{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CIFAR10 CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train a simple deep CNN on the CIFAR10 small images dataset.\n",
    "\n",
    "Some constants we'll use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "num_classes = 10\n",
    "epochs = 200\n",
    "data_augmentation = True\n",
    "num_predictions = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n",
      "conx, version 3.5.9\n"
     ]
    }
   ],
   "source": [
    "from conx import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = Network(\"CIRAR10\")\n",
    "net.add(ImageLayer(\"input\", (32, 32), 3)) # depends on K.image_data_format(), right?\n",
    "net.add(Conv2DLayer(\"conv1\", 32, (3, 3), padding='same', activation='relu'))\n",
    "net.add(Conv2DLayer(\"conv2\", 32, (3, 3), activation='relu'))\n",
    "net.add(MaxPool2DLayer(\"pool1\", pool_size=(2, 2), dropout=0.25))\n",
    "net.add(Conv2DLayer(\"conv3\", 64, (3, 3), padding='same', activation='relu'))\n",
    "net.add(Conv2DLayer(\"conv4\", 64, (3, 3), activation='relu'))\n",
    "net.add(MaxPool2DLayer(\"pool2\", pool_size=(2, 2), dropout=0.25))\n",
    "net.add(FlattenLayer(\"flatten\"))\n",
    "net.add(Layer(\"hidden1\", 512, activation='relu', vshape=(16, 32), dropout=0.5))\n",
    "net.add(Layer(\"output\", num_classes, activation='softmax'))\n",
    "net.connect()\n",
    "\n",
    "# initiate RMSprop optimizer\n",
    "opt = RMSprop(lr=0.0001, decay=1e-6)\n",
    "\n",
    "net.compile(loss='categorical_crossentropy',\n",
    "            optimizer=opt)\n",
    "\n",
    "# Let's train the model using RMSprop\n",
    "net.compile(loss='categorical_crossentropy',\n",
    "              optimizer=opt,\n",
    "              metrics=['accuracy'])\n",
    "model = net.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.dataset.get(\"cifar10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Dataset name**: CIFAR-10\n",
       "\n",
       "\n",
       "Original source: https://www.cs.toronto.edu/~kriz/cifar.html\n",
       "\n",
       "The CIFAR-10 dataset consists of 60000 32x32 colour images in 10\n",
       "classes, with 6000 images per class.\n",
       "\n",
       "The classes are completely mutually exclusive. There is no overlap\n",
       "between automobiles and trucks. \"Automobile\" includes sedans, SUVs,\n",
       "things of that sort. \"Truck\" includes only big trucks. Neither\n",
       "includes pickup trucks.\n",
       "\n",
       "**Dataset Split**:\n",
       "   * training  : 60000\n",
       "   * testing   : 0\n",
       "   * total     : 60000\n",
       "\n",
       "**Input Summary**:\n",
       "   * shape  : [(32, 32, 3)]\n",
       "   * range  : [(0.0, 1.0)]\n",
       "\n",
       "**Target Summary**:\n",
       "   * shape  : [(10,)]\n",
       "   * range  : [(0.0, 1.0)]\n",
       "\n"
      ],
      "text/plain": [
       "**Dataset name**: CIFAR-10\n",
       "\n",
       "\n",
       "Original source: https://www.cs.toronto.edu/~kriz/cifar.html\n",
       "\n",
       "The CIFAR-10 dataset consists of 60000 32x32 colour images in 10\n",
       "classes, with 6000 images per class.\n",
       "\n",
       "The classes are completely mutually exclusive. There is no overlap\n",
       "between automobiles and trucks. \"Automobile\" includes sedans, SUVs,\n",
       "things of that sort. \"Truck\" includes only big trucks. Neither\n",
       "includes pickup trucks.\n",
       "\n",
       "**Dataset Split**:\n",
       "   * training  : 60000\n",
       "   * testing   : 0\n",
       "   * total     : 60000\n",
       "\n",
       "**Input Summary**:\n",
       "   * shape  : [(32, 32, 3)]\n",
       "   * range  : [(0.0, 1.0)]\n",
       "\n",
       "**Target Summary**:\n",
       "   * shape  : [(10,)]\n",
       "   * range  : [(0.0, 1.0)]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.dataset.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 32, 32, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.dataset._inputs[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "**Dataset name**: CIFAR-10\n",
       "\n",
       "\n",
       "Original source: https://www.cs.toronto.edu/~kriz/cifar.html\n",
       "\n",
       "The CIFAR-10 dataset consists of 60000 32x32 colour images in 10\n",
       "classes, with 6000 images per class.\n",
       "\n",
       "The classes are completely mutually exclusive. There is no overlap\n",
       "between automobiles and trucks. \"Automobile\" includes sedans, SUVs,\n",
       "things of that sort. \"Truck\" includes only big trucks. Neither\n",
       "includes pickup trucks.\n",
       "\n",
       "**Dataset Split**:\n",
       "   * training  : 10000\n",
       "   * testing   : 50000\n",
       "   * total     : 60000\n",
       "\n",
       "**Input Summary**:\n",
       "   * shape  : [(32, 32, 3)]\n",
       "   * range  : [(0.0, 1.0)]\n",
       "\n",
       "**Target Summary**:\n",
       "   * shape  : [(10,)]\n",
       "   * range  : [(0.0, 1.0)]\n",
       "\n"
      ],
      "text/plain": [
       "**Dataset name**: CIFAR-10\n",
       "\n",
       "\n",
       "Original source: https://www.cs.toronto.edu/~kriz/cifar.html\n",
       "\n",
       "The CIFAR-10 dataset consists of 60000 32x32 colour images in 10\n",
       "classes, with 6000 images per class.\n",
       "\n",
       "The classes are completely mutually exclusive. There is no overlap\n",
       "between automobiles and trucks. \"Automobile\" includes sedans, SUVs,\n",
       "things of that sort. \"Truck\" includes only big trucks. Neither\n",
       "includes pickup trucks.\n",
       "\n",
       "**Dataset Split**:\n",
       "   * training  : 10000\n",
       "   * testing   : 50000\n",
       "   * total     : 60000\n",
       "\n",
       "**Input Summary**:\n",
       "   * shape  : [(32, 32, 3)]\n",
       "   * range  : [(0.0, 1.0)]\n",
       "\n",
       "**Target Summary**:\n",
       "   * shape  : [(10,)]\n",
       "   * range  : [(0.0, 1.0)]\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.dataset.split(50000)\n",
    "net.dataset.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine Input as Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(32, 32, 3)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.dataset.inputs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(10,)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.dataset.targets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=160x160 at 0x7F50DE7E1908>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image = array2image(net.dataset.inputs[0], scale=5.0)\n",
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>Dashboard</code>.</p>\n",
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       "Dashboard(children=(Accordion(children=(HBox(children=(VBox(children=(Select(description='Dataset:', index=1, options=('Test', 'Train'), rows=1, value='Train'), FloatSlider(value=1.0, continuous_update=False, description='Zoom', max=3.0, min=0.5), IntText(value=150, description='Horizontal space between banks:', style=DescriptionStyle(description_width='initial')), IntText(value=30, description='Vertical space between layers:', style=DescriptionStyle(description_width='initial')), HBox(children=(Checkbox(value=False, description='Show Targets', style=DescriptionStyle(description_width='initial')), Checkbox(value=False, description='Errors', style=DescriptionStyle(description_width='initial')))), Select(description='Features:', options=('', 'input', 'conv1', 'conv2', 'pool1', 'conv3', 'conv4', 'pool2'), rows=1, value=''), IntText(value=3, description='Feature columns:', style=DescriptionStyle(description_width='initial')), FloatText(value=2.0, description='Feature scale:', 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'Vega10', 'Vega10_r', 'Vega20', 'Vega20_r', 'Vega20b', 'Vega20b_r', 'Vega20c', 'Vega20c_r', 'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd', 'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r', 'brg', 'brg_r', 'bwr', 'bwr_r', 'cool', 'cool_r', 'coolwarm', 'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r', 'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r', 'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r', 'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray', 'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'inferno', 'inferno_r', 'jet', 'jet_r', 'magma', 'magma_r', 'nipy_spectral', 'nipy_spectral_r', 'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow', 'rainbow_r', 'seismic', 'seismic_r', 'spectral', 'spectral_r', 'spring', 'spring_r', 'summer', 'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r', 'terrain', 'terrain_r', 'viridis', 'viridis_r', 'winter', 'winter_r'), rows=1, value=''), HTML(value='<img src=\"data:image/gif;base64,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FloatText(value=-1.0, description='Leftmost color maps to:', style=DescriptionStyle(description_width='initial')), FloatText(value=1.0, description='Rightmost color maps to:', style=DescriptionStyle(description_width='initial')), IntText(value=0, description='Feature to show:', style=DescriptionStyle(description_width='initial'))), layout=Layout(width='100%')))),), selected_index=None, _titles={'0': 'CIRAR10'}), VBox(children=(HBox(children=(IntSlider(value=0, continuous_update=False, description='Dataset index', layout=Layout(width='100%'), max=9999), Label(value='of 10000', layout=Layout(width='100px'))), layout=Layout(height='40px')), HBox(children=(Button(icon='fast-backward', layout=Layout(width='100%'), style=ButtonStyle()), Button(icon='backward', layout=Layout(width='100%'), style=ButtonStyle()), IntText(value=0, layout=Layout(width='100%')), Button(icon='forward', layout=Layout(width='100%'), style=ButtonStyle()), Button(icon='fast-forward', layout=Layout(width='100%'), style=ButtonStyle()), Button(description='Play', icon='play', layout=Layout(width='100%'), style=ButtonStyle()), Button(icon='refresh', layout=Layout(width='25%'), style=ButtonStyle())), layout=Layout(height='50px', width='100%'))), layout=Layout(width='100%')), HTML(value='<p style=\"text-align:center\"></p>', layout=Layout(justify_content='center', overflow_x='auto', overflow_y='auto', width='95%')), Output()))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "require(['base/js/namespace'], function(Jupyter) {\n",
       "    Jupyter.notebook.kernel.comm_manager.register_target('conx_svg_control', function(comm, msg) {\n",
       "        comm.on_msg(function(msg) {\n",
       "            var data = msg[\"content\"][\"data\"];\n",
       "            var images = document.getElementsByClassName(data[\"class\"]);\n",
       "            for (var i = 0; i < images.length; i++) {\n",
       "                if (data[\"href\"]) {\n",
       "                    images[i].setAttributeNS(null, \"href\", data[\"href\"]);\n",
       "                }\n",
       "                if (data[\"src\"]) {\n",
       "                    images[i].setAttributeNS(null, \"src\", data[\"src\"]);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "});\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "require(['base/js/namespace'], function(Jupyter) {\n",
       "    Jupyter.notebook.kernel.comm_manager.register_target('conx_svg_control', function(comm, msg) {\n",
       "        comm.on_msg(function(msg) {\n",
       "            var data = msg[\"content\"][\"data\"];\n",
       "            var images = document.getElementsByClassName(data[\"class\"]);\n",
       "            for (var i = 0; i < images.length; i++) {\n",
       "                if (data[\"href\"]) {\n",
       "                    images[i].setAttributeNS(null, \"href\", data[\"href\"]);\n",
       "                }\n",
       "                if (data[\"src\"]) {\n",
       "                    images[i].setAttributeNS(null, \"src\", data[\"src\"]);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "});\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "require(['base/js/namespace'], function(Jupyter) {\n",
       "    Jupyter.notebook.kernel.comm_manager.register_target('conx_svg_control', function(comm, msg) {\n",
       "        comm.on_msg(function(msg) {\n",
       "            var data = msg[\"content\"][\"data\"];\n",
       "            var images = document.getElementsByClassName(data[\"class\"]);\n",
       "            for (var i = 0; i < images.length; i++) {\n",
       "                if (data[\"href\"]) {\n",
       "                    images[i].setAttributeNS(null, \"href\", data[\"href\"]);\n",
       "                }\n",
       "                if (data[\"src\"]) {\n",
       "                    images[i].setAttributeNS(null, \"src\", data[\"src\"]);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "    });\n",
       "});\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.dashboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0949893519282341,\n",
       " 0.09791158884763718,\n",
       " 0.10104776173830032,\n",
       " 0.10612580180168152,\n",
       " 0.09653763473033905,\n",
       " 0.10584577172994614,\n",
       " 0.0927908718585968,\n",
       " 0.10790029913187027,\n",
       " 0.0942075327038765,\n",
       " 0.10264338552951813]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.propagate(net.dataset.inputs[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let Keras Take over from here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.datasets import cifar10\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"288pt\" version=\"1.1\" viewBox=\"0 0 720 288\" width=\"720pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       " <defs>\n",
       "  <style type=\"text/css\">\n",
       "*{stroke-linecap:butt;stroke-linejoin:round;}\n",
       "  </style>\n",
       " </defs>\n",
       " <g id=\"figure_1\">\n",
       "  <g id=\"patch_1\">\n",
       "   <path d=\"M 0 288  L 720 288  L 720 0  L 0 0  z \" style=\"fill:#ffffff;\"/>\n",
       "  </g>\n",
       "  <g id=\"axes_1\">\n",
       "   <g id=\"patch_2\">\n",
       "    <path d=\"M 90 252  L 343.636364 252  L 343.636364 34.56  L 90 34.56  z \" style=\"fill:#ffffff;\"/>\n",
       "   </g>\n",
       "   <g id=\"matplotlib.axis_1\">\n",
       "    <g id=\"xtick_1\">\n",
       "     <g id=\"line2d_1\">\n",
       "      <defs>\n",
       "       <path d=\"M 0 0  L 0 3.5  \" id=\"m06dbafe400\" style=\"stroke:#000000;stroke-width:0.8;\"/>\n",
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     "text": [
      "========================================================================\n",
      "       |  Training |  Training |  Validate |  Validate \n",
      "Epochs |     Error |  Accuracy |     Error |  Accuracy \n",
      "------ | --------- | --------- | --------- | --------- \n",
      "#    1 |   2.14204 |   0.20052 |   1.97837 |   0.29421 \n"
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    "(x_train, y_train), (x_test, y_test) = net.dataset._split_data()"
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      "Using real-time data augmentation.\n",
      "Epoch 1/200\n",
      "313/312 [==============================] - 219s 700ms/step - loss: 1.6980 - acc: 0.3732 - val_loss: 1.5751 - val_acc: 0.4305\n",
      "Epoch 2/200\n",
      "313/312 [==============================] - 225s 718ms/step - loss: 1.6487 - acc: 0.3901 - val_loss: 1.5533 - val_acc: 0.4324\n",
      "Epoch 3/200\n",
      "311/312 [============================>.] - ETA: 0s - loss: 1.6029 - acc: 0.4136"
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     "ename": "KeyboardInterrupt",
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     "output_type": "error",
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      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-57-1ff22741f6bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     30\u001b[0m                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m                         \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m                         validation_data=(x_test, y_test))\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     85\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[1;32m     86\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 87\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\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[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   2142\u001b[0m                                 \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2143\u001b[0m                                 \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_sample_weights\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2144\u001b[0;31m                                 verbose=0)\n\u001b[0m\u001b[1;32m   2145\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2146\u001b[0m                             \u001b[0mval_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mval_outs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps)\u001b[0m\n\u001b[1;32m   1725\u001b[0m                                \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1726\u001b[0m                                \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1727\u001b[0;31m                                steps=steps)\n\u001b[0m\u001b[1;32m   1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1729\u001b[0m     def predict(self, x,\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_test_loop\u001b[0;34m(self, f, ins, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m   1368\u001b[0m                     \u001b[0mins_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_slice_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1370\u001b[0;31m                 \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1371\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1372\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/backend/theano_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   1221\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1222\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1223\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/theano/compile/function_module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    882\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    883\u001b[0m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 884\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moutput_subset\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    885\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_subset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_subset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    886\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/theano/ifelse.py\u001b[0m in \u001b[0;36mthunk\u001b[0;34m()\u001b[0m\n\u001b[1;32m    244\u001b[0m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 246\u001b[0;31m         \u001b[0;32mdef\u001b[0m \u001b[0mthunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    247\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcompute_map\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcond\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "if not data_augmentation:\n",
    "    print('Not using data augmentation.')\n",
    "    model.fit(x_train, y_train,\n",
    "              batch_size=batch_size,\n",
    "              epochs=epochs,\n",
    "              validation_data=(x_test, y_test),\n",
    "              shuffle=True)\n",
    "else:\n",
    "    print('Using real-time data augmentation.')\n",
    "    # This will do preprocessing and realtime data augmentation:\n",
    "    datagen = ImageDataGenerator(\n",
    "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "        samplewise_center=False,  # set each sample mean to 0\n",
    "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
    "        samplewise_std_normalization=False,  # divide each input by its std\n",
    "        zca_whitening=False,  # apply ZCA whitening\n",
    "        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
    "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
    "        horizontal_flip=True,  # randomly flip images\n",
    "        vertical_flip=False)  # randomly flip images\n",
    "\n",
    "    # Compute quantities required for feature-wise normalization\n",
    "    # (std, mean, and principal components if ZCA whitening is applied).\n",
    "    datagen.fit(x_train[0])\n",
    "\n",
    "    # Fit the model on the batches generated by datagen.flow().\n",
    "    model.fit_generator(datagen.flow(x_train[0], y_train[0],\n",
    "                                     batch_size=batch_size),\n",
    "                        steps_per_epoch=x_train[0].shape[0] // batch_size,\n",
    "                        epochs=epochs,\n",
    "                        validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Accuracy = 0.43\n",
      "Actual Label = [ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.] vs. Predicted Label = [ 0.07781513  0.11049026  0.0632093   0.02424265  0.10382453  0.04574762\n",
      "  0.03441789  0.42630658  0.02449505  0.08945103]\n",
      "Actual Label = [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.] vs. Predicted Label = [ 0.02727769  0.19264257  0.02373797  0.01587937  0.0090649   0.00988456\n",
      "  0.06715904  0.01502883  0.06517704  0.57414806]\n",
      "Actual Label = [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.] vs. Predicted Label = [ 0.05388747  0.72588551  0.00831101  0.00158087  0.00303652  0.00182245\n",
      "  0.00224504  0.00250599  0.10607026  0.0946549 ]\n",
      "Actual Label = [ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.] vs. Predicted Label = [ 0.01729291  0.19587232  0.07844362  0.10607366  0.05843127  0.09728301\n",
      "  0.20552364  0.0685864   0.01614707  0.15634608]\n",
      "Actual Label = [ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.] vs. Predicted Label = [ 0.04557527  0.47399527  0.04536494  0.04711241  0.02142892  0.03853729\n",
      "  0.02306257  0.03526938  0.04544082  0.22421315]\n",
      "Actual Label = [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.] vs. Predicted Label = [ 0.01227825  0.0398778   0.04749325  0.05364216  0.03648441  0.07180805\n",
      "  0.13807741  0.3137106   0.00615273  0.28047535]\n",
      "Actual Label = [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.] vs. Predicted Label = [ 0.00148747  0.00222605  0.03411757  0.24753405  0.05446577  0.16512007\n",
      "  0.44495872  0.04444287  0.00107558  0.00457185]\n",
      "Actual Label = [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.] vs. Predicted Label = [ 0.00741599  0.02212191  0.05528059  0.25858176  0.04116985  0.24418072\n",
      "  0.209802    0.12378619  0.00374054  0.03392046]\n",
      "Actual Label = [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.] vs. Predicted Label = [ 0.00456545  0.00249086  0.09983079  0.03194337  0.23732299  0.03509691\n",
      "  0.14823847  0.42950818  0.00175047  0.00925251]\n",
      "Actual Label = [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.] vs. Predicted Label = [ 0.01474431  0.00438144  0.08705295  0.17581116  0.1401801   0.10807402\n",
      "  0.40283573  0.04501324  0.00754052  0.01436656]\n",
      "Actual Label = [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.] vs. Predicted Label = [ 0.20857221  0.03858071  0.11660673  0.02005922  0.05146967  0.01053343\n",
      "  0.02061583  0.02458823  0.28485668  0.22411728]\n",
      "Actual Label = [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.] vs. Predicted Label = [ 0.04369825  0.00953077  0.28183886  0.03723752  0.39792955  0.04845857\n",
      "  0.05786296  0.08070038  0.03028672  0.01245641]\n",
      "Actual Label = [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.] vs. Predicted Label = [ 0.03174659  0.14564584  0.01506849  0.00770176  0.00636712  0.00667131\n",
      "  0.01012393  0.01783629  0.16324712  0.59559155]\n",
      "Actual Label = [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.] vs. Predicted Label = [ 0.01913743  0.42621526  0.00817715  0.00716725  0.0026362   0.00949341\n",
      "  0.01235665  0.01647761  0.10527103  0.39306799]\n",
      "Actual Label = [ 0.  0.  0.  0.  0.  1.  0.  0.  0.  0.] vs. Predicted Label = [ 0.28236881  0.04135361  0.1647729   0.03696084  0.15157856  0.03136211\n",
      "  0.02248296  0.04669645  0.17347305  0.04895073]\n",
      "Actual Label = [ 0.  0.  0.  0.  1.  0.  0.  0.  0.  0.] vs. Predicted Label = [ 0.05246869  0.45963278  0.00994109  0.00715726  0.0059837   0.0033786\n",
      "  0.01398534  0.00770203  0.06684003  0.37291047]\n",
      "Actual Label = [ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.] vs. Predicted Label = [ 0.04716396  0.04682264  0.11449952  0.13412473  0.13372231  0.10707654\n",
      "  0.17844412  0.09313755  0.0549487   0.09005994]\n",
      "Actual Label = [ 0.  0.  0.  1.  0.  0.  0.  0.  0.  0.] vs. Predicted Label = [ 0.03275985  0.08420437  0.18667556  0.0925509   0.07995293  0.05029331\n",
      "  0.30199629  0.04829691  0.01449488  0.108775  ]\n",
      "Actual Label = [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.] vs. Predicted Label = [ 0.00613854  0.00442893  0.12136693  0.05807019  0.19370349  0.09075771\n",
      "  0.36420116  0.14638041  0.00331027  0.0116424 ]\n",
      "Actual Label = [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.] vs. Predicted Label = [ 0.01641119  0.00571625  0.2858237   0.06443242  0.29150918  0.04093195\n",
      "  0.22316463  0.05377265  0.00830103  0.00993697]\n",
      "Actual Label = [ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.] vs. Predicted Label = [ 0.08868346  0.36420235  0.01048361  0.00159288  0.00550383  0.00113587\n",
      "  0.00297236  0.00778512  0.1336575   0.38398302]\n"
     ]
    }
   ],
   "source": [
    "# Evaluate model with test data set and share sample prediction results\n",
    "evaluation = model.evaluate_generator(datagen.flow(x_test[0], y_test[0],\n",
    "                                      batch_size=batch_size),\n",
    "                                      steps=x_test[0].shape[0] // batch_size)\n",
    "\n",
    "print('Model Accuracy = %.2f' % (evaluation[1]))\n",
    "\n",
    "predict_gen = model.predict_generator(datagen.flow(x_test[0], y_test[0],\n",
    "                                      batch_size=batch_size),\n",
    "                                      steps=x_test[0].shape[0] // batch_size)\n",
    "\n",
    "for predict_index, predicted_y in enumerate(predict_gen):\n",
    "    #actual_label = labels['label_names'][np.argmax(y_test[predict_index])]\n",
    "    #predicted_label = labels['label_names'][np.argmax(predicted_y)]\n",
    "    #print('Actual Label = %s vs. Predicted Label = %s' % (actual_label,\n",
    "    #                                                      predicted_label))\n",
    "    print('Actual Label = %s vs. Predicted Label = %s' % (y_test[0][predict_index],\n",
    "                                                          predicted_y))\n",
    "    if predict_index == num_predictions:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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