loss = 0.0540493, acc = 0.95\n",
      "[Train] Batch ID = 20990, loss = 0.0169467, acc = 1.0\n",
      "[Validation] Batch ID = 20990, loss = 0.0253798, acc = 1.0\n",
      "[Train] Batch ID = 21000, loss = 0.021881, acc = 1.0\n",
      "[Validation] Batch ID = 21000, loss = 0.049757, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0595609 Best loss: 0.0622949\n",
      "[TOTAL Validation] Batch ID = 21000, loss = 0.0595609, acc = 0.96462585034\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.09749231931617755\n",
      "[Train] Batch ID = 21010, loss = 0.0322005, acc = 1.0\n",
      "[Validation] Batch ID = 21010, loss = 0.107089, acc = 0.9\n",
      "[Train] Batch ID = 21020, loss = 0.0120354, acc = 1.0\n",
      "[Validation] Batch ID = 21020, loss = 0.0647376, acc = 0.95\n",
      "[Train] Batch ID = 21030, loss = 0.0187768, acc = 1.0\n",
      "[Validation] Batch ID = 21030, loss = 0.0581623, acc = 0.95\n",
      "[Train] Batch ID = 21040, loss = 0.0116168, acc = 1.0\n",
      "[Validation] Batch ID = 21040, loss = 0.0890934, acc = 0.95\n",
      "[Train] Batch ID = 21050, loss = 0.0127792, acc = 1.0\n",
      "[Validation] Batch ID = 21050, loss = 0.0650195, acc = 0.95\n",
      "[Train] Batch ID = 21060, loss = 0.0154782, acc = 1.0\n",
      "[Validation] Batch ID = 21060, loss = 0.0264488, acc = 1.0\n",
      "[Train] Batch ID = 21070, loss = 0.0298988, acc = 1.0\n",
      "[Validation] Batch ID = 21070, loss = 0.0839817, acc = 0.95\n",
      "[Train] Batch ID = 21080, loss = 0.0148923, acc = 1.0\n",
      "[Validation] Batch ID = 21080, loss = 0.0757209, acc = 0.95\n",
      "[Train] Batch ID = 21090, loss = 0.0189812, acc = 1.0\n",
      "[Validation] Batch ID = 21090, loss = 0.0393295, acc = 1.0\n",
      "[Train] Batch ID = 21100, loss = 0.0316225, acc = 1.0\n",
      "[Validation] Batch ID = 21100, loss = 0.0328439, acc = 1.0\n",
      "[Train] Batch ID = 21110, loss = 0.0162518, acc = 1.0\n",
      "[Validation] Batch ID = 21110, loss = 0.0467599, acc = 1.0\n",
      "[Train] Batch ID = 21120, loss = 0.0184658, acc = 1.0\n",
      "[Validation] Batch ID = 21120, loss = 0.0796865, acc = 0.9\n",
      "[Train] Batch ID = 21130, loss = 0.019077, acc = 1.0\n",
      "[Validation] Batch ID = 21130, loss = 0.0892279, acc = 0.95\n",
      "[Train] Batch ID = 21140, loss = 0.0112953, acc = 1.0\n",
      "[Validation] Batch ID = 21140, loss = 0.0557212, acc = 0.95\n",
      "[Train] Batch ID = 21150, loss = 0.298954, acc = 0.7\n",
      "[Validation] Batch ID = 21150, loss = 0.0276189, acc = 1.0\n",
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      "[Validation] Batch ID = 21170, loss = 0.0736318, acc = 0.9\n",
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      "[Train] Batch ID = 21190, loss = 0.0128407, acc = 1.0\n",
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      "[Validation] Batch ID = 21230, loss = 0.0702206, acc = 1.0\n",
      "[Train] Batch ID = 21240, loss = 0.0103298, acc = 1.0\n",
      "[Validation] Batch ID = 21240, loss = 0.0281984, acc = 1.0\n",
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      "[Validation] Batch ID = 21250, loss = 0.0584561, acc = 0.95\n",
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      "[Validation] Batch ID = 21260, loss = 0.0883015, acc = 0.9\n",
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      "[Validation] Batch ID = 21300, loss = 0.0441692, acc = 1.0\n",
      "[Train] Batch ID = 21310, loss = 0.390293, acc = 0.5\n",
      "[Validation] Batch ID = 21310, loss = 0.0494591, acc = 0.95\n",
      "[Train] Batch ID = 21320, loss = 0.0190995, acc = 1.0\n",
      "[Validation] Batch ID = 21320, loss = 0.0886653, acc = 0.95\n",
      "[Train] Batch ID = 21330, loss = 0.0417007, acc = 1.0\n",
      "[Validation] Batch ID = 21330, loss = 0.0455928, acc = 1.0\n",
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      "[Validation] Batch ID = 21730, loss = 0.0699265, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 21740, loss = 0.0131933, acc = 1.0\n",
      "[Validation] Batch ID = 21740, loss = 0.0606853, acc = 1.0\n",
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      "[Validation] Batch ID = 21900, loss = 0.0719771, acc = 0.95\n",
      "[Train] Batch ID = 21910, loss = 0.0141419, acc = 1.0\n",
      "[Validation] Batch ID = 21910, loss = 0.0598766, acc = 0.95\n",
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      "[Validation] Batch ID = 21930, loss = 0.0854894, acc = 0.9\n",
      "[Train] Batch ID = 21940, loss = 0.0251606, acc = 1.0\n",
      "[Validation] Batch ID = 21940, loss = 0.0556984, acc = 0.95\n",
      "[Train] Batch ID = 21950, loss = 0.0190271, acc = 1.0\n",
      "[Validation] Batch ID = 21950, loss = 0.082108, acc = 0.85\n",
      "[Train] Batch ID = 21960, loss = 0.0137965, acc = 1.0\n",
      "[Validation] Batch ID = 21960, loss = 0.0656228, acc = 0.95\n",
      "[Train] Batch ID = 21970, loss = 0.324551, acc = 0.6\n",
      "[Validation] Batch ID = 21970, loss = 0.107772, acc = 0.9\n",
      "[Train] Batch ID = 21980, loss = 0.0110983, acc = 1.0\n",
      "[Validation] Batch ID = 21980, loss = 0.0393796, acc = 0.95\n",
      "[Train] Batch ID = 21990, loss = 0.0165334, acc = 1.0\n",
      "[Validation] Batch ID = 21990, loss = 0.029917, acc = 1.0\n",
      "[Train] Batch ID = 22000, loss = 0.00853649, acc = 1.0\n",
      "[Validation] Batch ID = 22000, loss = 0.0515002, acc = 0.95\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0577136 Best loss: 0.0595609\n",
      "[TOTAL Validation] Batch ID = 22000, loss = 0.0577136, acc = 0.963718820862\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.0877430873845598\n",
      "[Train] Batch ID = 22010, loss = 0.0206322, acc = 1.0\n",
      "[Validation] Batch ID = 22010, loss = 0.0831448, acc = 0.95\n",
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      "[Validation] Batch ID = 22670, loss = 0.0790024, acc = 0.9\n",
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      "[Validation] Batch ID = 22680, loss = 0.0628594, acc = 0.95\n",
      "[Train] Batch ID = 22690, loss = 0.00996247, acc = 1.0\n",
      "[Validation] Batch ID = 22690, loss = 0.0397817, acc = 1.0\n",
      "[Train] Batch ID = 22700, loss = 0.35126, acc = 0.55\n",
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      "[Validation] Batch ID = 22730, loss = 0.0528512, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 22740, loss = 0.0253119, acc = 1.0\n",
      "[Validation] Batch ID = 22740, loss = 0.10836, acc = 0.85\n",
      "[Train] Batch ID = 22750, loss = 0.00877721, acc = 1.0\n",
      "[Validation] Batch ID = 22750, loss = 0.0618588, acc = 0.95\n",
      "[Train] Batch ID = 22760, loss = 0.0200403, acc = 1.0\n",
      "[Validation] Batch ID = 22760, loss = 0.0685434, acc = 0.95\n",
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      "[Validation] Batch ID = 22770, loss = 0.0500775, acc = 1.0\n",
      "[Train] Batch ID = 22780, loss = 0.0251595, acc = 1.0\n",
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      "[Train] Batch ID = 22810, loss = 0.0266075, acc = 1.0\n",
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      "[Validation] Batch ID = 22820, loss = 0.0331827, acc = 1.0\n",
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      "[Train] Batch ID = 22840, loss = 0.0160013, acc = 1.0\n",
      "[Validation] Batch ID = 22840, loss = 0.0237414, acc = 1.0\n",
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      "[Validation] Batch ID = 22860, loss = 0.0205945, acc = 1.0\n",
      "[Train] Batch ID = 22870, loss = 0.0295897, acc = 1.0\n",
      "[Validation] Batch ID = 22870, loss = 0.0410202, acc = 1.0\n",
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      "[Validation] Batch ID = 22880, loss = 0.10891, acc = 0.9\n",
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      "[Validation] Batch ID = 22890, loss = 0.04169, acc = 1.0\n",
      "[Train] Batch ID = 22900, loss = 0.0159052, acc = 1.0\n",
      "[Validation] Batch ID = 22900, loss = 0.0945263, acc = 0.95\n",
      "[Train] Batch ID = 22910, loss = 0.0326405, acc = 1.0\n",
      "[Validation] Batch ID = 22910, loss = 0.0284977, acc = 1.0\n",
      "[Train] Batch ID = 22920, loss = 0.37721, acc = 0.6\n",
      "[Validation] Batch ID = 22920, loss = 0.105764, acc = 0.95\n",
      "[Train] Batch ID = 22930, loss = 0.017868, acc = 1.0\n",
      "[Validation] Batch ID = 22930, loss = 0.0233183, acc = 1.0\n",
      "[Train] Batch ID = 22940, loss = 0.0109963, acc = 1.0\n",
      "[Validation] Batch ID = 22940, loss = 0.0760282, acc = 1.0\n",
      "[Train] Batch ID = 22950, loss = 0.0201041, acc = 1.0\n",
      "[Validation] Batch ID = 22950, loss = 0.0638433, acc = 0.85\n",
      "[Train] Batch ID = 22960, loss = 0.0198837, acc = 1.0\n",
      "[Validation] Batch ID = 22960, loss = 0.0328119, acc = 1.0\n",
      "[Train] Batch ID = 22970, loss = 0.0127952, acc = 1.0\n",
      "[Validation] Batch ID = 22970, loss = 0.0388825, acc = 1.0\n",
      "[Train] Batch ID = 22980, loss = 0.0356518, acc = 1.0\n",
      "[Validation] Batch ID = 22980, loss = 0.0472992, acc = 0.95\n",
      "[Train] Batch ID = 22990, loss = 0.026015, acc = 1.0\n",
      "[Validation] Batch ID = 22990, loss = 0.022669, acc = 1.0\n",
      "[Train] Batch ID = 23000, loss = 0.0229927, acc = 1.0\n",
      "[Validation] Batch ID = 23000, loss = 0.0549135, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0530457 Best loss: 0.0577136\n",
      "[TOTAL Validation] Batch ID = 23000, loss = 0.0530457, acc = 0.967800453515\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.07896877864610383\n",
      "[Train] Batch ID = 23010, loss = 0.0079619, acc = 1.0\n",
      "[Validation] Batch ID = 23010, loss = 0.0208555, acc = 1.0\n",
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      "[Validation] Batch ID = 23020, loss = 0.0243407, acc = 1.0\n",
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      "[Validation] Batch ID = 23030, loss = 0.161022, acc = 0.8\n",
      "[Train] Batch ID = 23040, loss = 0.0221568, acc = 1.0\n",
      "[Validation] Batch ID = 23040, loss = 0.0599317, acc = 0.95\n",
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      "[Validation] Batch ID = 23110, loss = 0.0666643, acc = 0.95\n",
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      "[Validation] Batch ID = 23730, loss = 0.0462442, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 23740, loss = 0.00943203, acc = 1.0\n",
      "[Validation] Batch ID = 23740, loss = 0.0510262, acc = 1.0\n",
      "[Train] Batch ID = 23750, loss = 0.0231611, acc = 1.0\n",
      "[Validation] Batch ID = 23750, loss = 0.0355011, acc = 1.0\n",
      "[Train] Batch ID = 23760, loss = 0.0227167, acc = 1.0\n",
      "[Validation] Batch ID = 23760, loss = 0.0959588, acc = 0.9\n",
      "[Train] Batch ID = 23770, loss = 0.0201045, acc = 1.0\n",
      "[Validation] Batch ID = 23770, loss = 0.0957603, acc = 0.9\n",
      "[Train] Batch ID = 23780, loss = 0.0109169, acc = 1.0\n",
      "[Validation] Batch ID = 23780, loss = 0.0924214, acc = 0.9\n",
      "[Train] Batch ID = 23790, loss = 0.0121704, acc = 1.0\n",
      "[Validation] Batch ID = 23790, loss = 0.0333739, acc = 1.0\n",
      "[Train] Batch ID = 23800, loss = 0.0153672, acc = 1.0\n",
      "[Validation] Batch ID = 23800, loss = 0.0294489, acc = 1.0\n",
      "[Train] Batch ID = 23810, loss = 0.0245732, acc = 1.0\n",
      "[Validation] Batch ID = 23810, loss = 0.0374172, acc = 1.0\n",
      "[Train] Batch ID = 23820, loss = 0.0336263, acc = 0.95\n",
      "[Validation] Batch ID = 23820, loss = 0.0295304, acc = 1.0\n",
      "[Train] Batch ID = 23830, loss = 0.0261085, acc = 1.0\n",
      "[Validation] Batch ID = 23830, loss = 0.0299675, acc = 1.0\n",
      "[Train] Batch ID = 23840, loss = 0.0088108, acc = 1.0\n",
      "[Validation] Batch ID = 23840, loss = 0.0678781, acc = 0.95\n",
      "[Train] Batch ID = 23850, loss = 0.023997, acc = 1.0\n",
      "[Validation] Batch ID = 23850, loss = 0.0699668, acc = 0.95\n",
      "[Train] Batch ID = 23860, loss = 0.0177779, acc = 1.0\n",
      "[Validation] Batch ID = 23860, loss = 0.0200188, acc = 1.0\n",
      "[Train] Batch ID = 23870, loss = 0.0222881, acc = 1.0\n",
      "[Validation] Batch ID = 23870, loss = 0.0969685, acc = 0.9\n",
      "[Train] Batch ID = 23880, loss = 0.0107572, acc = 1.0\n",
      "[Validation] Batch ID = 23880, loss = 0.0956522, acc = 0.9\n",
      "[Train] Batch ID = 23890, loss = 0.0125229, acc = 1.0\n",
      "[Validation] Batch ID = 23890, loss = 0.0580667, acc = 0.95\n",
      "[Train] Batch ID = 23900, loss = 0.0161164, acc = 1.0\n",
      "[Validation] Batch ID = 23900, loss = 0.0304014, acc = 1.0\n",
      "[Train] Batch ID = 23910, loss = 0.0242683, acc = 1.0\n",
      "[Validation] Batch ID = 23910, loss = 0.0278243, acc = 1.0\n",
      "[Train] Batch ID = 23920, loss = 0.0109238, acc = 1.0\n",
      "[Validation] Batch ID = 23920, loss = 0.0443823, acc = 1.0\n",
      "[Train] Batch ID = 23930, loss = 0.018276, acc = 1.0\n",
      "[Validation] Batch ID = 23930, loss = 0.0499303, acc = 0.95\n",
      "[Train] Batch ID = 23940, loss = 0.00945921, acc = 1.0\n",
      "[Validation] Batch ID = 23940, loss = 0.0197832, acc = 1.0\n",
      "[Train] Batch ID = 23950, loss = 0.018079, acc = 1.0\n",
      "[Validation] Batch ID = 23950, loss = 0.0683156, acc = 0.9\n",
      "[Train] Batch ID = 23960, loss = 0.0147513, acc = 1.0\n",
      "[Validation] Batch ID = 23960, loss = 0.0678004, acc = 1.0\n",
      "[Train] Batch ID = 23970, loss = 0.00718255, acc = 1.0\n",
      "[Validation] Batch ID = 23970, loss = 0.0763395, acc = 0.9\n",
      "[Train] Batch ID = 23980, loss = 0.0141237, acc = 1.0\n",
      "[Validation] Batch ID = 23980, loss = 0.13691, acc = 0.8\n",
      "[Train] Batch ID = 23990, loss = 0.015606, acc = 1.0\n",
      "[Validation] Batch ID = 23990, loss = 0.0647296, acc = 1.0\n",
      "[Train] Batch ID = 24000, loss = 0.00729386, acc = 1.0\n",
      "[Validation] Batch ID = 24000, loss = 0.0768584, acc = 0.95\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0536194 Best loss: 0.0530457\n",
      "[TOTAL Validation] Batch ID = 24000, loss = 0.0536194, acc = 0.970975056689\n",
      "Augmented Factor = 0.07107190078149345\n",
      "[Train] Batch ID = 24010, loss = 0.0234999, acc = 1.0\n",
      "[Validation] Batch ID = 24010, loss = 0.0462218, acc = 0.95\n",
      "[Train] Batch ID = 24020, loss = 0.00918167, acc = 1.0\n",
      "[Validation] Batch ID = 24020, loss = 0.0255557, acc = 1.0\n",
      "[Train] Batch ID = 24030, loss = 0.0116025, acc = 1.0\n",
      "[Validation] Batch ID = 24030, loss = 0.0208021, acc = 1.0\n",
      "[Train] Batch ID = 24040, loss = 0.0168811, acc = 1.0\n",
      "[Validation] Batch ID = 24040, loss = 0.0334475, acc = 1.0\n",
      "[Train] Batch ID = 24050, loss = 0.0053407, acc = 1.0\n",
      "[Validation] Batch ID = 24050, loss = 0.0837405, acc = 0.95\n",
      "[Train] Batch ID = 24060, loss = 0.0120874, acc = 1.0\n",
      "[Validation] Batch ID = 24060, loss = 0.0233794, acc = 1.0\n",
      "[Train] Batch ID = 24070, loss = 0.0123034, acc = 1.0\n",
      "[Validation] Batch ID = 24070, loss = 0.0280794, acc = 1.0\n",
      "[Train] Batch ID = 24080, loss = 0.0107446, acc = 1.0\n",
      "[Validation] Batch ID = 24080, loss = 0.0207445, acc = 1.0\n",
      "[Train] Batch ID = 24090, loss = 0.00348867, acc = 1.0\n",
      "[Validation] Batch ID = 24090, loss = 0.0500868, acc = 1.0\n",
      "[Train] Batch ID = 24100, loss = 0.0276266, acc = 1.0\n",
      "[Validation] Batch ID = 24100, loss = 0.036954, acc = 0.95\n",
      "[Train] Batch ID = 24110, loss = 0.0111328, acc = 1.0\n",
      "[Validation] Batch ID = 24110, loss = 0.0455262, acc = 1.0\n",
      "[Train] Batch ID = 24120, loss = 0.0176224, acc = 1.0\n",
      "[Validation] Batch ID = 24120, loss = 0.0346202, acc = 1.0\n",
      "[Train] Batch ID = 24130, loss = 0.0107963, acc = 1.0\n",
      "[Validation] Batch ID = 24130, loss = 0.0305999, acc = 1.0\n",
      "[Train] Batch ID = 24140, loss = 0.0176149, acc = 1.0\n",
      "[Validation] Batch ID = 24140, loss = 0.0566865, acc = 1.0\n",
      "[Train] Batch ID = 24150, loss = 0.0137349, acc = 1.0\n",
      "[Validation] Batch ID = 24150, loss = 0.0499028, acc = 0.95\n",
      "[Train] Batch ID = 24160, loss = 0.0215197, acc = 1.0\n",
      "[Validation] Batch ID = 24160, loss = 0.0271372, acc = 1.0\n",
      "[Train] Batch ID = 24170, loss = 0.0101831, acc = 1.0\n",
      "[Validation] Batch ID = 24170, loss = 0.0164193, acc = 1.0\n",
      "[Train] Batch ID = 24180, loss = 0.0232947, acc = 1.0\n",
      "[Validation] Batch ID = 24180, loss = 0.0451, acc = 0.95\n",
      "[Train] Batch ID = 24190, loss = 0.0420171, acc = 1.0\n",
      "[Validation] Batch ID = 24190, loss = 0.0754196, acc = 0.95\n",
      "[Train] Batch ID = 24200, loss = 0.00953792, acc = 1.0\n",
      "[Validation] Batch ID = 24200, loss = 0.056834, acc = 0.95\n",
      "[Train] Batch ID = 24210, loss = 0.0195311, acc = 1.0\n",
      "[Validation] Batch ID = 24210, loss = 0.0192782, acc = 1.0\n",
      "[Train] Batch ID = 24220, loss = 0.0166431, acc = 1.0\n",
      "[Validation] Batch ID = 24220, loss = 0.0893828, acc = 0.9\n",
      "[Train] Batch ID = 24230, loss = 0.0148793, acc = 1.0\n",
      "[Validation] Batch ID = 24230, loss = 0.0488359, acc = 1.0\n",
      "[Train] Batch ID = 24240, loss = 0.0140424, acc = 1.0\n",
      "[Validation] Batch ID = 24240, loss = 0.0847293, acc = 0.95\n",
      "[Train] Batch ID = 24250, loss = 0.0180456, acc = 1.0\n",
      "[Validation] Batch ID = 24250, loss = 0.0486684, acc = 1.0\n",
      "[Train] Batch ID = 24260, loss = 0.016337, acc = 1.0\n",
      "[Validation] Batch ID = 24260, loss = 0.0248243, acc = 1.0\n",
      "[Train] Batch ID = 24270, loss = 0.0194539, acc = 1.0\n",
      "[Validation] Batch ID = 24270, loss = 0.0317983, acc = 1.0\n",
      "[Train] Batch ID = 24280, loss = 0.0115947, acc = 1.0\n",
      "[Validation] Batch ID = 24280, loss = 0.028903, acc = 0.95\n",
      "[Train] Batch ID = 24290, loss = 0.00376821, acc = 1.0\n",
      "[Validation] Batch ID = 24290, loss = 0.0197685, acc = 1.0\n",
      "[Train] Batch ID = 24300, loss = 0.0145901, acc = 1.0\n",
      "[Validation] Batch ID = 24300, loss = 0.0584202, acc = 1.0\n",
      "[Train] Batch ID = 24310, loss = 0.0419525, acc = 0.95\n",
      "[Validation] Batch ID = 24310, loss = 0.138848, acc = 0.8\n",
      "[Train] Batch ID = 24320, loss = 0.00986533, acc = 1.0\n",
      "[Validation] Batch ID = 24320, loss = 0.038162, acc = 1.0\n",
      "[Train] Batch ID = 24330, loss = 0.0115911, acc = 1.0\n",
      "[Validation] Batch ID = 24330, loss = 0.0318668, acc = 1.0\n",
      "[Train] Batch ID = 24340, loss = 0.010583, acc = 1.0\n",
      "[Validation] Batch ID = 24340, loss = 0.0438858, acc = 1.0\n",
      "[Train] Batch ID = 24350, loss = 0.0141271, acc = 1.0\n",
      "[Validation] Batch ID = 24350, loss = 0.0549098, acc = 0.95\n",
      "[Train] Batch ID = 24360, loss = 0.365588, acc = 0.5\n",
      "[Validation] Batch ID = 24360, loss = 0.0187354, acc = 1.0\n",
      "[Train] Batch ID = 24370, loss = 0.0168328, acc = 1.0\n",
      "[Validation] Batch ID = 24370, loss = 0.0704325, acc = 1.0\n",
      "[Train] Batch ID = 24380, loss = 0.313827, acc = 0.7\n",
      "[Validation] Batch ID = 24380, loss = 0.0777834, acc = 0.95\n",
      "[Train] Batch ID = 24390, loss = 0.0120923, acc = 1.0\n",
      "[Validation] Batch ID = 24390, loss = 0.0703709, acc = 1.0\n",
      "[Train] Batch ID = 24400, loss = 0.0256425, acc = 1.0\n",
      "[Validation] Batch ID = 24400, loss = 0.0780912, acc = 0.95\n",
      "[Train] Batch ID = 24410, loss = 0.0145547, acc = 1.0\n",
      "[Validation] Batch ID = 24410, loss = 0.0473129, acc = 0.95\n",
      "[Train] Batch ID = 24420, loss = 0.0139971, acc = 1.0\n",
      "[Validation] Batch ID = 24420, loss = 0.0609479, acc = 0.95\n",
      "[Train] Batch ID = 24430, loss = 0.00680685, acc = 1.0\n",
      "[Validation] Batch ID = 24430, loss = 0.0899946, acc = 0.9\n",
      "[Train] Batch ID = 24440, loss = 0.0048993, acc = 1.0\n",
      "[Validation] Batch ID = 24440, loss = 0.0537531, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 24450, loss = 0.00868384, acc = 1.0\n",
      "[Validation] Batch ID = 24450, loss = 0.0418357, acc = 1.0\n",
      "[Train] Batch ID = 24460, loss = 0.401142, acc = 0.5\n",
      "[Validation] Batch ID = 24460, loss = 0.0492618, acc = 1.0\n",
      "[Train] Batch ID = 24470, loss = 0.0208831, acc = 1.0\n",
      "[Validation] Batch ID = 24470, loss = 0.0205543, acc = 1.0\n",
      "[Train] Batch ID = 24480, loss = 0.00842277, acc = 1.0\n",
      "[Validation] Batch ID = 24480, loss = 0.0779202, acc = 0.95\n",
      "[Train] Batch ID = 24490, loss = 0.0102501, acc = 1.0\n",
      "[Validation] Batch ID = 24490, loss = 0.0391499, acc = 0.95\n",
      "[Train] Batch ID = 24500, loss = 0.379225, acc = 0.55\n",
      "[Validation] Batch ID = 24500, loss = 0.0574853, acc = 1.0\n",
      "[Train] Batch ID = 24510, loss = 0.014043, acc = 1.0\n",
      "[Validation] Batch ID = 24510, loss = 0.0609347, acc = 0.95\n",
      "[Train] Batch ID = 24520, loss = 0.0258396, acc = 1.0\n",
      "[Validation] Batch ID = 24520, loss = 0.0513903, acc = 1.0\n",
      "[Train] Batch ID = 24530, loss = 0.00518842, acc = 1.0\n",
      "[Validation] Batch ID = 24530, loss = 0.0242941, acc = 1.0\n",
      "[Train] Batch ID = 24540, loss = 0.0176699, acc = 1.0\n",
      "[Validation] Batch ID = 24540, loss = 0.0334201, acc = 1.0\n",
      "[Train] Batch ID = 24550, loss = 0.00977927, acc = 1.0\n",
      "[Validation] Batch ID = 24550, loss = 0.0818059, acc = 0.9\n",
      "[Train] Batch ID = 24560, loss = 0.0197356, acc = 1.0\n",
      "[Validation] Batch ID = 24560, loss = 0.0768142, acc = 0.95\n",
      "[Train] Batch ID = 24570, loss = 0.00974647, acc = 1.0\n",
      "[Validation] Batch ID = 24570, loss = 0.049799, acc = 0.95\n",
      "[Train] Batch ID = 24580, loss = 0.022039, acc = 1.0\n",
      "[Validation] Batch ID = 24580, loss = 0.0417936, acc = 1.0\n",
      "[Train] Batch ID = 24590, loss = 0.0259989, acc = 1.0\n",
      "[Validation] Batch ID = 24590, loss = 0.0606937, acc = 0.95\n",
      "[Train] Batch ID = 24600, loss = 0.0200986, acc = 1.0\n",
      "[Validation] Batch ID = 24600, loss = 0.0643108, acc = 0.95\n",
      "[Train] Batch ID = 24610, loss = 0.00864481, acc = 1.0\n",
      "[Validation] Batch ID = 24610, loss = 0.0168178, acc = 1.0\n",
      "[Train] Batch ID = 24620, loss = 0.0171327, acc = 1.0\n",
      "[Validation] Batch ID = 24620, loss = 0.0974074, acc = 0.85\n",
      "[Train] Batch ID = 24630, loss = 0.00518628, acc = 1.0\n",
      "[Validation] Batch ID = 24630, loss = 0.0575835, acc = 0.95\n",
      "[Train] Batch ID = 24640, loss = 0.0162646, acc = 1.0\n",
      "[Validation] Batch ID = 24640, loss = 0.0490195, acc = 0.95\n",
      "[Train] Batch ID = 24650, loss = 0.387369, acc = 0.5\n",
      "[Validation] Batch ID = 24650, loss = 0.076383, acc = 0.95\n",
      "[Train] Batch ID = 24660, loss = 0.48756, acc = 0.3\n",
      "[Validation] Batch ID = 24660, loss = 0.0285105, acc = 1.0\n",
      "[Train] Batch ID = 24670, loss = 0.0208987, acc = 1.0\n",
      "[Validation] Batch ID = 24670, loss = 0.0176923, acc = 1.0\n",
      "[Train] Batch ID = 24680, loss = 0.0248814, acc = 1.0\n",
      "[Validation] Batch ID = 24680, loss = 0.0249041, acc = 1.0\n",
      "[Train] Batch ID = 24690, loss = 0.0168648, acc = 1.0\n",
      "[Validation] Batch ID = 24690, loss = 0.0138945, acc = 1.0\n",
      "[Train] Batch ID = 24700, loss = 0.0220295, acc = 1.0\n",
      "[Validation] Batch ID = 24700, loss = 0.0291243, acc = 1.0\n",
      "[Train] Batch ID = 24710, loss = 0.00601087, acc = 1.0\n",
      "[Validation] Batch ID = 24710, loss = 0.0345346, acc = 1.0\n",
      "[Train] Batch ID = 24720, loss = 0.0225757, acc = 1.0\n",
      "[Validation] Batch ID = 24720, loss = 0.0649661, acc = 0.9\n",
      "[Train] Batch ID = 24730, loss = 0.017305, acc = 1.0\n",
      "[Validation] Batch ID = 24730, loss = 0.0670801, acc = 0.95\n",
      "[Train] Batch ID = 24740, loss = 0.0305345, acc = 1.0\n",
      "[Validation] Batch ID = 24740, loss = 0.0643437, acc = 0.95\n",
      "[Train] Batch ID = 24750, loss = 0.0184435, acc = 1.0\n",
      "[Validation] Batch ID = 24750, loss = 0.0487902, acc = 0.95\n",
      "[Train] Batch ID = 24760, loss = 0.0127528, acc = 1.0\n",
      "[Validation] Batch ID = 24760, loss = 0.02972, acc = 1.0\n",
      "[Train] Batch ID = 24770, loss = 0.00978202, acc = 1.0\n",
      "[Validation] Batch ID = 24770, loss = 0.0140432, acc = 1.0\n",
      "[Train] Batch ID = 24780, loss = 0.0113096, acc = 1.0\n",
      "[Validation] Batch ID = 24780, loss = 0.0619992, acc = 1.0\n",
      "[Train] Batch ID = 24790, loss = 0.0210944, acc = 1.0\n",
      "[Validation] Batch ID = 24790, loss = 0.0560661, acc = 1.0\n",
      "[Train] Batch ID = 24800, loss = 0.0155624, acc = 1.0\n",
      "[Validation] Batch ID = 24800, loss = 0.0334856, acc = 1.0\n",
      "[Train] Batch ID = 24810, loss = 0.0178894, acc = 1.0\n",
      "[Validation] Batch ID = 24810, loss = 0.0503911, acc = 1.0\n",
      "[Train] Batch ID = 24820, loss = 0.015349, acc = 1.0\n",
      "[Validation] Batch ID = 24820, loss = 0.0385726, acc = 1.0\n",
      "[Train] Batch ID = 24830, loss = 0.00847889, acc = 1.0\n",
      "[Validation] Batch ID = 24830, loss = 0.107804, acc = 0.9\n",
      "[Train] Batch ID = 24840, loss = 0.00602994, acc = 1.0\n",
      "[Validation] Batch ID = 24840, loss = 0.0401418, acc = 0.95\n",
      "[Train] Batch ID = 24850, loss = 0.0202432, acc = 1.0\n",
      "[Validation] Batch ID = 24850, loss = 0.0444058, acc = 0.95\n",
      "[Train] Batch ID = 24860, loss = 0.0105937, acc = 1.0\n",
      "[Validation] Batch ID = 24860, loss = 0.0715412, acc = 0.95\n",
      "[Train] Batch ID = 24870, loss = 0.0161423, acc = 1.0\n",
      "[Validation] Batch ID = 24870, loss = 0.0918176, acc = 0.95\n",
      "[Train] Batch ID = 24880, loss = 0.0074446, acc = 1.0\n",
      "[Validation] Batch ID = 24880, loss = 0.127481, acc = 0.8\n",
      "[Train] Batch ID = 24890, loss = 0.00829513, acc = 1.0\n",
      "[Validation] Batch ID = 24890, loss = 0.0625525, acc = 0.95\n",
      "[Train] Batch ID = 24900, loss = 0.0127116, acc = 1.0\n",
      "[Validation] Batch ID = 24900, loss = 0.0319691, acc = 1.0\n",
      "[Train] Batch ID = 24910, loss = 0.0213126, acc = 1.0\n",
      "[Validation] Batch ID = 24910, loss = 0.0352176, acc = 0.95\n",
      "[Train] Batch ID = 24920, loss = 0.0118546, acc = 1.0\n",
      "[Validation] Batch ID = 24920, loss = 0.0604267, acc = 0.95\n",
      "[Train] Batch ID = 24930, loss = 0.0243901, acc = 1.0\n",
      "[Validation] Batch ID = 24930, loss = 0.0917169, acc = 0.9\n",
      "[Train] Batch ID = 24940, loss = 0.0164605, acc = 1.0\n",
      "[Validation] Batch ID = 24940, loss = 0.0566263, acc = 1.0\n",
      "[Train] Batch ID = 24950, loss = 0.017797, acc = 1.0\n",
      "[Validation] Batch ID = 24950, loss = 0.122255, acc = 0.85\n",
      "[Train] Batch ID = 24960, loss = 0.00716556, acc = 1.0\n",
      "[Validation] Batch ID = 24960, loss = 0.0605375, acc = 0.95\n",
      "[Train] Batch ID = 24970, loss = 0.0158124, acc = 1.0\n",
      "[Validation] Batch ID = 24970, loss = 0.0343641, acc = 1.0\n",
      "[Train] Batch ID = 24980, loss = 0.01882, acc = 1.0\n",
      "[Validation] Batch ID = 24980, loss = 0.0310567, acc = 1.0\n",
      "[Train] Batch ID = 24990, loss = 0.0337737, acc = 0.95\n",
      "[Validation] Batch ID = 24990, loss = 0.0931339, acc = 0.95\n",
      "[Train] Batch ID = 25000, loss = 0.0143866, acc = 1.0\n",
      "[Validation] Batch ID = 25000, loss = 0.025429, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0497706 Best loss: 0.0530457\n",
      "[TOTAL Validation] Batch ID = 25000, loss = 0.0497706, acc = 0.971882086168\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.0639647107033441\n",
      "[Train] Batch ID = 25010, loss = 0.00695508, acc = 1.0\n",
      "[Validation] Batch ID = 25010, loss = 0.0575831, acc = 1.0\n",
      "[Train] Batch ID = 25020, loss = 0.0106731, acc = 1.0\n",
      "[Validation] Batch ID = 25020, loss = 0.0126594, acc = 1.0\n",
      "[Train] Batch ID = 25030, loss = 0.0169218, acc = 1.0\n",
      "[Validation] Batch ID = 25030, loss = 0.0221281, acc = 1.0\n",
      "[Train] Batch ID = 25040, loss = 0.00929503, acc = 1.0\n",
      "[Validation] Batch ID = 25040, loss = 0.020204, acc = 1.0\n",
      "[Train] Batch ID = 25050, loss = 0.012229, acc = 1.0\n",
      "[Validation] Batch ID = 25050, loss = 0.0383262, acc = 1.0\n",
      "[Train] Batch ID = 25060, loss = 0.00751185, acc = 1.0\n",
      "[Validation] Batch ID = 25060, loss = 0.0291004, acc = 1.0\n",
      "[Train] Batch ID = 25070, loss = 0.0107549, acc = 1.0\n",
      "[Validation] Batch ID = 25070, loss = 0.0394678, acc = 1.0\n",
      "[Train] Batch ID = 25080, loss = 0.0122478, acc = 1.0\n",
      "[Validation] Batch ID = 25080, loss = 0.0488478, acc = 1.0\n",
      "[Train] Batch ID = 25090, loss = 0.348434, acc = 0.65\n",
      "[Validation] Batch ID = 25090, loss = 0.0114544, acc = 1.0\n",
      "[Train] Batch ID = 25100, loss = 0.0215672, acc = 1.0\n",
      "[Validation] Batch ID = 25100, loss = 0.0696534, acc = 0.95\n",
      "[Train] Batch ID = 25110, loss = 0.0324582, acc = 1.0\n",
      "[Validation] Batch ID = 25110, loss = 0.0401368, acc = 1.0\n",
      "[Train] Batch ID = 25120, loss = 0.0184652, acc = 1.0\n",
      "[Validation] Batch ID = 25120, loss = 0.0236548, acc = 1.0\n",
      "[Train] Batch ID = 25130, loss = 0.00722134, acc = 1.0\n",
      "[Validation] Batch ID = 25130, loss = 0.0207214, acc = 1.0\n",
      "[Train] Batch ID = 25140, loss = 0.0177797, acc = 1.0\n",
      "[Validation] Batch ID = 25140, loss = 0.0270226, acc = 1.0\n",
      "[Train] Batch ID = 25150, loss = 0.0269538, acc = 1.0\n",
      "[Validation] Batch ID = 25150, loss = 0.0483018, acc = 1.0\n",
      "[Train] Batch ID = 25160, loss = 0.0089587, acc = 1.0\n",
      "[Validation] Batch ID = 25160, loss = 0.0564324, acc = 0.95\n",
      "[Train] Batch ID = 25170, loss = 0.0128584, acc = 1.0\n",
      "[Validation] Batch ID = 25170, loss = 0.0313593, acc = 1.0\n",
      "[Train] Batch ID = 25180, loss = 0.00743306, acc = 1.0\n",
      "[Validation] Batch ID = 25180, loss = 0.0459764, acc = 0.95\n",
      "[Train] Batch ID = 25190, loss = 0.0255289, acc = 1.0\n",
      "[Validation] Batch ID = 25190, loss = 0.0887083, acc = 0.95\n",
      "[Train] Batch ID = 25200, loss = 0.0108033, acc = 1.0\n",
      "[Validation] Batch ID = 25200, loss = 0.0964447, acc = 0.85\n",
      "[Train] Batch ID = 25210, loss = 0.0196701, acc = 1.0\n",
      "[Validation] Batch ID = 25210, loss = 0.0362774, acc = 1.0\n",
      "[Train] Batch ID = 25220, loss = 0.0140847, acc = 1.0\n",
      "[Validation] Batch ID = 25220, loss = 0.0287157, acc = 1.0\n",
      "[Train] Batch ID = 25230, loss = 0.0175841, acc = 1.0\n",
      "[Validation] Batch ID = 25230, loss = 0.0320773, acc = 1.0\n",
      "[Train] Batch ID = 25240, loss = 0.0082025, acc = 1.0\n",
      "[Validation] Batch ID = 25240, loss = 0.0504333, acc = 1.0\n",
      "[Train] Batch ID = 25250, loss = 0.0143581, acc = 1.0\n",
      "[Validation] Batch ID = 25250, loss = 0.0804555, acc = 0.95\n",
      "[Train] Batch ID = 25260, loss = 0.020164, acc = 1.0\n",
      "[Validation] Batch ID = 25260, loss = 0.117586, acc = 0.9\n",
      "[Train] Batch ID = 25270, loss = 0.0135111, acc = 1.0\n",
      "[Validation] Batch ID = 25270, loss = 0.0346784, acc = 1.0\n",
      "[Train] Batch ID = 25280, loss = 0.019605, acc = 1.0\n",
      "[Validation] Batch ID = 25280, loss = 0.0311654, acc = 1.0\n",
      "[Train] Batch ID = 25290, loss = 0.0179685, acc = 1.0\n",
      "[Validation] Batch ID = 25290, loss = 0.0434151, acc = 0.95\n",
      "[Train] Batch ID = 25300, loss = 0.0215843, acc = 1.0\n",
      "[Validation] Batch ID = 25300, loss = 0.0594859, acc = 0.95\n",
      "[Train] Batch ID = 25310, loss = 0.00909343, acc = 1.0\n",
      "[Validation] Batch ID = 25310, loss = 0.0910082, acc = 0.9\n",
      "[Train] Batch ID = 25320, loss = 0.337173, acc = 0.6\n",
      "[Validation] Batch ID = 25320, loss = 0.0385474, acc = 0.95\n",
      "[Train] Batch ID = 25330, loss = 0.00607291, acc = 1.0\n",
      "[Validation] Batch ID = 25330, loss = 0.0199414, acc = 1.0\n",
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      "[Validation] Batch ID = 25610, loss = 0.0286841, acc = 0.95\n",
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      "[Validation] Batch ID = 25640, loss = 0.0260022, acc = 1.0\n",
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      "[Validation] Batch ID = 25680, loss = 0.0355098, acc = 1.0\n",
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      "[Validation] Batch ID = 25700, loss = 0.0254578, acc = 1.0\n",
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      "[Validation] Batch ID = 25710, loss = 0.0225552, acc = 1.0\n",
      "[Train] Batch ID = 25720, loss = 0.355825, acc = 0.6\n",
      "[Validation] Batch ID = 25720, loss = 0.0331156, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 25730, loss = 0.0214869, acc = 1.0\n",
      "[Validation] Batch ID = 25730, loss = 0.0520201, acc = 1.0\n",
      "[Train] Batch ID = 25740, loss = 0.0066507, acc = 1.0\n",
      "[Validation] Batch ID = 25740, loss = 0.0322224, acc = 1.0\n",
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      "[Validation] Batch ID = 25750, loss = 0.0713646, acc = 0.95\n",
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      "[Validation] Batch ID = 25760, loss = 0.101965, acc = 0.9\n",
      "[Train] Batch ID = 25770, loss = 0.00992391, acc = 1.0\n",
      "[Validation] Batch ID = 25770, loss = 0.0518087, acc = 0.95\n",
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      "[Validation] Batch ID = 25780, loss = 0.0549769, acc = 0.95\n",
      "[Train] Batch ID = 25790, loss = 0.0079691, acc = 1.0\n",
      "[Validation] Batch ID = 25790, loss = 0.0552684, acc = 0.95\n",
      "[Train] Batch ID = 25800, loss = 0.0246463, acc = 1.0\n",
      "[Validation] Batch ID = 25800, loss = 0.0724031, acc = 0.95\n",
      "[Train] Batch ID = 25810, loss = 0.00904438, acc = 1.0\n",
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      "[Validation] Batch ID = 25830, loss = 0.0665405, acc = 0.95\n",
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      "[Validation] Batch ID = 25840, loss = 0.0629098, acc = 0.95\n",
      "[Train] Batch ID = 25850, loss = 0.00951536, acc = 1.0\n",
      "[Validation] Batch ID = 25850, loss = 0.0508719, acc = 0.95\n",
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      "[Validation] Batch ID = 25860, loss = 0.041014, acc = 1.0\n",
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      "[Validation] Batch ID = 25880, loss = 0.036889, acc = 0.95\n",
      "[Train] Batch ID = 25890, loss = 0.0168464, acc = 1.0\n",
      "[Validation] Batch ID = 25890, loss = 0.0470433, acc = 1.0\n",
      "[Train] Batch ID = 25900, loss = 0.0116736, acc = 1.0\n",
      "[Validation] Batch ID = 25900, loss = 0.0689247, acc = 0.95\n",
      "[Train] Batch ID = 25910, loss = 0.434894, acc = 0.4\n",
      "[Validation] Batch ID = 25910, loss = 0.0372032, acc = 1.0\n",
      "[Train] Batch ID = 25920, loss = 0.00977581, acc = 1.0\n",
      "[Validation] Batch ID = 25920, loss = 0.0709312, acc = 1.0\n",
      "[Train] Batch ID = 25930, loss = 0.0123399, acc = 1.0\n",
      "[Validation] Batch ID = 25930, loss = 0.0517763, acc = 0.95\n",
      "[Train] Batch ID = 25940, loss = 0.0171661, acc = 1.0\n",
      "[Validation] Batch ID = 25940, loss = 0.0639162, acc = 0.95\n",
      "[Train] Batch ID = 25950, loss = 0.00514866, acc = 1.0\n",
      "[Validation] Batch ID = 25950, loss = 0.0218761, acc = 1.0\n",
      "[Train] Batch ID = 25960, loss = 0.00842306, acc = 1.0\n",
      "[Validation] Batch ID = 25960, loss = 0.122825, acc = 0.85\n",
      "[Train] Batch ID = 25970, loss = 0.0113857, acc = 1.0\n",
      "[Validation] Batch ID = 25970, loss = 0.0164411, acc = 1.0\n",
      "[Train] Batch ID = 25980, loss = 0.00759123, acc = 1.0\n",
      "[Validation] Batch ID = 25980, loss = 0.0752641, acc = 0.95\n",
      "[Train] Batch ID = 25990, loss = 0.314315, acc = 0.65\n",
      "[Validation] Batch ID = 25990, loss = 0.0509192, acc = 0.95\n",
      "[Train] Batch ID = 26000, loss = 0.0204238, acc = 1.0\n",
      "[Validation] Batch ID = 26000, loss = 0.0304908, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0484679 Best loss: 0.0497706\n",
      "[TOTAL Validation] Batch ID = 26000, loss = 0.0484679, acc = 0.974603174603\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.057568239633009693\n",
      "[Train] Batch ID = 26010, loss = 0.0168216, acc = 1.0\n",
      "[Validation] Batch ID = 26010, loss = 0.0500936, acc = 1.0\n",
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      "[Validation] Batch ID = 26020, loss = 0.0746559, acc = 0.95\n",
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      "[Validation] Batch ID = 26030, loss = 0.048777, acc = 1.0\n",
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      "[Validation] Batch ID = 26040, loss = 0.0496575, acc = 0.95\n",
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      "[Validation] Batch ID = 26050, loss = 0.0454085, acc = 0.95\n",
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      "[Validation] Batch ID = 26440, loss = 0.0444731, acc = 1.0\n",
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      "[Validation] Batch ID = 26490, loss = 0.0473191, acc = 1.0\n",
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      "[Validation] Batch ID = 26500, loss = 0.0462583, acc = 1.0\n",
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      "[Train] Batch ID = 26660, loss = 0.0045423, acc = 1.0\n",
      "[Validation] Batch ID = 26660, loss = 0.0534656, acc = 0.95\n",
      "[Train] Batch ID = 26670, loss = 0.0110758, acc = 1.0\n",
      "[Validation] Batch ID = 26670, loss = 0.0210098, acc = 1.0\n",
      "[Train] Batch ID = 26680, loss = 0.0124104, acc = 1.0\n",
      "[Validation] Batch ID = 26680, loss = 0.0713115, acc = 0.9\n",
      "[Train] Batch ID = 26690, loss = 0.0212004, acc = 1.0\n",
      "[Validation] Batch ID = 26690, loss = 0.0223013, acc = 1.0\n",
      "[Train] Batch ID = 26700, loss = 0.280851, acc = 0.7\n",
      "[Validation] Batch ID = 26700, loss = 0.0219291, acc = 1.0\n",
      "[Train] Batch ID = 26710, loss = 0.0111647, acc = 1.0\n",
      "[Validation] Batch ID = 26710, loss = 0.0474596, acc = 1.0\n",
      "[Train] Batch ID = 26720, loss = 0.0118617, acc = 1.0\n",
      "[Validation] Batch ID = 26720, loss = 0.0560457, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 26730, loss = 0.0101166, acc = 1.0\n",
      "[Validation] Batch ID = 26730, loss = 0.0233491, acc = 1.0\n",
      "[Train] Batch ID = 26740, loss = 0.00676514, acc = 1.0\n",
      "[Validation] Batch ID = 26740, loss = 0.0348646, acc = 1.0\n",
      "[Train] Batch ID = 26750, loss = 0.0137924, acc = 1.0\n",
      "[Validation] Batch ID = 26750, loss = 0.055754, acc = 0.95\n",
      "[Train] Batch ID = 26760, loss = 0.00941373, acc = 1.0\n",
      "[Validation] Batch ID = 26760, loss = 0.0372332, acc = 1.0\n",
      "[Train] Batch ID = 26770, loss = 0.0136795, acc = 1.0\n",
      "[Validation] Batch ID = 26770, loss = 0.0389496, acc = 1.0\n",
      "[Train] Batch ID = 26780, loss = 0.0191171, acc = 1.0\n",
      "[Validation] Batch ID = 26780, loss = 0.0288715, acc = 1.0\n",
      "[Train] Batch ID = 26790, loss = 0.0140614, acc = 1.0\n",
      "[Validation] Batch ID = 26790, loss = 0.0580353, acc = 1.0\n",
      "[Train] Batch ID = 26800, loss = 0.00918972, acc = 1.0\n",
      "[Validation] Batch ID = 26800, loss = 0.0168742, acc = 1.0\n",
      "[Train] Batch ID = 26810, loss = 0.0103189, acc = 1.0\n",
      "[Validation] Batch ID = 26810, loss = 0.062421, acc = 0.95\n",
      "[Train] Batch ID = 26820, loss = 0.0138533, acc = 1.0\n",
      "[Validation] Batch ID = 26820, loss = 0.0466853, acc = 0.95\n",
      "[Train] Batch ID = 26830, loss = 0.00265969, acc = 1.0\n",
      "[Validation] Batch ID = 26830, loss = 0.0265705, acc = 1.0\n",
      "[Train] Batch ID = 26840, loss = 0.0115824, acc = 1.0\n",
      "[Validation] Batch ID = 26840, loss = 0.0467503, acc = 0.95\n",
      "[Train] Batch ID = 26850, loss = 0.00289212, acc = 1.0\n",
      "[Validation] Batch ID = 26850, loss = 0.0274831, acc = 1.0\n",
      "[Train] Batch ID = 26860, loss = 0.01989, acc = 1.0\n",
      "[Validation] Batch ID = 26860, loss = 0.061635, acc = 0.95\n",
      "[Train] Batch ID = 26870, loss = 0.02267, acc = 1.0\n",
      "[Validation] Batch ID = 26870, loss = 0.0331661, acc = 1.0\n",
      "[Train] Batch ID = 26880, loss = 0.0102246, acc = 1.0\n",
      "[Validation] Batch ID = 26880, loss = 0.0460261, acc = 1.0\n",
      "[Train] Batch ID = 26890, loss = 0.31942, acc = 0.7\n",
      "[Validation] Batch ID = 26890, loss = 0.0984664, acc = 0.85\n",
      "[Train] Batch ID = 26900, loss = 0.0127749, acc = 1.0\n",
      "[Validation] Batch ID = 26900, loss = 0.0516451, acc = 1.0\n",
      "[Train] Batch ID = 26910, loss = 0.0136773, acc = 1.0\n",
      "[Validation] Batch ID = 26910, loss = 0.0745957, acc = 0.95\n",
      "[Train] Batch ID = 26920, loss = 0.00800008, acc = 1.0\n",
      "[Validation] Batch ID = 26920, loss = 0.0425324, acc = 1.0\n",
      "[Train] Batch ID = 26930, loss = 0.0152819, acc = 1.0\n",
      "[Validation] Batch ID = 26930, loss = 0.0361559, acc = 1.0\n",
      "[Train] Batch ID = 26940, loss = 0.00353019, acc = 1.0\n",
      "[Validation] Batch ID = 26940, loss = 0.065798, acc = 0.95\n",
      "[Train] Batch ID = 26950, loss = 0.0109031, acc = 1.0\n",
      "[Validation] Batch ID = 26950, loss = 0.0326357, acc = 1.0\n",
      "[Train] Batch ID = 26960, loss = 0.00781034, acc = 1.0\n",
      "[Validation] Batch ID = 26960, loss = 0.0208063, acc = 1.0\n",
      "[Train] Batch ID = 26970, loss = 0.00871116, acc = 1.0\n",
      "[Validation] Batch ID = 26970, loss = 0.0487918, acc = 0.95\n",
      "[Train] Batch ID = 26980, loss = 0.0179528, acc = 1.0\n",
      "[Validation] Batch ID = 26980, loss = 0.0556905, acc = 0.95\n",
      "[Train] Batch ID = 26990, loss = 0.0205895, acc = 1.0\n",
      "[Validation] Batch ID = 26990, loss = 0.0417805, acc = 1.0\n",
      "[Train] Batch ID = 27000, loss = 0.0282285, acc = 1.0\n",
      "[Validation] Batch ID = 27000, loss = 0.0931367, acc = 0.9\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0489002 Best loss: 0.0484679\n",
      "[TOTAL Validation] Batch ID = 27000, loss = 0.0489002, acc = 0.971428571429\n",
      "Augmented Factor = 0.051811415669708726\n",
      "[Train] Batch ID = 27010, loss = 0.0103868, acc = 1.0\n",
      "[Validation] Batch ID = 27010, loss = 0.0238705, acc = 1.0\n",
      "[Train] Batch ID = 27020, loss = 0.0156344, acc = 1.0\n",
      "[Validation] Batch ID = 27020, loss = 0.00850536, acc = 1.0\n",
      "[Train] Batch ID = 27030, loss = 0.0316967, acc = 0.95\n",
      "[Validation] Batch ID = 27030, loss = 0.0363903, acc = 1.0\n",
      "[Train] Batch ID = 27040, loss = 0.0187391, acc = 1.0\n",
      "[Validation] Batch ID = 27040, loss = 0.0519505, acc = 0.95\n",
      "[Train] Batch ID = 27050, loss = 0.00629256, acc = 1.0\n",
      "[Validation] Batch ID = 27050, loss = 0.0571744, acc = 0.95\n",
      "[Train] Batch ID = 27060, loss = 0.00676555, acc = 1.0\n",
      "[Validation] Batch ID = 27060, loss = 0.0442867, acc = 1.0\n",
      "[Train] Batch ID = 27070, loss = 0.0084971, acc = 1.0\n",
      "[Validation] Batch ID = 27070, loss = 0.0511798, acc = 0.95\n",
      "[Train] Batch ID = 27080, loss = 0.0135188, acc = 1.0\n",
      "[Validation] Batch ID = 27080, loss = 0.0135706, acc = 1.0\n",
      "[Train] Batch ID = 27090, loss = 0.0169244, acc = 1.0\n",
      "[Validation] Batch ID = 27090, loss = 0.0384846, acc = 1.0\n",
      "[Train] Batch ID = 27100, loss = 0.0164801, acc = 1.0\n",
      "[Validation] Batch ID = 27100, loss = 0.0899702, acc = 0.9\n",
      "[Train] Batch ID = 27110, loss = 0.0160082, acc = 1.0\n",
      "[Validation] Batch ID = 27110, loss = 0.0650536, acc = 0.95\n",
      "[Train] Batch ID = 27120, loss = 0.00690103, acc = 1.0\n",
      "[Validation] Batch ID = 27120, loss = 0.0340084, acc = 1.0\n",
      "[Train] Batch ID = 27130, loss = 0.0113671, acc = 1.0\n",
      "[Validation] Batch ID = 27130, loss = 0.0790165, acc = 0.95\n",
      "[Train] Batch ID = 27140, loss = 0.00638138, acc = 1.0\n",
      "[Validation] Batch ID = 27140, loss = 0.0153196, acc = 1.0\n",
      "[Train] Batch ID = 27150, loss = 0.00955009, acc = 1.0\n",
      "[Validation] Batch ID = 27150, loss = 0.0243848, acc = 1.0\n",
      "[Train] Batch ID = 27160, loss = 0.356612, acc = 0.5\n",
      "[Validation] Batch ID = 27160, loss = 0.0232497, acc = 1.0\n",
      "[Train] Batch ID = 27170, loss = 0.0113162, acc = 1.0\n",
      "[Validation] Batch ID = 27170, loss = 0.0586887, acc = 0.95\n",
      "[Train] Batch ID = 27180, loss = 0.0120623, acc = 1.0\n",
      "[Validation] Batch ID = 27180, loss = 0.105432, acc = 0.9\n",
      "[Train] Batch ID = 27190, loss = 0.0126141, acc = 1.0\n",
      "[Validation] Batch ID = 27190, loss = 0.0598255, acc = 0.95\n",
      "[Train] Batch ID = 27200, loss = 0.0276602, acc = 1.0\n",
      "[Validation] Batch ID = 27200, loss = 0.0596761, acc = 0.95\n",
      "[Train] Batch ID = 27210, loss = 0.0157134, acc = 1.0\n",
      "[Validation] Batch ID = 27210, loss = 0.0400198, acc = 1.0\n",
      "[Train] Batch ID = 27220, loss = 0.0113678, acc = 1.0\n",
      "[Validation] Batch ID = 27220, loss = 0.0248712, acc = 1.0\n",
      "[Train] Batch ID = 27230, loss = 0.00553785, acc = 1.0\n",
      "[Validation] Batch ID = 27230, loss = 0.0237579, acc = 1.0\n",
      "[Train] Batch ID = 27240, loss = 0.0144425, acc = 1.0\n",
      "[Validation] Batch ID = 27240, loss = 0.0128114, acc = 1.0\n",
      "[Train] Batch ID = 27250, loss = 0.0140071, acc = 1.0\n",
      "[Validation] Batch ID = 27250, loss = 0.0237384, acc = 1.0\n",
      "[Train] Batch ID = 27260, loss = 0.0167797, acc = 1.0\n",
      "[Validation] Batch ID = 27260, loss = 0.0291161, acc = 1.0\n",
      "[Train] Batch ID = 27270, loss = 0.00921588, acc = 1.0\n",
      "[Validation] Batch ID = 27270, loss = 0.0360969, acc = 1.0\n",
      "[Train] Batch ID = 27280, loss = 0.00629285, acc = 1.0\n",
      "[Validation] Batch ID = 27280, loss = 0.0232912, acc = 1.0\n",
      "[Train] Batch ID = 27290, loss = 0.0116661, acc = 1.0\n",
      "[Validation] Batch ID = 27290, loss = 0.0176177, acc = 1.0\n",
      "[Train] Batch ID = 27300, loss = 0.010198, acc = 1.0\n",
      "[Validation] Batch ID = 27300, loss = 0.0167815, acc = 1.0\n",
      "[Train] Batch ID = 27310, loss = 0.0274926, acc = 1.0\n",
      "[Validation] Batch ID = 27310, loss = 0.0304763, acc = 1.0\n",
      "[Train] Batch ID = 27320, loss = 0.0290764, acc = 1.0\n",
      "[Validation] Batch ID = 27320, loss = 0.0208895, acc = 1.0\n",
      "[Train] Batch ID = 27330, loss = 0.0117082, acc = 1.0\n",
      "[Validation] Batch ID = 27330, loss = 0.0601754, acc = 0.95\n",
      "[Train] Batch ID = 27340, loss = 0.0104543, acc = 1.0\n",
      "[Validation] Batch ID = 27340, loss = 0.035944, acc = 1.0\n",
      "[Train] Batch ID = 27350, loss = 0.010242, acc = 1.0\n",
      "[Validation] Batch ID = 27350, loss = 0.0191705, acc = 1.0\n",
      "[Train] Batch ID = 27360, loss = 0.0114951, acc = 1.0\n",
      "[Validation] Batch ID = 27360, loss = 0.0402581, acc = 1.0\n",
      "[Train] Batch ID = 27370, loss = 0.00978236, acc = 1.0\n",
      "[Validation] Batch ID = 27370, loss = 0.0441702, acc = 1.0\n",
      "[Train] Batch ID = 27380, loss = 0.00411112, acc = 1.0\n",
      "[Validation] Batch ID = 27380, loss = 0.0496063, acc = 0.95\n",
      "[Train] Batch ID = 27390, loss = 0.0195434, acc = 1.0\n",
      "[Validation] Batch ID = 27390, loss = 0.0273116, acc = 1.0\n",
      "[Train] Batch ID = 27400, loss = 0.00566962, acc = 1.0\n",
      "[Validation] Batch ID = 27400, loss = 0.0530834, acc = 0.95\n",
      "[Train] Batch ID = 27410, loss = 0.0156291, acc = 1.0\n",
      "[Validation] Batch ID = 27410, loss = 0.0357236, acc = 1.0\n",
      "[Train] Batch ID = 27420, loss = 0.0132876, acc = 1.0\n",
      "[Validation] Batch ID = 27420, loss = 0.0573006, acc = 0.95\n",
      "[Train] Batch ID = 27430, loss = 0.0111839, acc = 1.0\n",
      "[Validation] Batch ID = 27430, loss = 0.0425958, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 27440, loss = 0.0271162, acc = 1.0\n",
      "[Validation] Batch ID = 27440, loss = 0.0995558, acc = 0.85\n",
      "[Train] Batch ID = 27450, loss = 0.015518, acc = 1.0\n",
      "[Validation] Batch ID = 27450, loss = 0.0270035, acc = 1.0\n",
      "[Train] Batch ID = 27460, loss = 0.00814166, acc = 1.0\n",
      "[Validation] Batch ID = 27460, loss = 0.0241772, acc = 1.0\n",
      "[Train] Batch ID = 27470, loss = 0.0217701, acc = 1.0\n",
      "[Validation] Batch ID = 27470, loss = 0.0796048, acc = 0.9\n",
      "[Train] Batch ID = 27480, loss = 0.00915481, acc = 1.0\n",
      "[Validation] Batch ID = 27480, loss = 0.0493003, acc = 1.0\n",
      "[Train] Batch ID = 27490, loss = 0.0236594, acc = 1.0\n",
      "[Validation] Batch ID = 27490, loss = 0.0383219, acc = 0.95\n",
      "[Train] Batch ID = 27500, loss = 0.0143661, acc = 1.0\n",
      "[Validation] Batch ID = 27500, loss = 0.0516505, acc = 0.95\n",
      "[Train] Batch ID = 27510, loss = 0.0179936, acc = 1.0\n",
      "[Validation] Batch ID = 27510, loss = 0.048346, acc = 1.0\n",
      "[Train] Batch ID = 27520, loss = 0.00480405, acc = 1.0\n",
      "[Validation] Batch ID = 27520, loss = 0.0333894, acc = 1.0\n",
      "[Train] Batch ID = 27530, loss = 0.0109322, acc = 1.0\n",
      "[Validation] Batch ID = 27530, loss = 0.0597419, acc = 1.0\n",
      "[Train] Batch ID = 27540, loss = 0.0146012, acc = 1.0\n",
      "[Validation] Batch ID = 27540, loss = 0.126582, acc = 0.9\n",
      "[Train] Batch ID = 27550, loss = 0.0196239, acc = 1.0\n",
      "[Validation] Batch ID = 27550, loss = 0.0434703, acc = 1.0\n",
      "[Train] Batch ID = 27560, loss = 0.00773366, acc = 1.0\n",
      "[Validation] Batch ID = 27560, loss = 0.0887142, acc = 0.95\n",
      "[Train] Batch ID = 27570, loss = 0.00620381, acc = 1.0\n",
      "[Validation] Batch ID = 27570, loss = 0.0189433, acc = 1.0\n",
      "[Train] Batch ID = 27580, loss = 0.330757, acc = 0.55\n",
      "[Validation] Batch ID = 27580, loss = 0.045614, acc = 1.0\n",
      "[Train] Batch ID = 27590, loss = 0.015649, acc = 1.0\n",
      "[Validation] Batch ID = 27590, loss = 0.0506355, acc = 1.0\n",
      "[Train] Batch ID = 27600, loss = 0.0130711, acc = 1.0\n",
      "[Validation] Batch ID = 27600, loss = 0.0177349, acc = 1.0\n",
      "[Train] Batch ID = 27610, loss = 0.0209916, acc = 1.0\n",
      "[Validation] Batch ID = 27610, loss = 0.0294041, acc = 1.0\n",
      "[Train] Batch ID = 27620, loss = 0.360483, acc = 0.5\n",
      "[Validation] Batch ID = 27620, loss = 0.0973058, acc = 0.95\n",
      "[Train] Batch ID = 27630, loss = 0.0298321, acc = 1.0\n",
      "[Validation] Batch ID = 27630, loss = 0.0393781, acc = 1.0\n",
      "[Train] Batch ID = 27640, loss = 0.0194225, acc = 1.0\n",
      "[Validation] Batch ID = 27640, loss = 0.0452068, acc = 0.95\n",
      "[Train] Batch ID = 27650, loss = 0.312808, acc = 0.65\n",
      "[Validation] Batch ID = 27650, loss = 0.0417181, acc = 1.0\n",
      "[Train] Batch ID = 27660, loss = 0.0152516, acc = 1.0\n",
      "[Validation] Batch ID = 27660, loss = 0.0702343, acc = 0.95\n",
      "[Train] Batch ID = 27670, loss = 0.0135561, acc = 1.0\n",
      "[Validation] Batch ID = 27670, loss = 0.0421931, acc = 1.0\n",
      "[Train] Batch ID = 27680, loss = 0.0229634, acc = 1.0\n",
      "[Validation] Batch ID = 27680, loss = 0.124586, acc = 0.85\n",
      "[Train] Batch ID = 27690, loss = 0.00955127, acc = 1.0\n",
      "[Validation] Batch ID = 27690, loss = 0.0636628, acc = 1.0\n",
      "[Train] Batch ID = 27700, loss = 0.00821468, acc = 1.0\n",
      "[Validation] Batch ID = 27700, loss = 0.0155305, acc = 1.0\n",
      "[Train] Batch ID = 27710, loss = 0.0151318, acc = 1.0\n",
      "[Validation] Batch ID = 27710, loss = 0.0534236, acc = 0.95\n",
      "[Train] Batch ID = 27720, loss = 0.00486125, acc = 1.0\n",
      "[Validation] Batch ID = 27720, loss = 0.0160958, acc = 1.0\n",
      "[Train] Batch ID = 27730, loss = 0.343845, acc = 0.65\n",
      "[Validation] Batch ID = 27730, loss = 0.0389564, acc = 1.0\n",
      "[Train] Batch ID = 27740, loss = 0.010629, acc = 1.0\n",
      "[Validation] Batch ID = 27740, loss = 0.061228, acc = 0.95\n",
      "[Train] Batch ID = 27750, loss = 0.0228798, acc = 1.0\n",
      "[Validation] Batch ID = 27750, loss = 0.0282652, acc = 1.0\n",
      "[Train] Batch ID = 27760, loss = 0.00996465, acc = 1.0\n",
      "[Validation] Batch ID = 27760, loss = 0.0206973, acc = 1.0\n",
      "[Train] Batch ID = 27770, loss = 0.0144804, acc = 1.0\n",
      "[Validation] Batch ID = 27770, loss = 0.113851, acc = 0.9\n",
      "[Train] Batch ID = 27780, loss = 0.00666077, acc = 1.0\n",
      "[Validation] Batch ID = 27780, loss = 0.030611, acc = 1.0\n",
      "[Train] Batch ID = 27790, loss = 0.01264, acc = 1.0\n",
      "[Validation] Batch ID = 27790, loss = 0.0151804, acc = 1.0\n",
      "[Train] Batch ID = 27800, loss = 0.00709147, acc = 1.0\n",
      "[Validation] Batch ID = 27800, loss = 0.0758167, acc = 1.0\n",
      "[Train] Batch ID = 27810, loss = 0.00731359, acc = 1.0\n",
      "[Validation] Batch ID = 27810, loss = 0.0152599, acc = 1.0\n",
      "[Train] Batch ID = 27820, loss = 0.0163986, acc = 1.0\n",
      "[Validation] Batch ID = 27820, loss = 0.0676576, acc = 0.95\n",
      "[Train] Batch ID = 27830, loss = 0.0127725, acc = 1.0\n",
      "[Validation] Batch ID = 27830, loss = 0.0420076, acc = 1.0\n",
      "[Train] Batch ID = 27840, loss = 0.0313058, acc = 0.95\n",
      "[Validation] Batch ID = 27840, loss = 0.0463579, acc = 0.95\n",
      "[Train] Batch ID = 27850, loss = 0.0112587, acc = 1.0\n",
      "[Validation] Batch ID = 27850, loss = 0.00928514, acc = 1.0\n",
      "[Train] Batch ID = 27860, loss = 0.00303435, acc = 1.0\n",
      "[Validation] Batch ID = 27860, loss = 0.0140082, acc = 1.0\n",
      "[Train] Batch ID = 27870, loss = 0.00951881, acc = 1.0\n",
      "[Validation] Batch ID = 27870, loss = 0.0217359, acc = 1.0\n",
      "[Train] Batch ID = 27880, loss = 0.00964126, acc = 1.0\n",
      "[Validation] Batch ID = 27880, loss = 0.056362, acc = 0.95\n",
      "[Train] Batch ID = 27890, loss = 0.0131413, acc = 1.0\n",
      "[Validation] Batch ID = 27890, loss = 0.0746133, acc = 1.0\n",
      "[Train] Batch ID = 27900, loss = 0.00575348, acc = 1.0\n",
      "[Validation] Batch ID = 27900, loss = 0.0267506, acc = 1.0\n",
      "[Train] Batch ID = 27910, loss = 0.0124203, acc = 1.0\n",
      "[Validation] Batch ID = 27910, loss = 0.0183738, acc = 1.0\n",
      "[Train] Batch ID = 27920, loss = 0.00567721, acc = 1.0\n",
      "[Validation] Batch ID = 27920, loss = 0.054696, acc = 0.95\n",
      "[Train] Batch ID = 27930, loss = 0.00655089, acc = 1.0\n",
      "[Validation] Batch ID = 27930, loss = 0.0364141, acc = 0.95\n",
      "[Train] Batch ID = 27940, loss = 0.00787284, acc = 1.0\n",
      "[Validation] Batch ID = 27940, loss = 0.0714407, acc = 0.95\n",
      "[Train] Batch ID = 27950, loss = 0.0134411, acc = 1.0\n",
      "[Validation] Batch ID = 27950, loss = 0.0375173, acc = 0.95\n",
      "[Train] Batch ID = 27960, loss = 0.00869594, acc = 1.0\n",
      "[Validation] Batch ID = 27960, loss = 0.0298929, acc = 1.0\n",
      "[Train] Batch ID = 27970, loss = 0.00848122, acc = 1.0\n",
      "[Validation] Batch ID = 27970, loss = 0.0286811, acc = 1.0\n",
      "[Train] Batch ID = 27980, loss = 0.0119321, acc = 1.0\n",
      "[Validation] Batch ID = 27980, loss = 0.12738, acc = 0.85\n",
      "[Train] Batch ID = 27990, loss = 0.348982, acc = 0.7\n",
      "[Validation] Batch ID = 27990, loss = 0.0509999, acc = 1.0\n",
      "[Train] Batch ID = 28000, loss = 0.00911156, acc = 1.0\n",
      "[Validation] Batch ID = 28000, loss = 0.0462682, acc = 0.95\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0545515 Best loss: 0.0484679\n",
      "[TOTAL Validation] Batch ID = 28000, loss = 0.0545515, acc = 0.968027210884\n",
      "Augmented Factor = 0.04663027410273785\n",
      "[Train] Batch ID = 28010, loss = 0.00559198, acc = 1.0\n",
      "[Validation] Batch ID = 28010, loss = 0.0512981, acc = 1.0\n",
      "[Train] Batch ID = 28020, loss = 0.00915601, acc = 1.0\n",
      "[Validation] Batch ID = 28020, loss = 0.0126072, acc = 1.0\n",
      "[Train] Batch ID = 28030, loss = 0.011902, acc = 1.0\n",
      "[Validation] Batch ID = 28030, loss = 0.00803735, acc = 1.0\n",
      "[Train] Batch ID = 28040, loss = 0.00712367, acc = 1.0\n",
      "[Validation] Batch ID = 28040, loss = 0.0675776, acc = 0.9\n",
      "[Train] Batch ID = 28050, loss = 0.0147979, acc = 1.0\n",
      "[Validation] Batch ID = 28050, loss = 0.016528, acc = 1.0\n",
      "[Train] Batch ID = 28060, loss = 0.00904032, acc = 1.0\n",
      "[Validation] Batch ID = 28060, loss = 0.0218941, acc = 1.0\n",
      "[Train] Batch ID = 28070, loss = 0.00962401, acc = 1.0\n",
      "[Validation] Batch ID = 28070, loss = 0.0127144, acc = 1.0\n",
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      "[Train] Batch ID = 28090, loss = 0.0156235, acc = 1.0\n",
      "[Validation] Batch ID = 28090, loss = 0.0560121, acc = 0.95\n",
      "[Train] Batch ID = 28100, loss = 0.00410932, acc = 1.0\n",
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      "[Train] Batch ID = 28110, loss = 0.0110446, acc = 1.0\n",
      "[Validation] Batch ID = 28110, loss = 0.0228573, acc = 1.0\n",
      "[Train] Batch ID = 28120, loss = 0.00727432, acc = 1.0\n",
      "[Validation] Batch ID = 28120, loss = 0.143185, acc = 0.9\n",
      "[Train] Batch ID = 28130, loss = 0.0100746, acc = 1.0\n",
      "[Validation] Batch ID = 28130, loss = 0.0445037, acc = 0.95\n",
      "[Train] Batch ID = 28140, loss = 0.00594162, acc = 1.0\n",
      "[Validation] Batch ID = 28140, loss = 0.0360903, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 28150, loss = 0.0192624, acc = 1.0\n",
      "[Validation] Batch ID = 28150, loss = 0.0188427, acc = 1.0\n",
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      "[Train] Batch ID = 28290, loss = 0.00724224, acc = 1.0\n",
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      "[Validation] Batch ID = 28310, loss = 0.0608072, acc = 0.95\n",
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      "[Train] Batch ID = 28370, loss = 0.0141357, acc = 1.0\n",
      "[Validation] Batch ID = 28370, loss = 0.0143462, acc = 1.0\n",
      "[Train] Batch ID = 28380, loss = 0.0159047, acc = 1.0\n",
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      "[Train] Batch ID = 28390, loss = 0.0151132, acc = 1.0\n",
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      "[Validation] Batch ID = 28830, loss = 0.0218341, acc = 1.0\n",
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      "[Validation] Batch ID = 28860, loss = 0.072459, acc = 0.9\n",
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      "[Validation] Batch ID = 28870, loss = 0.017822, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 28880, loss = 0.0228902, acc = 1.0\n",
      "[Validation] Batch ID = 28880, loss = 0.128086, acc = 0.85\n",
      "[Train] Batch ID = 28890, loss = 0.0171544, acc = 1.0\n",
      "[Validation] Batch ID = 28890, loss = 0.0223697, acc = 1.0\n",
      "[Train] Batch ID = 28900, loss = 0.008646, acc = 1.0\n",
      "[Validation] Batch ID = 28900, loss = 0.0147439, acc = 1.0\n",
      "[Train] Batch ID = 28910, loss = 0.00648294, acc = 1.0\n",
      "[Validation] Batch ID = 28910, loss = 0.0421675, acc = 1.0\n",
      "[Train] Batch ID = 28920, loss = 0.0161794, acc = 1.0\n",
      "[Validation] Batch ID = 28920, loss = 0.05996, acc = 0.95\n",
      "[Train] Batch ID = 28930, loss = 0.0202289, acc = 1.0\n",
      "[Validation] Batch ID = 28930, loss = 0.0733339, acc = 0.9\n",
      "[Train] Batch ID = 28940, loss = 0.0110727, acc = 1.0\n",
      "[Validation] Batch ID = 28940, loss = 0.0329849, acc = 0.95\n",
      "[Train] Batch ID = 28950, loss = 0.00616388, acc = 1.0\n",
      "[Validation] Batch ID = 28950, loss = 0.0242042, acc = 1.0\n",
      "[Train] Batch ID = 28960, loss = 0.0152027, acc = 1.0\n",
      "[Validation] Batch ID = 28960, loss = 0.0268711, acc = 1.0\n",
      "[Train] Batch ID = 28970, loss = 0.0132604, acc = 1.0\n",
      "[Validation] Batch ID = 28970, loss = 0.0733769, acc = 0.95\n",
      "[Train] Batch ID = 28980, loss = 0.00407207, acc = 1.0\n",
      "[Validation] Batch ID = 28980, loss = 0.0189076, acc = 1.0\n",
      "[Train] Batch ID = 28990, loss = 0.0237551, acc = 1.0\n",
      "[Validation] Batch ID = 28990, loss = 0.0523311, acc = 1.0\n",
      "[Train] Batch ID = 29000, loss = 0.0141165, acc = 1.0\n",
      "[Validation] Batch ID = 29000, loss = 0.0410519, acc = 0.95\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0499777 Best loss: 0.0484679\n",
      "[TOTAL Validation] Batch ID = 29000, loss = 0.0499777, acc = 0.971428571429\n",
      "Augmented Factor = 0.041967246692464065\n",
      "[Train] Batch ID = 29010, loss = 0.0160004, acc = 1.0\n",
      "[Validation] Batch ID = 29010, loss = 0.0845387, acc = 0.9\n",
      "[Train] Batch ID = 29020, loss = 0.0084397, acc = 1.0\n",
      "[Validation] Batch ID = 29020, loss = 0.0968269, acc = 0.95\n",
      "[Train] Batch ID = 29030, loss = 0.00389956, acc = 1.0\n",
      "[Validation] Batch ID = 29030, loss = 0.0291305, acc = 1.0\n",
      "[Train] Batch ID = 29040, loss = 0.0113721, acc = 1.0\n",
      "[Validation] Batch ID = 29040, loss = 0.0556583, acc = 0.95\n",
      "[Train] Batch ID = 29050, loss = 0.0278837, acc = 1.0\n",
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      "[Train] Batch ID = 29060, loss = 0.00743807, acc = 1.0\n",
      "[Validation] Batch ID = 29060, loss = 0.0212515, acc = 1.0\n",
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      "[Validation] Batch ID = 29070, loss = 0.0643766, acc = 0.95\n",
      "[Train] Batch ID = 29080, loss = 0.00804837, acc = 1.0\n",
      "[Validation] Batch ID = 29080, loss = 0.0311004, acc = 1.0\n",
      "[Train] Batch ID = 29090, loss = 0.00697436, acc = 1.0\n",
      "[Validation] Batch ID = 29090, loss = 0.0484609, acc = 1.0\n",
      "[Train] Batch ID = 29100, loss = 0.00577638, acc = 1.0\n",
      "[Validation] Batch ID = 29100, loss = 0.0120345, acc = 1.0\n",
      "[Train] Batch ID = 29110, loss = 0.00601212, acc = 1.0\n",
      "[Validation] Batch ID = 29110, loss = 0.0484931, acc = 1.0\n",
      "[Train] Batch ID = 29120, loss = 0.00366048, acc = 1.0\n",
      "[Validation] Batch ID = 29120, loss = 0.0629059, acc = 0.95\n",
      "[Train] Batch ID = 29130, loss = 0.00384309, acc = 1.0\n",
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      "[Train] Batch ID = 29140, loss = 0.0111141, acc = 1.0\n",
      "[Validation] Batch ID = 29140, loss = 0.0464546, acc = 1.0\n",
      "[Train] Batch ID = 29150, loss = 0.00446491, acc = 1.0\n",
      "[Validation] Batch ID = 29150, loss = 0.0391683, acc = 1.0\n",
      "[Train] Batch ID = 29160, loss = 0.00879985, acc = 1.0\n",
      "[Validation] Batch ID = 29160, loss = 0.055905, acc = 0.95\n",
      "[Train] Batch ID = 29170, loss = 0.0154207, acc = 1.0\n",
      "[Validation] Batch ID = 29170, loss = 0.0771876, acc = 0.9\n",
      "[Train] Batch ID = 29180, loss = 0.00961058, acc = 1.0\n",
      "[Validation] Batch ID = 29180, loss = 0.0561285, acc = 0.95\n",
      "[Train] Batch ID = 29190, loss = 0.0106284, acc = 1.0\n",
      "[Validation] Batch ID = 29190, loss = 0.00779417, acc = 1.0\n",
      "[Train] Batch ID = 29200, loss = 0.0278142, acc = 1.0\n",
      "[Validation] Batch ID = 29200, loss = 0.0182397, acc = 1.0\n",
      "[Train] Batch ID = 29210, loss = 0.00799601, acc = 1.0\n",
      "[Validation] Batch ID = 29210, loss = 0.0489432, acc = 1.0\n",
      "[Train] Batch ID = 29220, loss = 0.331558, acc = 0.5\n",
      "[Validation] Batch ID = 29220, loss = 0.0659498, acc = 0.95\n",
      "[Train] Batch ID = 29230, loss = 0.00806944, acc = 1.0\n",
      "[Validation] Batch ID = 29230, loss = 0.103164, acc = 0.9\n",
      "[Train] Batch ID = 29240, loss = 0.00859062, acc = 1.0\n",
      "[Validation] Batch ID = 29240, loss = 0.0353921, acc = 1.0\n",
      "[Train] Batch ID = 29250, loss = 0.0189385, acc = 1.0\n",
      "[Validation] Batch ID = 29250, loss = 0.039444, acc = 1.0\n",
      "[Train] Batch ID = 29260, loss = 0.0149383, acc = 1.0\n",
      "[Validation] Batch ID = 29260, loss = 0.0220741, acc = 1.0\n",
      "[Train] Batch ID = 29270, loss = 0.014897, acc = 1.0\n",
      "[Validation] Batch ID = 29270, loss = 0.107075, acc = 0.9\n",
      "[Train] Batch ID = 29280, loss = 0.0114498, acc = 1.0\n",
      "[Validation] Batch ID = 29280, loss = 0.0504239, acc = 0.95\n",
      "[Train] Batch ID = 29290, loss = 0.00309866, acc = 1.0\n",
      "[Validation] Batch ID = 29290, loss = 0.0317118, acc = 1.0\n",
      "[Train] Batch ID = 29300, loss = 0.0122993, acc = 1.0\n",
      "[Validation] Batch ID = 29300, loss = 0.00810749, acc = 1.0\n",
      "[Train] Batch ID = 29310, loss = 0.020829, acc = 1.0\n",
      "[Validation] Batch ID = 29310, loss = 0.0938896, acc = 0.9\n",
      "[Train] Batch ID = 29320, loss = 0.0067056, acc = 1.0\n",
      "[Validation] Batch ID = 29320, loss = 0.0752461, acc = 0.95\n",
      "[Train] Batch ID = 29330, loss = 0.0199295, acc = 1.0\n",
      "[Validation] Batch ID = 29330, loss = 0.0103174, acc = 1.0\n",
      "[Train] Batch ID = 29340, loss = 0.0179278, acc = 1.0\n",
      "[Validation] Batch ID = 29340, loss = 0.0297772, acc = 1.0\n",
      "[Train] Batch ID = 29350, loss = 0.0189854, acc = 1.0\n",
      "[Validation] Batch ID = 29350, loss = 0.0443226, acc = 1.0\n",
      "[Train] Batch ID = 29360, loss = 0.00764502, acc = 1.0\n",
      "[Validation] Batch ID = 29360, loss = 0.0624111, acc = 0.9\n",
      "[Train] Batch ID = 29370, loss = 0.0167271, acc = 1.0\n",
      "[Validation] Batch ID = 29370, loss = 0.0237524, acc = 1.0\n",
      "[Train] Batch ID = 29380, loss = 0.0155116, acc = 1.0\n",
      "[Validation] Batch ID = 29380, loss = 0.0357803, acc = 1.0\n",
      "[Train] Batch ID = 29390, loss = 0.00805501, acc = 1.0\n",
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      "[Validation] Batch ID = 29580, loss = 0.0458462, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 29590, loss = 0.00919053, acc = 1.0\n",
      "[Validation] Batch ID = 29590, loss = 0.0744435, acc = 0.9\n",
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      "[Validation] Batch ID = 29860, loss = 0.0133864, acc = 1.0\n",
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      "[Train] Batch ID = 29890, loss = 0.0235789, acc = 1.0\n",
      "[Validation] Batch ID = 29890, loss = 0.0280722, acc = 1.0\n",
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      "[Validation] Batch ID = 29900, loss = 0.0744076, acc = 0.95\n",
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      "[Validation] Batch ID = 29910, loss = 0.0222754, acc = 1.0\n",
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      "[Validation] Batch ID = 29920, loss = 0.012706, acc = 1.0\n",
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      "[Validation] Batch ID = 29930, loss = 0.0224544, acc = 1.0\n",
      "[Train] Batch ID = 29940, loss = 0.00471518, acc = 1.0\n",
      "[Validation] Batch ID = 29940, loss = 0.051991, acc = 0.95\n",
      "[Train] Batch ID = 29950, loss = 0.00515875, acc = 1.0\n",
      "[Validation] Batch ID = 29950, loss = 0.0257679, acc = 1.0\n",
      "[Train] Batch ID = 29960, loss = 0.0139606, acc = 1.0\n",
      "[Validation] Batch ID = 29960, loss = 0.0348683, acc = 1.0\n",
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      "[Validation] Batch ID = 29970, loss = 0.0146334, acc = 1.0\n",
      "[Train] Batch ID = 29980, loss = 0.0105263, acc = 1.0\n",
      "[Validation] Batch ID = 29980, loss = 0.0344581, acc = 1.0\n",
      "[Train] Batch ID = 29990, loss = 0.00709051, acc = 1.0\n",
      "[Validation] Batch ID = 29990, loss = 0.0272386, acc = 1.0\n",
      "[Train] Batch ID = 30000, loss = 0.00629236, acc = 1.0\n",
      "[Validation] Batch ID = 30000, loss = 0.0444961, acc = 0.95\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0417431 Best loss: 0.0484679\n",
      "[TOTAL Validation] Batch ID = 30000, loss = 0.0417431, acc = 0.973242630385\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.03777052202321766\n",
      "[Train] Batch ID = 30010, loss = 0.0118651, acc = 1.0\n",
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      "[Train] Batch ID = 30720, loss = 0.00440017, acc = 1.0\n",
      "[Validation] Batch ID = 30720, loss = 0.0163661, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 30730, loss = 0.00905366, acc = 1.0\n",
      "[Validation] Batch ID = 30730, loss = 0.0757433, acc = 0.95\n",
      "[Train] Batch ID = 30740, loss = 0.00879986, acc = 1.0\n",
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      "[Train] Batch ID = 30800, loss = 0.012879, acc = 1.0\n",
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      "[Train] Batch ID = 30950, loss = 0.0108882, acc = 1.0\n",
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      "[Train] Batch ID = 30960, loss = 0.0030465, acc = 1.0\n",
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      "[Train] Batch ID = 30970, loss = 0.0083205, acc = 1.0\n",
      "[Validation] Batch ID = 30970, loss = 0.0439755, acc = 1.0\n",
      "[Train] Batch ID = 30980, loss = 0.0153816, acc = 1.0\n",
      "[Validation] Batch ID = 30980, loss = 0.101285, acc = 0.9\n",
      "[Train] Batch ID = 30990, loss = 0.0134147, acc = 1.0\n",
      "[Validation] Batch ID = 30990, loss = 0.0260924, acc = 1.0\n",
      "[Train] Batch ID = 31000, loss = 0.0111061, acc = 1.0\n",
      "[Validation] Batch ID = 31000, loss = 0.028152, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0415063 Best loss: 0.0417431\n",
      "[TOTAL Validation] Batch ID = 31000, loss = 0.0415063, acc = 0.971655328798\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.03399346982089589\n",
      "[Train] Batch ID = 31010, loss = 0.0170458, acc = 1.0\n",
      "[Validation] Batch ID = 31010, loss = 0.0393193, acc = 1.0\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 31730, loss = 0.0134575, acc = 1.0\n",
      "[Validation] Batch ID = 31730, loss = 0.102514, acc = 0.9\n",
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      "[Train] Batch ID = 31950, loss = 0.00970547, acc = 1.0\n",
      "[Validation] Batch ID = 31950, loss = 0.0100081, acc = 1.0\n",
      "[Train] Batch ID = 31960, loss = 0.0101272, acc = 1.0\n",
      "[Validation] Batch ID = 31960, loss = 0.0344191, acc = 0.95\n",
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      "[Validation] Batch ID = 31970, loss = 0.0578113, acc = 0.95\n",
      "[Train] Batch ID = 31980, loss = 0.00625064, acc = 1.0\n",
      "[Validation] Batch ID = 31980, loss = 0.0275623, acc = 1.0\n",
      "[Train] Batch ID = 31990, loss = 0.0113023, acc = 1.0\n",
      "[Validation] Batch ID = 31990, loss = 0.0419937, acc = 0.95\n",
      "[Train] Batch ID = 32000, loss = 0.00601587, acc = 1.0\n",
      "[Validation] Batch ID = 32000, loss = 0.0213612, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0409339 Best loss: 0.0415063\n",
      "[TOTAL Validation] Batch ID = 32000, loss = 0.0409339, acc = 0.967800453515\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.030594122838806304\n",
      "[Train] Batch ID = 32010, loss = 0.00968233, acc = 1.0\n",
      "[Validation] Batch ID = 32010, loss = 0.00704085, acc = 1.0\n",
      "[Train] Batch ID = 32020, loss = 0.351594, acc = 0.65\n",
      "[Validation] Batch ID = 32020, loss = 0.0351, acc = 1.0\n",
      "[Train] Batch ID = 32030, loss = 0.00463197, acc = 1.0\n",
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      "[Train] Batch ID = 32040, loss = 0.0093765, acc = 1.0\n",
      "[Validation] Batch ID = 32040, loss = 0.0386216, acc = 1.0\n",
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      "[Validation] Batch ID = 32050, loss = 0.0772565, acc = 0.9\n",
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      "[Validation] Batch ID = 32060, loss = 0.057845, acc = 0.95\n",
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      "[Validation] Batch ID = 32720, loss = 0.0539247, acc = 0.95\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 32730, loss = 0.0111562, acc = 1.0\n",
      "[Validation] Batch ID = 32730, loss = 0.0260599, acc = 1.0\n",
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      "[Validation] Batch ID = 32920, loss = 0.0209137, acc = 1.0\n",
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      "[Validation] Batch ID = 32930, loss = 0.0369837, acc = 0.95\n",
      "[Train] Batch ID = 32940, loss = 0.00905514, acc = 1.0\n",
      "[Validation] Batch ID = 32940, loss = 0.0144165, acc = 1.0\n",
      "[Train] Batch ID = 32950, loss = 0.00750403, acc = 1.0\n",
      "[Validation] Batch ID = 32950, loss = 0.0313072, acc = 1.0\n",
      "[Train] Batch ID = 32960, loss = 0.00810229, acc = 1.0\n",
      "[Validation] Batch ID = 32960, loss = 0.0503699, acc = 1.0\n",
      "[Train] Batch ID = 32970, loss = 0.00910368, acc = 1.0\n",
      "[Validation] Batch ID = 32970, loss = 0.0384295, acc = 1.0\n",
      "[Train] Batch ID = 32980, loss = 0.00681464, acc = 1.0\n",
      "[Validation] Batch ID = 32980, loss = 0.0603437, acc = 0.95\n",
      "[Train] Batch ID = 32990, loss = 0.00373967, acc = 1.0\n",
      "[Validation] Batch ID = 32990, loss = 0.0300974, acc = 1.0\n",
      "[Train] Batch ID = 33000, loss = 0.00575941, acc = 1.0\n",
      "[Validation] Batch ID = 33000, loss = 0.037146, acc = 0.95\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0393778 Best loss: 0.0409339\n",
      "[TOTAL Validation] Batch ID = 33000, loss = 0.0393778, acc = 0.974829931973\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.027534710554925675\n",
      "[Train] Batch ID = 33010, loss = 0.0104437, acc = 1.0\n",
      "[Validation] Batch ID = 33010, loss = 0.0503971, acc = 1.0\n",
      "[Train] Batch ID = 33020, loss = 0.00444611, acc = 1.0\n",
      "[Validation] Batch ID = 33020, loss = 0.0705065, acc = 0.95\n",
      "[Train] Batch ID = 33030, loss = 0.0042565, acc = 1.0\n",
      "[Validation] Batch ID = 33030, loss = 0.0630739, acc = 0.95\n",
      "[Train] Batch ID = 33040, loss = 0.00281272, acc = 1.0\n",
      "[Validation] Batch ID = 33040, loss = 0.0341871, acc = 1.0\n",
      "[Train] Batch ID = 33050, loss = 0.00361255, acc = 1.0\n",
      "[Validation] Batch ID = 33050, loss = 0.0441929, acc = 1.0\n",
      "[Train] Batch ID = 33060, loss = 0.0153721, acc = 1.0\n",
      "[Validation] Batch ID = 33060, loss = 0.0859114, acc = 0.9\n",
      "[Train] Batch ID = 33070, loss = 0.00835545, acc = 1.0\n",
      "[Validation] Batch ID = 33070, loss = 0.0349528, acc = 1.0\n",
      "[Train] Batch ID = 33080, loss = 0.0102997, acc = 1.0\n",
      "[Validation] Batch ID = 33080, loss = 0.021403, acc = 1.0\n",
      "[Train] Batch ID = 33090, loss = 0.013318, acc = 1.0\n",
      "[Validation] Batch ID = 33090, loss = 0.0268364, acc = 1.0\n",
      "[Train] Batch ID = 33100, loss = 0.00564178, acc = 1.0\n",
      "[Validation] Batch ID = 33100, loss = 0.0300068, acc = 1.0\n",
      "[Train] Batch ID = 33110, loss = 0.00427646, acc = 1.0\n",
      "[Validation] Batch ID = 33110, loss = 0.0305779, acc = 1.0\n",
      "[Train] Batch ID = 33120, loss = 0.0148746, acc = 1.0\n",
      "[Validation] Batch ID = 33120, loss = 0.0356243, acc = 0.95\n",
      "[Train] Batch ID = 33130, loss = 0.0051311, acc = 1.0\n",
      "[Validation] Batch ID = 33130, loss = 0.00989772, acc = 1.0\n",
      "[Train] Batch ID = 33140, loss = 0.00859126, acc = 1.0\n",
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      "[Validation] Batch ID = 33160, loss = 0.047358, acc = 1.0\n",
      "[Train] Batch ID = 33170, loss = 0.00597329, acc = 1.0\n",
      "[Validation] Batch ID = 33170, loss = 0.0362092, acc = 1.0\n",
      "[Train] Batch ID = 33180, loss = 0.00482967, acc = 1.0\n",
      "[Validation] Batch ID = 33180, loss = 0.0158473, acc = 1.0\n",
      "[Train] Batch ID = 33190, loss = 0.00561764, acc = 1.0\n",
      "[Validation] Batch ID = 33190, loss = 0.0250311, acc = 1.0\n",
      "[Train] Batch ID = 33200, loss = 0.00512059, acc = 1.0\n",
      "[Validation] Batch ID = 33200, loss = 0.0429703, acc = 0.95\n",
      "[Train] Batch ID = 33210, loss = 0.00502141, acc = 1.0\n",
      "[Validation] Batch ID = 33210, loss = 0.0356932, acc = 1.0\n",
      "[Train] Batch ID = 33220, loss = 0.00610663, acc = 1.0\n",
      "[Validation] Batch ID = 33220, loss = 0.0254671, acc = 1.0\n",
      "[Train] Batch ID = 33230, loss = 0.00745682, acc = 1.0\n",
      "[Validation] Batch ID = 33230, loss = 0.033694, acc = 0.95\n",
      "[Train] Batch ID = 33240, loss = 0.00811821, acc = 1.0\n",
      "[Validation] Batch ID = 33240, loss = 0.0607304, acc = 0.95\n",
      "[Train] Batch ID = 33250, loss = 0.0109842, acc = 1.0\n",
      "[Validation] Batch ID = 33250, loss = 0.0713675, acc = 0.95\n",
      "[Train] Batch ID = 33260, loss = 0.00781216, acc = 1.0\n",
      "[Validation] Batch ID = 33260, loss = 0.0200448, acc = 1.0\n",
      "[Train] Batch ID = 33270, loss = 0.00734471, acc = 1.0\n",
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      "[Train] Batch ID = 33280, loss = 0.00381433, acc = 1.0\n",
      "[Validation] Batch ID = 33280, loss = 0.0308573, acc = 0.95\n",
      "[Train] Batch ID = 33290, loss = 0.0020797, acc = 1.0\n",
      "[Validation] Batch ID = 33290, loss = 0.0172212, acc = 1.0\n",
      "[Train] Batch ID = 33300, loss = 0.0122411, acc = 1.0\n",
      "[Validation] Batch ID = 33300, loss = 0.0954663, acc = 0.9\n",
      "[Train] Batch ID = 33310, loss = 0.00197562, acc = 1.0\n",
      "[Validation] Batch ID = 33310, loss = 0.0348831, acc = 1.0\n",
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      "[Validation] Batch ID = 33720, loss = 0.0288635, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 33730, loss = 0.01346, acc = 1.0\n",
      "[Validation] Batch ID = 33730, loss = 0.0269707, acc = 0.95\n",
      "[Train] Batch ID = 33740, loss = 0.00772923, acc = 1.0\n",
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      "[Validation] Batch ID = 33910, loss = 0.0262629, acc = 1.0\n",
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      "[Validation] Batch ID = 33920, loss = 0.0194961, acc = 1.0\n",
      "[Train] Batch ID = 33930, loss = 0.00522555, acc = 1.0\n",
      "[Validation] Batch ID = 33930, loss = 0.042374, acc = 0.95\n",
      "[Train] Batch ID = 33940, loss = 0.00253225, acc = 1.0\n",
      "[Validation] Batch ID = 33940, loss = 0.0285737, acc = 1.0\n",
      "[Train] Batch ID = 33950, loss = 0.00200877, acc = 1.0\n",
      "[Validation] Batch ID = 33950, loss = 0.0269075, acc = 0.95\n",
      "[Train] Batch ID = 33960, loss = 0.0196441, acc = 1.0\n",
      "[Validation] Batch ID = 33960, loss = 0.104717, acc = 0.9\n",
      "[Train] Batch ID = 33970, loss = 0.0140752, acc = 1.0\n",
      "[Validation] Batch ID = 33970, loss = 0.0613745, acc = 0.95\n",
      "[Train] Batch ID = 33980, loss = 0.00942575, acc = 1.0\n",
      "[Validation] Batch ID = 33980, loss = 0.0519837, acc = 0.95\n",
      "[Train] Batch ID = 33990, loss = 0.0111756, acc = 1.0\n",
      "[Validation] Batch ID = 33990, loss = 0.0200218, acc = 1.0\n",
      "[Train] Batch ID = 34000, loss = 0.00211971, acc = 1.0\n",
      "[Validation] Batch ID = 34000, loss = 0.0216393, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.038096 Best loss: 0.0393778\n",
      "[TOTAL Validation] Batch ID = 34000, loss = 0.038096, acc = 0.973242630385\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.02478123949943311\n",
      "[Train] Batch ID = 34010, loss = 0.0151905, acc = 1.0\n",
      "[Validation] Batch ID = 34010, loss = 0.0372922, acc = 1.0\n",
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      "[Train] Batch ID = 34640, loss = 0.00556009, acc = 1.0\n",
      "[Validation] Batch ID = 34640, loss = 0.0333368, acc = 1.0\n",
      "[Train] Batch ID = 34650, loss = 0.00990603, acc = 1.0\n",
      "[Validation] Batch ID = 34650, loss = 0.0469619, acc = 1.0\n",
      "[Train] Batch ID = 34660, loss = 0.00645189, acc = 1.0\n",
      "[Validation] Batch ID = 34660, loss = 0.0825298, acc = 0.9\n",
      "[Train] Batch ID = 34670, loss = 0.00398515, acc = 1.0\n",
      "[Validation] Batch ID = 34670, loss = 0.0642066, acc = 0.95\n",
      "[Train] Batch ID = 34680, loss = 0.00366724, acc = 1.0\n",
      "[Validation] Batch ID = 34680, loss = 0.0552447, acc = 0.9\n",
      "[Train] Batch ID = 34690, loss = 0.0110178, acc = 1.0\n",
      "[Validation] Batch ID = 34690, loss = 0.0477724, acc = 0.95\n",
      "[Train] Batch ID = 34700, loss = 0.00755666, acc = 1.0\n",
      "[Validation] Batch ID = 34700, loss = 0.100631, acc = 0.9\n",
      "[Train] Batch ID = 34710, loss = 0.00702933, acc = 1.0\n",
      "[Validation] Batch ID = 34710, loss = 0.0405237, acc = 1.0\n",
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      "[Validation] Batch ID = 34720, loss = 0.0105208, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 34730, loss = 0.00835629, acc = 1.0\n",
      "[Validation] Batch ID = 34730, loss = 0.0621039, acc = 0.95\n",
      "[Train] Batch ID = 34740, loss = 0.0027211, acc = 1.0\n",
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      "[Validation] Batch ID = 34760, loss = 0.0558632, acc = 1.0\n",
      "[Train] Batch ID = 34770, loss = 0.0107029, acc = 1.0\n",
      "[Validation] Batch ID = 34770, loss = 0.0229061, acc = 1.0\n",
      "[Train] Batch ID = 34780, loss = 0.0033408, acc = 1.0\n",
      "[Validation] Batch ID = 34780, loss = 0.0486381, acc = 0.95\n",
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      "[Validation] Batch ID = 34790, loss = 0.0313953, acc = 0.95\n",
      "[Train] Batch ID = 34800, loss = 0.00440521, acc = 1.0\n",
      "[Validation] Batch ID = 34800, loss = 0.0848406, acc = 0.9\n",
      "[Train] Batch ID = 34810, loss = 0.00279905, acc = 1.0\n",
      "[Validation] Batch ID = 34810, loss = 0.028152, acc = 1.0\n",
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      "[Validation] Batch ID = 34820, loss = 0.0235083, acc = 0.95\n",
      "[Train] Batch ID = 34830, loss = 0.0229424, acc = 0.95\n",
      "[Validation] Batch ID = 34830, loss = 0.0159734, acc = 1.0\n",
      "[Train] Batch ID = 34840, loss = 0.00874534, acc = 1.0\n",
      "[Validation] Batch ID = 34840, loss = 0.0769302, acc = 0.9\n",
      "[Train] Batch ID = 34850, loss = 0.0107407, acc = 1.0\n",
      "[Validation] Batch ID = 34850, loss = 0.0176174, acc = 1.0\n",
      "[Train] Batch ID = 34860, loss = 0.00806824, acc = 1.0\n",
      "[Validation] Batch ID = 34860, loss = 0.0275901, acc = 1.0\n",
      "[Train] Batch ID = 34870, loss = 0.00759299, acc = 1.0\n",
      "[Validation] Batch ID = 34870, loss = 0.0393554, acc = 1.0\n",
      "[Train] Batch ID = 34880, loss = 0.00572395, acc = 1.0\n",
      "[Validation] Batch ID = 34880, loss = 0.0719942, acc = 0.95\n",
      "[Train] Batch ID = 34890, loss = 0.00530158, acc = 1.0\n",
      "[Validation] Batch ID = 34890, loss = 0.0524166, acc = 0.95\n",
      "[Train] Batch ID = 34900, loss = 0.0113822, acc = 1.0\n",
      "[Validation] Batch ID = 34900, loss = 0.0142821, acc = 1.0\n",
      "[Train] Batch ID = 34910, loss = 0.328356, acc = 0.5\n",
      "[Validation] Batch ID = 34910, loss = 0.0222238, acc = 1.0\n",
      "[Train] Batch ID = 34920, loss = 0.014253, acc = 1.0\n",
      "[Validation] Batch ID = 34920, loss = 0.0110131, acc = 1.0\n",
      "[Train] Batch ID = 34930, loss = 0.00674066, acc = 1.0\n",
      "[Validation] Batch ID = 34930, loss = 0.0186037, acc = 1.0\n",
      "[Train] Batch ID = 34940, loss = 0.00235235, acc = 1.0\n",
      "[Validation] Batch ID = 34940, loss = 0.0157737, acc = 1.0\n",
      "[Train] Batch ID = 34950, loss = 0.00440479, acc = 1.0\n",
      "[Validation] Batch ID = 34950, loss = 0.110208, acc = 0.85\n",
      "[Train] Batch ID = 34960, loss = 0.00451844, acc = 1.0\n",
      "[Validation] Batch ID = 34960, loss = 0.0294122, acc = 1.0\n",
      "[Train] Batch ID = 34970, loss = 0.00297442, acc = 1.0\n",
      "[Validation] Batch ID = 34970, loss = 0.0190149, acc = 1.0\n",
      "[Train] Batch ID = 34980, loss = 0.00377494, acc = 1.0\n",
      "[Validation] Batch ID = 34980, loss = 0.0327786, acc = 1.0\n",
      "[Train] Batch ID = 34990, loss = 0.00252754, acc = 1.0\n",
      "[Validation] Batch ID = 34990, loss = 0.00955295, acc = 1.0\n",
      "[Train] Batch ID = 35000, loss = 0.00265349, acc = 1.0\n",
      "[Validation] Batch ID = 35000, loss = 0.031282, acc = 0.95\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0367303 Best loss: 0.038096\n",
      "[TOTAL Validation] Batch ID = 35000, loss = 0.0367303, acc = 0.974829931973\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1vl_16_c2d_0.7_rs_1_c1n_256_lr_0.0001_c2vl_32_c2n_64_c1s_9_c2s_6_c1nf_16_c1s_5--TrafficSign--1518328441.7715068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.0223031155494898\n",
      "[Train] Batch ID = 35010, loss = 0.014114, acc = 1.0\n",
      "[Validation] Batch ID = 35010, loss = 0.0110693, acc = 1.0\n",
      "[Train] Batch ID = 35020, loss = 0.0127414, acc = 1.0\n",
      "[Validation] Batch ID = 35020, loss = 0.0433513, acc = 1.0\n",
      "[Train] Batch ID = 35030, loss = 0.00760004, acc = 1.0\n",
      "[Validation] Batch ID = 35030, loss = 0.0157327, acc = 1.0\n",
      "[Train] Batch ID = 35040, loss = 0.00923885, acc = 1.0\n",
      "[Validation] Batch ID = 35040, loss = 0.0173605, acc = 1.0\n",
      "[Train] Batch ID = 35050, loss = 0.0025613, acc = 1.0\n",
      "[Validation] Batch ID = 35050, loss = 0.00859295, acc = 1.0\n",
      "[Train] Batch ID = 35060, loss = 0.00412515, acc = 1.0\n",
      "[Validation] Batch ID = 35060, loss = 0.0301139, acc = 1.0\n",
      "[Train] Batch ID = 35070, loss = 0.00567862, acc = 1.0\n",
      "[Validation] Batch ID = 35070, loss = 0.0253738, acc = 1.0\n",
      "[Train] Batch ID = 35080, loss = 0.00919179, acc = 1.0\n",
      "[Validation] Batch ID = 35080, loss = 0.0630545, acc = 0.95\n",
      "[Train] Batch ID = 35090, loss = 0.00719945, acc = 1.0\n",
      "[Validation] Batch ID = 35090, loss = 0.128013, acc = 0.8\n",
      "[Train] Batch ID = 35100, loss = 0.350173, acc = 0.65\n",
      "[Validation] Batch ID = 35100, loss = 0.0318907, acc = 1.0\n",
      "[Train] Batch ID = 35110, loss = 0.0123784, acc = 1.0\n",
      "[Validation] Batch ID = 35110, loss = 0.0553748, acc = 0.95\n",
      "[Train] Batch ID = 35120, loss = 0.00865715, acc = 1.0\n",
      "[Validation] Batch ID = 35120, loss = 0.0293734, acc = 1.0\n",
      "[Train] Batch ID = 35130, loss = 0.00630337, acc = 1.0\n",
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      "[Train] Batch ID = 35140, loss = 0.0040148, acc = 1.0\n",
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      "[Train] Batch ID = 35150, loss = 0.0044137, acc = 1.0\n",
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      "[Train] Batch ID = 35170, loss = 0.0133209, acc = 1.0\n",
      "[Validation] Batch ID = 35170, loss = 0.0663927, acc = 0.9\n",
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      "[Validation] Batch ID = 35180, loss = 0.0323266, acc = 1.0\n",
      "[Train] Batch ID = 35190, loss = 0.00581954, acc = 1.0\n",
      "[Validation] Batch ID = 35190, loss = 0.0799431, acc = 0.9\n",
      "[Train] Batch ID = 35200, loss = 0.00199923, acc = 1.0\n",
      "[Validation] Batch ID = 35200, loss = 0.0619695, acc = 0.95\n",
      "[Train] Batch ID = 35210, loss = 0.0127826, acc = 1.0\n",
      "[Validation] Batch ID = 35210, loss = 0.0256974, acc = 1.0\n",
      "[Train] Batch ID = 35220, loss = 0.00771311, acc = 1.0\n",
      "[Validation] Batch ID = 35220, loss = 0.015765, acc = 1.0\n",
      "[Train] Batch ID = 35230, loss = 0.0131362, acc = 1.0\n",
      "[Validation] Batch ID = 35230, loss = 0.0423079, acc = 0.95\n",
      "[Train] Batch ID = 35240, loss = 0.0032539, acc = 1.0\n",
      "[Validation] Batch ID = 35240, loss = 0.0189496, acc = 1.0\n",
      "[Train] Batch ID = 35250, loss = 0.00689725, acc = 1.0\n",
      "[Validation] Batch ID = 35250, loss = 0.0406073, acc = 0.95\n",
      "[Train] Batch ID = 35260, loss = 0.011237, acc = 1.0\n",
      "[Validation] Batch ID = 35260, loss = 0.10078, acc = 0.9\n",
      "[Train] Batch ID = 35270, loss = 0.00252338, acc = 1.0\n",
      "[Validation] Batch ID = 35270, loss = 0.0551675, acc = 0.95\n",
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      "[Validation] Batch ID = 35280, loss = 0.0481935, acc = 0.95\n",
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      "[Validation] Batch ID = 35290, loss = 0.0575739, acc = 0.95\n",
      "[Train] Batch ID = 35300, loss = 0.00665064, acc = 1.0\n",
      "[Validation] Batch ID = 35300, loss = 0.0289109, acc = 1.0\n",
      "[Train] Batch ID = 35310, loss = 0.0098773, acc = 1.0\n",
      "[Validation] Batch ID = 35310, loss = 0.0326654, acc = 0.95\n",
      "[Train] Batch ID = 35320, loss = 0.00682053, acc = 1.0\n",
      "[Validation] Batch ID = 35320, loss = 0.0467948, acc = 1.0\n",
      "[Train] Batch ID = 35330, loss = 0.0088753, acc = 1.0\n",
      "[Validation] Batch ID = 35330, loss = 0.0764941, acc = 0.9\n",
      "[Train] Batch ID = 35340, loss = 0.00750441, acc = 1.0\n",
      "[Validation] Batch ID = 35340, loss = 0.0921542, acc = 0.85\n",
      "[Train] Batch ID = 35350, loss = 0.00445802, acc = 1.0\n",
      "[Validation] Batch ID = 35350, loss = 0.0466406, acc = 0.95\n",
      "[Train] Batch ID = 35360, loss = 0.00423119, acc = 1.0\n",
      "[Validation] Batch ID = 35360, loss = 0.0482516, acc = 1.0\n",
      "[Train] Batch ID = 35370, loss = 0.00425953, acc = 1.0\n",
      "[Validation] Batch ID = 35370, loss = 0.0152464, acc = 1.0\n",
      "[Train] Batch ID = 35380, loss = 0.00449221, acc = 1.0\n",
      "[Validation] Batch ID = 35380, loss = 0.0381137, acc = 0.95\n",
      "[Train] Batch ID = 35390, loss = 0.00531714, acc = 1.0\n",
      "[Validation] Batch ID = 35390, loss = 0.0281281, acc = 1.0\n",
      "[Train] Batch ID = 35400, loss = 0.00574261, acc = 1.0\n",
      "[Validation] Batch ID = 35400, loss = 0.0617883, acc = 0.9\n",
      "[Train] Batch ID = 35410, loss = 0.00763377, acc = 1.0\n",
      "[Validation] Batch ID = 35410, loss = 0.020997, acc = 0.95\n",
      "[Train] Batch ID = 35420, loss = 0.00801608, acc = 1.0\n",
      "[Validation] Batch ID = 35420, loss = 0.00973775, acc = 1.0\n",
      "[Train] Batch ID = 35430, loss = 0.00499615, acc = 1.0\n",
      "[Validation] Batch ID = 35430, loss = 0.019508, acc = 1.0\n",
      "[Train] Batch ID = 35440, loss = 0.00341999, acc = 1.0\n",
      "[Validation] Batch ID = 35440, loss = 0.0119677, acc = 1.0\n",
      "[Train] Batch ID = 35450, loss = 0.00122769, acc = 1.0\n",
      "[Validation] Batch ID = 35450, loss = 0.0528067, acc = 0.95\n",
      "[Train] Batch ID = 35460, loss = 0.00329072, acc = 1.0\n",
      "[Validation] Batch ID = 35460, loss = 0.0728209, acc = 0.95\n",
      "[Train] Batch ID = 35470, loss = 0.00284836, acc = 1.0\n",
      "[Validation] Batch ID = 35470, loss = 0.0413233, acc = 0.95\n",
      "[Train] Batch ID = 35480, loss = 0.0099092, acc = 1.0\n",
      "[Validation] Batch ID = 35480, loss = 0.071488, acc = 0.95\n",
      "[Train] Batch ID = 35490, loss = 0.0079867, acc = 1.0\n",
      "[Validation] Batch ID = 35490, loss = 0.0415399, acc = 1.0\n",
      "[Train] Batch ID = 35500, loss = 0.00975513, acc = 1.0\n",
      "[Validation] Batch ID = 35500, loss = 0.0214919, acc = 1.0\n",
      "[Train] Batch ID = 35510, loss = 0.00669824, acc = 1.0\n",
      "[Validation] Batch ID = 35510, loss = 0.0656564, acc = 0.95\n",
      "[Train] Batch ID = 35520, loss = 0.0012009, acc = 1.0\n",
      "[Validation] Batch ID = 35520, loss = 0.020219, acc = 1.0\n",
      "[Train] Batch ID = 35530, loss = 0.0105476, acc = 1.0\n",
      "[Validation] Batch ID = 35530, loss = 0.0232488, acc = 1.0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-d7273b31b1ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;31m### Training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m     \u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m     \u001b[0;31m### Validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0mx_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-b27ddab8c87f>\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, images, labels, tb_save)\u001b[0m\n\u001b[1;32m    195\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_images\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_labels\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_conv_2_dropout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv_2_dropout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m         })\n\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    893\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    896\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1122\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1124\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1125\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m       \u001b[0mresults\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~/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1319\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1320\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1321\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1322\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1325\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1326\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1327\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1328\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1329\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/tensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1304\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1305\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1306\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "### Train your model here.\n",
    "### Calculate and report the accuracy on the training and validation set.\n",
    "### Once a final model architecture is selected, \n",
    "### the accuracy on the test set should be calculated and reported as well.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "BATCH_SIZE = 20\n",
    "\n",
    "# Utils method to print the current progression\n",
    "def plot_progression(b, cost, acc, label): print(\n",
    "    \"[%s] Batch ID = %s, loss = %s, acc = %s\" % (label, b, cost, acc))\n",
    "\n",
    "# Training pipeline\n",
    "b = 0\n",
    "valid_batch = inference_datagen.flow(X_valid, y_valid, batch_size=BATCH_SIZE)\n",
    "best_validation_loss = None\n",
    "augmented_factor = 0.99\n",
    "decrease_factor = 0.90\n",
    "train_batches = train_datagen.flow(X_train, y_train, batch_size=BATCH_SIZE)\n",
    "augmented_train_batches = train_datagen_augmented.flow(X_train, y_train, batch_size=BATCH_SIZE)\n",
    "\n",
    "while True:\n",
    "    next_batch = next(\n",
    "        augmented_train_batches if random.uniform(0, 1) < augmented_factor else train_batches)\n",
    "    x_batch, y_batch = next_batch\n",
    "\n",
    "    ### Training\n",
    "    cost, acc = model.optimize(x_batch, y_batch)\n",
    "    ### Validation\n",
    "    x_batch, y_batch = next(valid_batch, None)\n",
    "    # Retrieve the cost and acc on this validation batch and save it in tensorboard\n",
    "    cost_val, acc_val = model.evaluate(x_batch, y_batch, tb_test_save=True)\n",
    "\n",
    "    if b % 10 == 0: # Plot the last results\n",
    "        plot_progression(b, cost, acc, \"Train\")\n",
    "        plot_progression(b, cost_val, acc_val, \"Validation\")\n",
    "    if b % 1000 == 0: # Test the model on all the validation\n",
    "        print(\"Evaluate full validation dataset ...\")\n",
    "        loss, acc, _ = model.evaluate_dataset(X_valid, y_valid)\n",
    "        print(\"Current loss: %s Best loss: %s\" % (loss, best_validation_loss))\n",
    "        plot_progression(b, loss, acc, \"TOTAL Validation\")\n",
    "        if best_validation_loss is None or loss < best_validation_loss:\n",
    "            best_validation_loss = loss\n",
    "            model.save()\n",
    "        augmented_factor = augmented_factor * decrease_factor\n",
    "        print(\"Augmented Factor = %s\" % augmented_factor)\n",
    "\n",
    "    b += 1\n",
    "\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy =  0.967062549485\n",
      "Test Loss =  0.0440878\n"
     ]
    }
   ],
   "source": [
    "# Test the model on the test set\n",
    "\n",
    "# Evaluate all the dataset\n",
    "loss, acc, predicted_class = model.evaluate_dataset(X_test, y_test)\n",
    "\n",
    "print(\"Test Accuracy = \", acc)\n",
    "print(\"Test Loss = \", loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 3: Test a Model on New Images\n",
    "\n",
    "To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.\n",
    "\n",
    "You may find `signnames.csv` useful as it contains mappings from the class id (integer) to the actual sign name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Output the Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q3h/z7/+YzjkRO/IbhqdY8BuGp1jwG4anWPAbhqdY8BuGp6TSrisLwHsAwoi7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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1814106898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18c2e17518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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787GD3me396PX8/AuAD8D8NcA1q2QH/8NwIMAtqMXfBuG4MfF6P1Jvx3A/f1/\nlw57TQI/hromAF6PXlHc7ei90fy7w67ZewE8CuCbAEYWM4++4SdEpuS+4SdEtij4hcgUBb8QmaLg\nFyJTFPxCZIqCX4hMUfALkSkKfiEy5f8DuxjG22efLrIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18c2e32898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1814106828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18141ce0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Load the images and plot them here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import os\n",
    "images = []\n",
    "\n",
    "# Read all image into the folder\n",
    "for filename in os.listdir(\"from_web\"):\n",
    "    img = Image.open(os.path.join(\"from_web\", filename))\n",
    "    img = img.resize((32, 32))\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    img = np.array(img) / 255\n",
    "    images.append(img)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Run the predictions here and use the model to output the prediction for each image.\n",
    "### Make sure to pre-process the images with the same pre-processing pipeline used earlier.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "# Get the prediction\n",
    "predictions = model.predict(images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oxq2zknVLS+N5ut53Vnxl+bxe8TiAyneOC7PFf3iw4HXq1atXmG0cnCzLlsT8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CYP3sLmw1PcpUokfRV4JrAEuBv4e+CbwMXAAcDtwP+OiNEXpHc0SU8FfgT8Dw9ed/MeiuukunZs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\nM7OKnEiZmZmZVfT/ASHKvMVu1uQzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f18140795c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Calculate the accuracy for these 5 new images. \n",
    "### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.\n",
    "\n",
    "# Plot the result\n",
    "fig, axs = plt.subplots(5, 2, figsize=(10, 25))\n",
    "axs = axs.ravel()\n",
    "for i in range(10):\n",
    "    if i%2 == 0:\n",
    "        axs[i].axis('off')\n",
    "        axs[i].imshow(images[i // 2])\n",
    "        axs[i].set_title(\"Prediction: %s\" % id_to_name[np.argmax(predictions[i // 2])])\n",
    "    else:\n",
    "        axs[i].bar(np.arange(43), predictions[i // 2])\n",
    "        axs[i].set_ylabel(\"Softmax\")\n",
    "        axs[i].set_xlabel(\"Labels\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Top 5 Softmax Probabilities For Each Image Found on the Web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of the new images, print out the model's softmax probabilities to show the **certainty** of the model's predictions (limit the output to the top 5 probabilities for each image). [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. \n",
    "\n",
    "The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.\n",
    "\n",
    "`tf.nn.top_k` will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.\n",
    "\n",
    "Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. `tf.nn.top_k` is used to choose the three classes with the highest probability:\n",
    "\n",
    "```\n",
    "# (5, 6) array\n",
    "a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,\n",
    "         0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,\n",
    "         0.15899337],\n",
    "       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,\n",
    "         0.23892179],\n",
    "       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,\n",
    "         0.16505091],\n",
    "       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,\n",
    "         0.09155967]])\n",
    "```\n",
    "\n",
    "Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))` produces:\n",
    "\n",
    "```\n",
    "TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.18063401],\n",
    "       [ 0.26076848,  0.23892179,  0.23664738],\n",
    "       [ 0.29198961,  0.26234032,  0.16505091],\n",
    "       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],\n",
    "       [0, 1, 4],\n",
    "       [0, 5, 1],\n",
    "       [1, 3, 5],\n",
    "       [1, 4, 3]], dtype=int32))\n",
    "```\n",
    "\n",
    "Looking just at the first row we get `[ 0.34763842,  0.24879643,  0.12789202]`, you can confirm these are the 3 largest probabilities in `a`. You'll also notice `[3, 0, 5]` are the corresponding indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web. \n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Writeup\n",
    "\n",
    "Once you have completed the code implementation, document your results in a project writeup using this [template](https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md) as a guide. The writeup can be in a markdown or pdf file. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 4 (Optional): Visualize the Neural Network's State with Test Images\n",
    "\n",
    " This Section is not required to complete but acts as an additional excersise for understaning the output of a neural network's weights. While neural networks can be a great learning device they are often referred to as a black box. We can understand what the weights of a neural network look like better by plotting their feature maps. After successfully training your neural network you can see what it's feature maps look like by plotting the output of the network's weight layers in response to a test stimuli image. From these plotted feature maps, it's possible to see what characteristics of an image the network finds interesting. For a sign, maybe the inner network feature maps react with high activation to the sign's boundary outline or to the contrast in the sign's painted symbol.\n",
    "\n",
    " Provided for you below is the function code that allows you to get the visualization output of any tensorflow weight layer you want. The inputs to the function should be a stimuli image, one used during training or a new one you provided, and then the tensorflow variable name that represents the layer's state during the training process, for instance if you wanted to see what the [LeNet lab's](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) feature maps looked like for it's second convolutional layer you could enter conv2 as the tf_activation variable.\n",
    "\n",
    "For an example of what feature map outputs look like, check out NVIDIA's results in their paper [End-to-End Deep Learning for Self-Driving Cars](https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/) in the section Visualization of internal CNN State. NVIDIA was able to show that their network's inner weights had high activations to road boundary lines by comparing feature maps from an image with a clear path to one without. Try experimenting with a similar test to show that your trained network's weights are looking for interesting features, whether it's looking at differences in feature maps from images with or without a sign, or even what feature maps look like in a trained network vs a completely untrained one on the same sign image.\n",
    "\n",
    "<figure>\n",
    " <img src=\"visualize_cnn.png\" width=\"380\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " <p style=\"text-align: center;\"> Your output should look something like this (above)</p> \n",
    " </figcaption>\n",
    "</figure>\n",
    " <p></p> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Visualize your network's feature maps here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "# image_input: the test image being fed into the network to produce the feature maps\n",
    "# tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer\n",
    "# activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output\n",
    "# plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry\n",
    "\n",
    "def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):\n",
    "    # Here make sure to preprocess your image_input in a way your network expects\n",
    "    # with size, normalization, ect if needed\n",
    "    # image_input =\n",
    "    # Note: x should be the same name as your network's tensorflow data placeholder variable\n",
    "    # If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function\n",
    "    activation = tf_activation.eval(session=sess,feed_dict={x : image_input})\n",
    "    featuremaps = activation.shape[3]\n",
    "    plt.figure(plt_num, figsize=(15,15))\n",
    "    for featuremap in range(featuremaps):\n",
    "        plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column\n",
    "        plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number\n",
    "        if activation_min != -1 & activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin =activation_min, vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_min !=-1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin=activation_min, cmap=\"gray\")\n",
    "        else:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", cmap=\"gray\")"
   ]
  }
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