residual_cifar10.ipynb

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     "text": [
      "Using Theano backend.\n"
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   "source": [
    "from keras.models import Sequential, Model\n",
    "from keras.datasets import cifar10\n",
    "from keras.layers import Dense, Activation, Flatten, Input, merge, Convolution2D, MaxPooling2D\n",
    "from keras.utils import np_utils\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.utils.visualize_util import model_to_dot, plot\n",
    "import numpy as np\n",
    "from IPython.display import SVG\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use(\"ggplot\")\n",
    "np.random.seed(13)"
   ]
  },
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     "text": [
      "X_train shape: (50000, 3, 32, 32)\n",
      "50000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "batch_size = 256\n",
    "nb_classes = 10\n",
    "nb_epoch = 20\n",
    "nb_filter = 10\n",
    "\n",
    "img_rows, img_cols = 32, 32\n",
    "img_channels = 3\n",
    "\n",
    "(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')\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "X_train /= 255\n",
    "X_test /= 255\n",
    "\n",
    "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
    "Y_test = np_utils.to_categorical(y_test, nb_classes)"
   ]
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      "text/plain": [
       "<IPython.core.display.SVG object>"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Convolution2D(nb_filter, 3, 3, input_shape=(img_channels, img_rows, img_cols), border_mode=\"same\", activation=\"relu\"))\n",
    "model.add(Convolution2D(nb_filter, 3, 3, border_mode=\"same\", activation=\"relu\"))\n",
    "model.add(Convolution2D(nb_filter, 3, 3, border_mode=\"same\", activation=\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Convolution2D(nb_filter, 3, 3, border_mode=\"same\", activation=\"relu\"))\n",
    "model.add(Convolution2D(nb_filter, 3, 3, border_mode=\"same\", activation=\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512, activation=\"relu\"))\n",
    "model.add(Dense(nb_classes, activation=\"softmax\"))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adadelta',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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     },
     "execution_count": 4,
     "metadata": {},
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   "source": [
    "in_img = Input(shape=(img_channels, img_rows, img_cols))\n",
    "x = Convolution2D(nb_filter, 3, 3, border_mode=\"same\", activation=\"relu\")(in_img)\n",
    "for _ in range(2):\n",
    "    y = Convolution2D(nb_filter, 3, 3, border_mode=\"same\", activation=\"relu\")(x)\n",
    "    y = Convolution2D(nb_filter, 3, 3, border_mode=\"same\")(y)\n",
    "    x = merge([x, y], mode=\"sum\")\n",
    "    x = Activation(\"relu\")(x)\n",
    "    x = MaxPooling2D(pool_size=(2, 2))(x)\n",
    "\n",
    "x = Flatten()(x)\n",
    "x = Dense(512, activation=\"relu\")(x)\n",
    "x = Dense(nb_classes, activation=\"softmax\")(x)\n",
    "\n",
    "\n",
    "residual = Model(input=in_img, output=x)\n",
    "\n",
    "residual.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adadelta',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "SVG(model_to_dot(residual, show_shapes=True).create(prog='dot', format='svg'))"
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  {
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   "execution_count": null,
   "metadata": {
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   "outputs": [
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     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "50000/50000 [==============================] - 6s - loss: 1.9700 - acc: 0.2956 - val_loss: 1.8894 - val_acc: 0.3011\n",
      "Epoch 2/20\n",
      "50000/50000 [==============================] - 6s - loss: 1.5663 - acc: 0.4408 - val_loss: 1.4413 - val_acc: 0.4778\n",
      "Epoch 3/20\n",
      "50000/50000 [==============================] - 6s - loss: 1.3498 - acc: 0.5181 - val_loss: 1.3117 - val_acc: 0.5307\n",
      "Epoch 4/20\n",
      "50000/50000 [==============================] - 6s - loss: 1.1882 - acc: 0.5787 - val_loss: 1.5642 - val_acc: 0.4462\n",
      "Epoch 5/20\n",
      "50000/50000 [==============================] - 6s - loss: 1.0748 - acc: 0.6248 - val_loss: 1.1815 - val_acc: 0.5963\n",
      "Epoch 6/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.9711 - acc: 0.6597 - val_loss: 1.2062 - val_acc: 0.5857\n",
      "Epoch 7/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.8747 - acc: 0.6933 - val_loss: 1.1563 - val_acc: 0.6054\n",
      "Epoch 8/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.7713 - acc: 0.7292 - val_loss: 1.2170 - val_acc: 0.6138\n",
      "Epoch 9/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.6858 - acc: 0.7585 - val_loss: 1.1581 - val_acc: 0.6286\n",
      "Epoch 10/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.5845 - acc: 0.7964 - val_loss: 1.3494 - val_acc: 0.5967\n",
      "Epoch 11/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.5085 - acc: 0.8212 - val_loss: 1.4148 - val_acc: 0.6119\n",
      "Epoch 12/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.4111 - acc: 0.8577 - val_loss: 1.5797 - val_acc: 0.6001\n",
      "Epoch 13/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.3346 - acc: 0.8836 - val_loss: 1.7362 - val_acc: 0.6223\n",
      "Epoch 14/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.2697 - acc: 0.9065 - val_loss: 1.8886 - val_acc: 0.5741\n",
      "Epoch 15/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.2175 - acc: 0.9264 - val_loss: 1.9610 - val_acc: 0.6096\n",
      "Epoch 16/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.1658 - acc: 0.9443 - val_loss: 2.2704 - val_acc: 0.5949\n",
      "Epoch 17/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.1431 - acc: 0.9521 - val_loss: 2.2897 - val_acc: 0.6134\n",
      "Epoch 18/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.1258 - acc: 0.9601 - val_loss: 2.3318 - val_acc: 0.6165\n",
      "Epoch 19/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.1007 - acc: 0.9668 - val_loss: 2.4861 - val_acc: 0.6117\n",
      "Epoch 20/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.1019 - acc: 0.9696 - val_loss: 2.4492 - val_acc: 0.6220\n"
     ]
    }
   ],
   "source": [
    "cnn = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, validation_data=(X_test, Y_test))"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
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     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "50000/50000 [==============================] - 6s - loss: 1.9815 - acc: 0.3124 - val_loss: 1.6688 - val_acc: 0.4223\n",
      "Epoch 2/20\n",
      "50000/50000 [==============================] - 6s - loss: 1.4312 - acc: 0.4943 - val_loss: 1.6099 - val_acc: 0.4463\n",
      "Epoch 3/20\n",
      "50000/50000 [==============================] - 6s - loss: 1.2106 - acc: 0.5747 - val_loss: 1.2428 - val_acc: 0.5683\n",
      "Epoch 4/20\n",
      "50000/50000 [==============================] - 6s - loss: 1.0569 - acc: 0.6313 - val_loss: 1.2517 - val_acc: 0.5729\n",
      "Epoch 5/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.9351 - acc: 0.6731 - val_loss: 1.1756 - val_acc: 0.5904\n",
      "Epoch 6/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.8356 - acc: 0.7124 - val_loss: 1.2673 - val_acc: 0.5862\n",
      "Epoch 7/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.7249 - acc: 0.7475 - val_loss: 1.3000 - val_acc: 0.5748\n",
      "Epoch 8/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.6222 - acc: 0.7836 - val_loss: 1.2662 - val_acc: 0.6048\n",
      "Epoch 9/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.5320 - acc: 0.8147 - val_loss: 1.2739 - val_acc: 0.6152\n",
      "Epoch 10/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.4390 - acc: 0.8472 - val_loss: 1.3529 - val_acc: 0.6183\n",
      "Epoch 11/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.3395 - acc: 0.8813 - val_loss: 1.4030 - val_acc: 0.6517\n",
      "Epoch 12/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.2762 - acc: 0.9041 - val_loss: 1.5631 - val_acc: 0.6281\n",
      "Epoch 13/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.1983 - acc: 0.9326 - val_loss: 1.6813 - val_acc: 0.6403\n",
      "Epoch 14/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.1472 - acc: 0.9521 - val_loss: 1.7946 - val_acc: 0.6439\n",
      "Epoch 15/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.1139 - acc: 0.9644 - val_loss: 2.0615 - val_acc: 0.6440\n",
      "Epoch 16/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.0841 - acc: 0.9727 - val_loss: 2.3302 - val_acc: 0.6272\n",
      "Epoch 17/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.0981 - acc: 0.9707 - val_loss: 2.0693 - val_acc: 0.6587\n",
      "Epoch 18/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.0923 - acc: 0.9782 - val_loss: 2.1440 - val_acc: 0.6649\n",
      "Epoch 19/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.0870 - acc: 0.9789 - val_loss: 2.2455 - val_acc: 0.6543\n",
      "Epoch 20/20\n",
      "50000/50000 [==============================] - 6s - loss: 0.0581 - acc: 0.9862 - val_loss: 2.7374 - val_acc: 0.6404\n"
     ]
    }
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   "source": [
    "resi = residual.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, validation_data=(X_test, Y_test))"
   ]
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  {
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M0Wjksssu46GHHvKPn01NTWX9+vVkZGSwZMkSTCYTx44dY9OmTWRnZ/P000+T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\nOx6O9sfGoyVYPz0cGqlzl5b//W0lB0sREd3ByJE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zzDPw9PSEEIKjxLto69at+PLLL+Hr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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0d77fcc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GtmgfY8eO5ZtvvmHz5s1MmTKl3Yf5qKpKfn4+KSkpVFZWMnjwYCZMmCDDfoTDSeAKIS6L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\nIYTIAglcIYQQIgskcIUQQogs+D8077cYbFFulwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0d71e2a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(nb_epoch)\n",
    "plt.plot(x, cnn.history['acc'], label=\"cnn train\")\n",
    "plt.plot(x, cnn.history['val_acc'], label=\"cnn val\")\n",
    "plt.plot(x, resi.history['acc'], label=\"resi train\")\n",
    "plt.plot(x, resi.history['val_acc'], label=\"resi val\")\n",
    "plt.title(\"accuracy\")\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.show()\n",
    "\n",
    "plt.plot(x, cnn.history['loss'], label=\"cnn train\")\n",
    "plt.plot(x, cnn.history['val_loss'], label=\"cnn val\")\n",
    "plt.plot(x, resi.history['loss'], label=\"resi train\")\n",
    "plt.plot(x, resi.history['val_loss'], label=\"resi val\")\n",
    "plt.title(\"loss\")\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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