LeNet and LeNet5.ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.datasets import cifar10\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeNetBlock(keras.Model):\n",
    "    def __init__(self, nf, input_shape=None, padding='valid', pool:bool=True):\n",
    "        super(LeNetBlock, self).__init__()\n",
    "        self.is_pool = pool\n",
    "        if input_shape and input_shape is not None:\n",
    "            self.conv = layers.Conv2D(filters=nf, kernel_size=(5, 5), input_shape=input_shape, padding=padding)\n",
    "        else:\n",
    "            self.conv = layers.Conv2D(filters=nf, kernel_size=(5, 5), padding=padding)\n",
    "        if pool:\n",
    "            self.pool = layers.AveragePooling2D()\n",
    "        \n",
    "    def call(self, x):\n",
    "        x = self.conv(x)\n",
    "        if self.is_pool: x = self.pool(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LeNet(keras.Model):\n",
    "    def __init__(self, input_shape):\n",
    "        super(LeNet, self).__init__(name='LeNet')\n",
    "        \n",
    "        self.body = keras.Sequential([LeNetBlock(nf=6, input_shape=input_shape),\n",
    "                                     LeNetBlock(nf=16),\n",
    "                                     LeNetBlock(nf=120, pool=False)])\n",
    "        self.flatten = layers.Flatten()\n",
    "        self.dense = layers.Dense(84)\n",
    "        self.out = layers.Dense(10, activation='softmax')\n",
    "    def call(self, x):\n",
    "        x = self.body(x)\n",
    "        x = self.flatten(x)\n",
    "        x = self.dense(x)\n",
    "        x = self.out(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LeNet(input_shape=(32, 32, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.build((1, 32, 32, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"LeNet\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "sequential_1 (Sequential)    multiple                  50992     \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          multiple                  0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              multiple                  10164     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              multiple                  850       \n",
      "=================================================================\n",
      "Total params: 62,006\n",
      "Trainable params: 62,006\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data (cifar10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_valid, y_valid) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((50000, 32, 32, 3), (50000, 1), (10000, 32, 32, 3), (10000, 1))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape, y_train.shape, x_valid.shape, y_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train/255.0\n",
    "x_valid = x_valid/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = y_train.squeeze()\n",
    "y_valid = y_valid.squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.unique(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 50000 samples, validate on 10000 samples\n",
      "50000/50000 [==============================] - 101s 2ms/sample - loss: 1.8598 - accuracy: 0.3495 - val_loss: 1.7943 - val_accuracy: 0.3708\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1e782494128>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, batch_size=16, validation_data=(x_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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