demo_notebooks/lecture_05/cifar10_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running this notebook without GPU will be really slow.   \n",
    "### If you have only CPU, make the networks smaller."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some code originates from: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py\n",
    "\n",
    "#### You may need to install pydot for the figure. You can do it via running the code below in a cell:\n",
    "!pip install pydot --user "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load some functions from keras:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import keras\n",
    "from keras.datasets import cifar10\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = 10\n",
    "\n",
    "save_dir = os.path.join(os.getcwd(), 'saved_models')\n",
    "model_name = 'keras_cifar10_trained_model.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (50000, 32, 32, 3)\n",
      "50000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at a few train images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u/MM28jwnUYWKR64sGDEMXRY691If7DrAti0OHbkdgPPnzrC+vkSzoY2EflDB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6FwgI1O5s1EDyvh+GZeI4yfn3kws33/ccFdjx9hOhVNri3KdpmtMiJQiIe4qkb8Uy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\nPYz0GJycdtXcV55CZonGOgs15k612dgwAcxDlwN0NkTFNnErHa6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zTzwFwOmZR/m1L/9/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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9d82c88470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cifar10classes = {0:'airplane', 1:'automobile', 2:'bird', 3:'cat', 4:'deer', 5:'dog', \n",
    "                  6:'frog', 7:'horse', 8:'ship', 9:'truck'}\n",
    "fig = plt.figure(1, (15., 4.))\n",
    "grid = ImageGrid(fig, 111, nrows_ncols=(3, 12), axes_pad=0.4)\n",
    "\n",
    "for i in range(36):\n",
    "    grid[i].imshow(x_train[i])\n",
    "    grid[i].set_title(cifar10classes[y_train[i][0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test  = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scaling pixel values to 0-1 region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), padding='same', input_shape=x_train.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(32, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(64, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(128, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(128, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dense(num_classes))\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "# initiate RMSprop optimizer\n",
    "opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=opt,\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine the parameters!\n",
    "\n",
    "1. layer $\\to (f\\cdot f\\cdot n^{0}_c + 1)\\cdot n^{1}_c = (3\\cdot 3\\cdot 3 + 1)\\cdot 32 = 896$ \n",
    "2. layer $\\to (f\\cdot f\\cdot n^{1}_c + 1)\\cdot n^{2}_c = (3\\cdot 3\\cdot 32 + 1)\\cdot 32 = 9248$   \n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 30, 30, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 30, 30, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 15, 15, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 15, 15, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 13, 13, 64)        36928     \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 4, 4, 128)         73856     \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 4, 4, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 2, 2, 128)         147584    \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 2, 2, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               262656    \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                5130      \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 554,794\n",
      "Trainable params: 554,794\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
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   "execution_count": 10,
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      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/30\n",
      "50000/50000 [==============================] - 43s - loss: 1.7893 - acc: 0.3436 - val_loss: 1.5395 - val_acc: 0.4242\n",
      "Epoch 2/30\n",
      "50000/50000 [==============================] - 12s - loss: 1.4369 - acc: 0.4781 - val_loss: 1.4694 - val_acc: 0.4796\n",
      "Epoch 3/30\n",
      "50000/50000 [==============================] - 12s - loss: 1.2693 - acc: 0.5462 - val_loss: 1.2472 - val_acc: 0.5527\n",
      "Epoch 4/30\n",
      "50000/50000 [==============================] - 12s - loss: 1.1507 - acc: 0.5914 - val_loss: 1.1751 - val_acc: 0.5801\n",
      "Epoch 5/30\n",
      "50000/50000 [==============================] - 12s - loss: 1.0556 - acc: 0.6276 - val_loss: 1.0582 - val_acc: 0.6247\n",
      "Epoch 6/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.9766 - acc: 0.6548 - val_loss: 1.0389 - val_acc: 0.6375\n",
      "Epoch 7/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.9119 - acc: 0.6796 - val_loss: 0.9916 - val_acc: 0.6489\n",
      "Epoch 8/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.8547 - acc: 0.7013 - val_loss: 0.8869 - val_acc: 0.6840\n",
      "Epoch 9/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.7992 - acc: 0.7205 - val_loss: 0.9768 - val_acc: 0.6613\n",
      "Epoch 10/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.7512 - acc: 0.7369 - val_loss: 0.9533 - val_acc: 0.6769\n",
      "Epoch 11/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.7048 - acc: 0.7522 - val_loss: 0.8420 - val_acc: 0.7095\n",
      "Epoch 12/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.6637 - acc: 0.7662 - val_loss: 0.8000 - val_acc: 0.7248\n",
      "Epoch 13/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.6256 - acc: 0.7817 - val_loss: 0.8137 - val_acc: 0.7263\n",
      "Epoch 14/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.5894 - acc: 0.7958 - val_loss: 0.7734 - val_acc: 0.7337\n",
      "Epoch 15/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.5555 - acc: 0.8061 - val_loss: 0.7967 - val_acc: 0.7333\n",
      "Epoch 16/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.5242 - acc: 0.8165 - val_loss: 0.8197 - val_acc: 0.7298\n",
      "Epoch 17/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.4904 - acc: 0.8277 - val_loss: 0.7435 - val_acc: 0.7520\n",
      "Epoch 18/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.4608 - acc: 0.8402 - val_loss: 0.7849 - val_acc: 0.7347\n",
      "Epoch 19/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.4302 - acc: 0.8511 - val_loss: 0.8050 - val_acc: 0.7368\n",
      "Epoch 20/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.4012 - acc: 0.8594 - val_loss: 0.7954 - val_acc: 0.7458\n",
      "Epoch 21/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.3732 - acc: 0.8700 - val_loss: 0.8381 - val_acc: 0.7503\n",
      "Epoch 22/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.3449 - acc: 0.8811 - val_loss: 0.7819 - val_acc: 0.7578\n",
      "Epoch 23/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.3183 - acc: 0.8887 - val_loss: 0.8321 - val_acc: 0.7520\n",
      "Epoch 24/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.2918 - acc: 0.8995 - val_loss: 0.8473 - val_acc: 0.7610\n",
      "Epoch 25/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.2670 - acc: 0.9073 - val_loss: 0.9739 - val_acc: 0.7363\n",
      "Epoch 26/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.2426 - acc: 0.9165 - val_loss: 0.9186 - val_acc: 0.7578\n",
      "Epoch 27/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.2193 - acc: 0.9251 - val_loss: 0.9286 - val_acc: 0.7560\n",
      "Epoch 28/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.1978 - acc: 0.9317 - val_loss: 1.0456 - val_acc: 0.7497\n",
      "Epoch 29/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.1768 - acc: 0.9390 - val_loss: 1.0583 - val_acc: 0.7535\n",
      "Epoch 30/30\n",
      "50000/50000 [==============================] - 12s - loss: 0.1576 - acc: 0.9456 - val_loss: 1.1165 - val_acc: 0.7515\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f9d7b577be0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train,\n",
    "              batch_size=32,\n",
    "              epochs=30,\n",
    "              validation_data=(x_test, y_test),\n",
    "              shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try a fully connected network too!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(2048, activation='relu', input_dim=32*32*3))\n",
    "model.add(Dense(2048, activation='relu'))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(128, activation='relu'))\n",
    "\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_3 (Dense)              (None, 2048)              6293504   \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 2048)              4196352   \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 512)               1049088   \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 128)               65664     \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 11,605,898\n",
      "Trainable params: 11,605,898\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"81.5\" y=\"-379.8\">dense_3_input: InputLayer</text>\n",
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     "execution_count": 14,
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   "source": [
    "SVG(model_to_dot(model).create(prog='dot', format='svg'))"
   ]
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.8842 - acc: 0.3147 - val_loss: 1.7690 - val_acc: 0.3547\n",
      "Epoch 2/30\n",
      "50000/50000 [==============================] - 26s - loss: 1.7035 - acc: 0.3870 - val_loss: 1.6788 - val_acc: 0.3888\n",
      "Epoch 3/30\n",
      "50000/50000 [==============================] - 26s - loss: 1.6279 - acc: 0.4131 - val_loss: 1.6046 - val_acc: 0.4306\n",
      "Epoch 4/30\n",
      "50000/50000 [==============================] - 26s - loss: 1.5705 - acc: 0.4334 - val_loss: 1.5805 - val_acc: 0.4372\n",
      "Epoch 5/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.5257 - acc: 0.4515 - val_loss: 1.5774 - val_acc: 0.4341\n",
      "Epoch 6/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.4859 - acc: 0.4644 - val_loss: 1.5242 - val_acc: 0.4637\n",
      "Epoch 7/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.4554 - acc: 0.4769 - val_loss: 1.4948 - val_acc: 0.4691\n",
      "Epoch 8/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.4266 - acc: 0.4853 - val_loss: 1.4920 - val_acc: 0.4678\n",
      "Epoch 9/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.3986 - acc: 0.4968 - val_loss: 1.5259 - val_acc: 0.4669\n",
      "Epoch 10/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.3735 - acc: 0.5066 - val_loss: 1.4773 - val_acc: 0.4749\n",
      "Epoch 11/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.3510 - acc: 0.5135 - val_loss: 1.4496 - val_acc: 0.4907\n",
      "Epoch 12/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.3218 - acc: 0.5238 - val_loss: 1.4759 - val_acc: 0.4885\n",
      "Epoch 13/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.2999 - acc: 0.5297 - val_loss: 1.4660 - val_acc: 0.4851\n",
      "Epoch 14/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.2804 - acc: 0.5367 - val_loss: 1.5458 - val_acc: 0.4628\n",
      "Epoch 15/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.2508 - acc: 0.5487 - val_loss: 1.5128 - val_acc: 0.4764\n",
      "Epoch 16/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.2336 - acc: 0.5549 - val_loss: 1.4622 - val_acc: 0.4931\n",
      "Epoch 17/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.2032 - acc: 0.5638 - val_loss: 1.5019 - val_acc: 0.4948\n",
      "Epoch 18/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.1802 - acc: 0.5730 - val_loss: 1.5531 - val_acc: 0.4676\n",
      "Epoch 19/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.1550 - acc: 0.5824 - val_loss: 1.5210 - val_acc: 0.4933\n",
      "Epoch 20/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.1365 - acc: 0.5870 - val_loss: 1.5314 - val_acc: 0.4900\n",
      "Epoch 21/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.1096 - acc: 0.5969 - val_loss: 1.5852 - val_acc: 0.4895\n",
      "Epoch 22/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.0835 - acc: 0.6063 - val_loss: 1.5928 - val_acc: 0.4907\n",
      "Epoch 23/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.0607 - acc: 0.6143 - val_loss: 1.5973 - val_acc: 0.4910\n",
      "Epoch 24/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.0356 - acc: 0.6228 - val_loss: 1.7483 - val_acc: 0.4760\n",
      "Epoch 25/30\n",
      "50000/50000 [==============================] - 25s - loss: 1.0062 - acc: 0.6330 - val_loss: 1.6753 - val_acc: 0.4800\n",
      "Epoch 26/30\n",
      "50000/50000 [==============================] - 25s - loss: 0.9880 - acc: 0.6422 - val_loss: 1.7094 - val_acc: 0.4798\n",
      "Epoch 27/30\n",
      "50000/50000 [==============================] - 25s - loss: 0.9642 - acc: 0.6499 - val_loss: 1.7746 - val_acc: 0.4744\n",
      "Epoch 28/30\n",
      "50000/50000 [==============================] - 25s - loss: 0.9319 - acc: 0.6588 - val_loss: 1.7837 - val_acc: 0.4787\n",
      "Epoch 29/30\n",
      "50000/50000 [==============================] - 26s - loss: 0.9220 - acc: 0.6642 - val_loss: 1.8092 - val_acc: 0.4787\n",
      "Epoch 30/30\n",
      "50000/50000 [==============================] - 26s - loss: 0.8929 - acc: 0.6751 - val_loss: 1.8239 - val_acc: 0.4770\n"
     ]
    },
    {
     "data": {
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       "<keras.callbacks.History at 0x7f9d7b63af60>"
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     },
     "execution_count": 15,
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   ],
   "source": [
    "model.fit(x_train.reshape(50000,32*32*3), y_train,\n",
    "              batch_size=32,\n",
    "              epochs=30,\n",
    "              validation_data=(x_test.reshape(10000,32*32*3), y_test),\n",
    "              shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison:\n",
    "\n",
    "\n",
    " type | validation accuracy | train accuracy | #parameters |\n",
    " - | - | - | - |\n",
    "fully connected | 48% | 67% | 11.6M |\n",
    "convolutional |   75% | 94% | 555K | "
   ]
  }
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