Keras CNN CIFAR10.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.datasets import cifar10\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPool2D, Dense\n",
    "from keras.layers import Dropout, Activation, Flatten\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf \n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(32)\n",
    "NUM_CLASSES = 10\n",
    "(X_train, y_train), (X_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "TYPE_MAP = {\n",
    "    0: 'Plain',\n",
    "    1: 'Auto',\n",
    "    2: 'Bird',\n",
    "    3: 'Cat',\n",
    "    4: 'Deer',\n",
    "    5: 'Dog',\n",
    "    6: 'Frog',\n",
    "    7: 'Horse',\n",
    "    8: 'Ship',\n",
    "    9: 'Truck'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.astype('float32') / 255.0\n",
    "X_test = X_test.astype('float32') / 255.0\n",
    "\n",
    "y_train = keras.utils.to_categorical(y_train, NUM_CLASSES)\n",
    "y_test = keras.utils.to_categorical(y_test, NUM_CLASSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: Frog\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f34f91642b0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "dataset_size = X_train.shape[0]\n",
    "\n",
    "idx = random.randint(0, dataset_size)\n",
    "#sample = random.choice(X_train)\n",
    "\n",
    "sample_img = X_train[idx]\n",
    "sample_label = np.argmax(y_train[idx])\n",
    "\n",
    "print(\"Image: %s\" % TYPE_MAP[sample_label])\n",
    "#print(sample_img)\n",
    "plt.imshow(sample_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6002154\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        2432      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 30, 30, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 15, 15, 64)        51264     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 15, 15, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 13, 13, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 6, 6, 64)          65600     \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2304)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2048)              4720640   \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 512)               1049088   \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               65664     \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                1290      \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 6,002,154\n",
      "Trainable params: 6,002,154\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "input_shape = X_train.shape[1:]\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# less and small filters would be better for this model\n",
    "\n",
    "model.add(Conv2D(32, (5, 5), padding='same', input_shape=input_shape))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Conv2D(32, (3, 3)))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(64, (5, 5), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3)))\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Conv2D(64, (4, 4), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(2048, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(NUM_CLASSES))\n",
    "# model.add(Dense(NUM_CLASSES, activation='softmax'))\n",
    "# normally it is better to use the code line above but because of \n",
    "# dependency problems in the current environment, I used the code line below\n",
    "model.add(Activation(tf.nn.softmax))\n",
    "\n",
    "print(model.count_params())\n",
    "print(model.summary())\n",
    "\n",
    "\"\"\"\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "             optimizer=keras.optimizers.Adadelta(),\n",
    "             metrics=['accuracy'])\n",
    "\"\"\"\n",
    "\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "             optimizer=keras.optimizers.Adam(),\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/12\n",
      "50000/50000 [==============================] - 1344s 27ms/step - loss: 1.7733 - acc: 0.3253 - val_loss: 1.4032 - val_acc: 0.4785\n",
      "Epoch 2/12\n",
      "50000/50000 [==============================] - 1343s 27ms/step - loss: 1.3503 - acc: 0.5058 - val_loss: 1.2064 - val_acc: 0.5693\n",
      "Epoch 3/12\n",
      "50000/50000 [==============================] - 1341s 27ms/step - loss: 1.1741 - acc: 0.5769 - val_loss: 1.0389 - val_acc: 0.6332\n",
      "Epoch 4/12\n",
      "50000/50000 [==============================] - 1341s 27ms/step - loss: 1.0339 - acc: 0.6331 - val_loss: 0.9187 - val_acc: 0.6757\n",
      "Epoch 5/12\n",
      "50000/50000 [==============================] - 1341s 27ms/step - loss: 0.9412 - acc: 0.6680 - val_loss: 0.8950 - val_acc: 0.6847\n",
      "Epoch 6/12\n",
      "50000/50000 [==============================] - 1342s 27ms/step - loss: 0.8782 - acc: 0.6933 - val_loss: 0.8115 - val_acc: 0.7138\n",
      "Epoch 7/12\n",
      "50000/50000 [==============================] - 1342s 27ms/step - loss: 0.8310 - acc: 0.7070 - val_loss: 0.7813 - val_acc: 0.7305\n",
      "Epoch 8/12\n",
      "50000/50000 [==============================] - 1343s 27ms/step - loss: 0.7886 - acc: 0.7223 - val_loss: 0.7494 - val_acc: 0.7434\n",
      "Epoch 9/12\n",
      "50000/50000 [==============================] - 1342s 27ms/step - loss: 0.7414 - acc: 0.7419 - val_loss: 0.7602 - val_acc: 0.7405\n",
      "Epoch 10/12\n",
      "50000/50000 [==============================] - 1343s 27ms/step - loss: 0.7231 - acc: 0.7465 - val_loss: 0.7082 - val_acc: 0.7556\n",
      "Epoch 11/12\n",
      "50000/50000 [==============================] - 1342s 27ms/step - loss: 0.6913 - acc: 0.7571 - val_loss: 0.6915 - val_acc: 0.7605\n",
      "Epoch 12/12\n",
      "50000/50000 [==============================] - 1342s 27ms/step - loss: 0.6630 - acc: 0.7697 - val_loss: 0.6994 - val_acc: 0.7566\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f34f7a7a588>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, batch_size=128, epochs=12, verbose=1, \n",
    "          validation_data=(X_test, y_test), shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 86s 9ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6993805432796478, 0.7566]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: Frog\n",
      "Probability of being Plain: 0.00%\n",
      "Probability of being Auto: 0.00%\n",
      "Probability of being Bird: 0.18%\n",
      "Probability of being Cat: 0.37%\n",
      "Probability of being Deer: 0.00%\n",
      "Probability of being Dog: 0.01%\n",
      "Probability of being Frog: 99.44%\n",
      "Probability of being Horse: 0.00%\n",
      "Probability of being Ship: 0.00%\n",
      "Probability of being Truck: 0.00%\n"
     ]
    },
    {
     "data": {
      "image/png": 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allduobaFPUvUVjN+3pVSeIILfAhIoiUAII9IW6urkUy7tXDGYnOOS31veQu/LudeCWdUAkCW8xZVzXr4xHPj2XmtOvdjMOTZeblzP8z4PBYK4XHDIb+Hc3LODn6cK9k2+N39o4HNX5n4CEKImxJ9w0+IRFHwC5EoCn4hEkXBL0SiKPiFSJSpFvAcDnOskmKLr67yDL1qLyyvzN/Cs682u7w4Zr/Di1nuIcUUAeCed94e3H7b4b10zOFbeJHLcxc2qO0Xf3Wa2m47wL9N3SRZc8Mel4A2u1yiajVmqG3PEv/S1sZm+HquLPPrUjKeaXdpmc9VP+d+WDFsqzfChU4BoNPj92KW8bmqV7meWq3y52yBpO+VK1wWHZDCpHYVeX168guRKAp+IRJFwS9Eoij4hUgUBb8QiaLgFyJRpiv1ZTlWLoYLQvYHvHijk4yoQqQ3mg25LXNu2+jwQov9flimLBT5/u666zZqq9Z5NuBzP+dSXyfS4++XD4czD2ukdx4AeKS4JwpcErOcZ7jNzYavZz/jMlqsnx3A/Zidn6W2S6+GM+OKpTIdk/d5Nl13yDMZqznPBqzXw0VoR5D+igMuK5ZL4axEs8mf53ryC5EoCn4hEkXBL0SiKPiFSBQFvxCJMt3V/tyx2Q6vpHqJJ2cUquGVWcv5iq1FVvQLxci4SGJEtRL2Mc95QkqlzFWM/Xv5KnXeP0BtP/tL3h4MHk4ueduRw3xMhJW1NWorGE+2YavbxUibLGcFHgGUIorEbJNfz85m2I8GaQEHABcvvkJtKPCQiSkIhUgRxQGpN1ktxxQCPleToie/EImi4BciURT8QiSKgl+IRFHwC5EoCn4hEmWSdl2HAfwJgAMAcgDH3f2LZrYI4BsAjmDUsut33H0ltq/cHZ1+WOrpZrzG3GAYttlZLkO15rjE1pptUttwyBNPWjNhaa4U+RNaqfG2UAYue83UuWyU5fyA9WK47luZ1PYDgH4kQcqNS0rtWBLUIHxu/QGf39jtuLLBk23m94XbuQHA/Ew42aZY5ud1aZMnTtUqc9TWmuVydYP4AQC1WrhOYrXK9zcYsLmfXAKc5MmfAfhDd387gPcA+H0zuwvAgwAec/c7ATw2/l0I8SZh2+B393Pu/uT45w0AJwEcAnA/gIfHL3sYwIdulJNCiOvPVX3mN7MjAN4N4AkA+939HDD6AwGAv/cSQtx0TBz8ZtYC8C0An3T3WNWFK8cdM7MTZnai2+Of24QQ02Wi4DezMkaB/1V3//Z483kzOzi2HwRwITTW3Y+7+1F3P1qLLDoJIabLtsFvZgbgKwBOuvvnLzM9AuCB8c8PAPju9XdPCHGjmCSr770APgbgGTN7arztUwA+C+CbZvZxAC8C+O3tdmQAWCm5WNYWq1e21eZSWbPFT61S5BlWa+02ta2shrPY9i1x+ceH/KNOm7QuAwAQSRQAjhwM1+kDgAZp8TTX5OeMIpc+O13+fDj9Iq9ByGTMgvHr3JrjWY4X1vh16T33ErXt3x+eq9OnztIx/YjsvDDLbVsR6dOK/H4slMLXpkey/QAgI5Jp7pNLfdsGv7v/AKB5rr858ZGEEDcV+oafEImi4BciURT8QiSKgl+IRFHwC5EoUy3gWalUcOTILUFbMfIFoOXlS2GD88y3Ck+mQ6XGM/4a2Ty15b2wvLJ8cZWOiXVP6nW4bFQYcmmuGCns2GiGZbsBuFS2tRFuoQYAl1b5udXmFqgtI225KhV+XvVZnvn2Swt7qO3kz3lB07MXw4mmhw7xAqmVSLFNzyNyXmRcpLYnOr3wPmOiXbFAbqxI57Ur0ZNfiERR8AuRKAp+IRJFwS9Eoij4hUgUBb8QiTJVqa9QLKLRDEtpC3u5zGOVcHZTucQlrzwilXnkb163z2uQljw8XcNI5ptHetOVIsUxq3WuVVbK/LK122Fp7sKrXLI7dZr3ptu3j2fave2u26itR2TReoNrXqVItuXSAs+c3HfgELWdPX8xuN0zPveFVkSCdX6t33o7z7acmeH36lY3fH9nQ571Wa2G5Wq/iqw+PfmFSBQFvxCJouAXIlEU/EIkioJfiESZ6mp/Nshw4Vx49bXd46vi1Xq4nVG/z5Msen3eyisb8NXcjUirpr3ze4PbZ2p8Jbe3xZNm2ls9assiSkC3E64lCACnXzod3N7JeZusO375MLXdditPgBkO+TUrkIymOlmlBoBWiys+9QYfN1vn818qh691Z3NIx6yv8usyP8+PtbjIk8Iy5/eckbnKcz6m2wvfp7ExV6InvxCJouAXIlEU/EIkioJfiERR8AuRKAp+IRJlW6nPzA4D+BMABwDkAI67+xfN7DMAfhfAa9rdp9z9e/F9AaSbFE69wOuwzbZqwe2HDvM2U5lxuWZ5nSfvVMu8Ll29EZZ5tnpcetvoc1unx+Wm4oBLfcNIwsfsnrBcduseLkM1Grx+YqHE5bx+pKVYgyQm1avhawnwFlQAsN7mtjJJIgKA/lY4aabgkaQw54XwWI1EAOiSWnwA0JjhiUnDPHyvOvh1yUgbuEg3tDcwic6fAfhDd3/SzGYA/NjMHh3bvuDu/37ywwkhbhYm6dV3DsC58c8bZnYSAM+hFEK8Kbiqz/xmdgTAuwE8Md70CTN72sweMjP+flkIcdMxcfCbWQvAtwB80t3XAXwJwB0A7sboncHnyLhjZnbCzE5sdSItqYUQU2Wi4DezMkaB/1V3/zYAuPt5dx+6ew7gywDuDY119+PuftTdjzbqfLFHCDFdtg1+MzMAXwFw0t0/f9n2y2sWfRjAs9ffPSHEjWKS1f73AvgYgGfM7Knxtk8B+KiZ3Y1RV6FTAH5vux0ZAFamrV7jrlgx/Deqs8allXKdyyTlSqRdV4P7UWuGne9HyqbtbfE2U7Uyz2LzSJ8vK3KJMOuHP1r1ezzbq9/jsuggcnKLi4vUNjsTrv2XRTIB220ui5aLkfvDeO2/jY3wuW21+UfQ3oD7OMgjtSGNy4CDiIw5GIZtgz5vsVZmrcGuoobfJKv9P0C4A1hU0xdC3NzoG35CJIqCX4hEUfALkSgKfiESRcEvRKJMuV1XAc2ZsLx1/uI5Ps7CMlW3zOU8RLLHGk3egmo45AU8+6vhtlbtiFTmWKK2WinytzfS5qvV4K28slJYxhyUudRUmucZZysr69RWqfL5dwv7nzv3I1aksxYpktrtcOnTCuFbvFTlUt/qxiVqu7gczqYDgGqNt+saRs47z0kbuCGX7YokJq4GPfmFSBQFvxCJouAXIlEU/EIkioJfiERR8AuRKFOV+ooFw0w9fMgiS/cDkOVhKWepwuWwfbORIoytSP+5dS7J1DzcZ7D8Mpcpz63xzKx8/63UNhepfTCI9DUsVcKSWF7kslEjIrFlGZe2lpeXqa1SDV/PWi0iz0YS0oZczYM532epHL4PFvdxeXNhD8/Oy3MuEUYSMdFsRnxEWP5u9yMxwXr/sWy/0EsnfqUQ4m8VCn4hEkXBL0SiKPiFSBQFvxCJouAXIlGmm9WHHA0LSyW3H+K95Orl8JiFJpfzTr8UzsADgHOvPEdtB5xLW0utsB+b53kWWCfjctjSgVuobW090uOvy31sNsNZibOzXNqKZefVG2Vq6/W47NVph30sk6xDACiX+e2Y55ECmP1IxiJxP8u47/X6DLW1Ij332ps8I3R1hdtq1bDGmYNLulsdkjU5nDzbT09+IRJFwS9Eoij4hUgUBb8QiaLgFyJRtl3tN7MagMcBVMev/1N3/7SZ3Qbg6wAWATwJ4GPukaVyAOWi4cB8OPHgLfPclfYgnBwziCxsdgaRVeUKH9jJeQuws4Pw0nG/yFfEMeAr6fMt3tW8XuLzESnthmIhfAkGkcyYfje2Ws5r5w2HmxE/wv53u5F6hzm/LoXIcyrL+LiZ2XC9wy65pwAAVT73HkkiynO+oj8c8tBg5fgqVT73FZK/UzCugL3htRO8pgfgN9z9XRi1477PzN4D4I8AfMHd7wSwAuDjEx9VCLHrbBv8PuK1P/Hl8T8H8BsA/nS8/WEAH7ohHgohbggTfeY3s+K4Q+8FAI8C+AWAVfe/ri99BsChG+OiEOJGMFHwu/vQ3e8GcAuAewG8PfSy0FgzO2ZmJ8zsxOom/zwthJguV7Xa7+6rAP43gPcAmDez11ZGbgFwlow57u5H3f3ofIsvYAghpsu2wW9me81sfvxzHcD7AZwE8GcA/tH4ZQ8A+O6NclIIcf2ZJLHnIICHzayI0R+Lb7r7/zCznwH4upn9GwD/F8BXttvR0HOsdYkc4rz22F+dCY958QKXr3zAbYstLg216ot8n5WwfJjN7qdj7BKXwzLnPhZLXFLqtnlSihfD+8wi0lbW4zLUMFJYr1Tmz44BSfoplvi7v795I/lGulv8I2M50pqtRCTT2RqXWdcjxxp0I3MfuZ55zuexbGFbtcbPq0jqWlph8jfz2wa/uz8N4N2B7S9g9PlfCPEmRN/wEyJRFPxCJIqCX4hEUfALkSgKfiESxdwjKWLX+2BmFwGcHv+6BODVqR2cIz9ej/x4PW82P97q7nsn2eFUg/91BzY74e5Hd+Xg8kN+yA+97RciVRT8QiTKbgb/8V089uXIj9cjP17P31o/du0zvxBid9HbfiESZVeC38zuM7O/NLPnzezB3fBh7McpM3vGzJ4ysxNTPO5DZnbBzJ69bNuimT1qZs+N/+dpZzfWj8+Y2cvjOXnKzD44BT8Om9mfmdlJM/upmf3BePtU5yTix1TnxMxqZvYXZvaTsR//erz9NjN7Yjwf3zAznvo5Ce4+1X8AihiVAbsdQAXATwDcNW0/xr6cArC0C8f9NQD3AHj2sm3/DsCD458fBPBHu+THZwD8synPx0EA94x/ngHw/wDcNe05ifgx1TkBYABa45/LAJ7AqIDONwF8ZLz9PwH4pzs5zm48+e8F8Ly7v+CjUt9fB3D/Lvixa7j74wCu7O55P0aFUIEpFUQlfkwddz/n7k+Of97AqFjMIUx5TiJ+TBUfccOL5u5G8B8C8NJlv+9m8U8H8H0z+7GZHdslH15jv7ufA0Y3IYB9u+jLJ8zs6fHHghv+8eNyzOwIRvUjnsAuzskVfgBTnpNpFM3djeAPdRXYLcnhve5+D4B/COD3zezXdsmPm4kvAbgDox4N5wB8bloHNrMWgG8B+KS7r0/ruBP4MfU58R0UzZ2U3Qj+MwAOX/Y7Lf55o3H3s+P/LwD4Dna3MtF5MzsIAOP/L+yGE+5+fnzj5QC+jCnNiZmVMQq4r7r7t8ebpz4nIT92a07Gx77qormTshvB/yMAd45XLisAPgLgkWk7YWZNM5t57WcAHwDwbHzUDeURjAqhArtYEPW1YBvzYUxhTszMMKoBedLdP3+ZaapzwvyY9pxMrWjutFYwr1jN/CBGK6m/APAvdsmH2zFSGn4C4KfT9APA1zB6+zjA6J3QxwHsAfAYgOfG/y/ukh//BcAzAJ7GKPgOTsGPX8HoLezTAJ4a//vgtOck4sdU5wTAOzEqivs0Rn9o/tVl9+xfAHgewH8DUN3JcfQNPyESRd/wEyJRFPxCJIqCX4hEUfALkSgKfiESRcEvRKIo+IVIFAW/EIny/wEAmyqYskckwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idx = random.randint(0, X_test.shape[0])\n",
    "\n",
    "sample_img = X_test[idx]\n",
    "sample_label = np.argmax(y_test[idx])\n",
    "\n",
    "print(\"Image: %s\" % TYPE_MAP[sample_label])\n",
    "plt.imshow(sample_img)\n",
    "\n",
    "sample_img = sample_img.reshape(1, 32, 32, 3)\n",
    "\n",
    "for i, v in enumerate(model.predict(sample_img)[0]):\n",
    "  print(\"Probability of being %s: %.2f%%\" % (TYPE_MAP[i], v * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "for layer in model.layers:\n",
    "  print(\"Layer : \\n\"), layer.get_weights()\n",
    "\"\"\"\n",
    "weights = model.layers[0].get_weights()[0]\n",
    "biases = model.layers[0].get_weights()[1]\n",
    "\n",
    "#print(biases)\n",
    "\n",
    "# to save weights and layers to use later\n",
    "model.save_weights('cifar10-cnn.h5')\n",
    "\n",
    "# to use the saved training, create the same layes and model.compile, then model load\n",
    "model.load_weights('cifar10-cnn.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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