{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Residual Networks (ResNet)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2019-07-03T22:37:50.067206Z",
"start_time": "2019-07-03T22:37:46.213755Z"
}
},
"outputs": [],
"source": [
"import d2l\n",
"from mxnet import gluon, np, npx\n",
"from mxnet.gluon import nn\n",
"npx.set_np()\n",
"\n",
"train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size=256, resize=96)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Residual block"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2019-07-03T22:37:50.083212Z",
"start_time": "2019-07-03T22:37:50.070948Z"
},
"attributes": {
"classes": [],
"id": "",
"n": "1"
}
},
"outputs": [],
"source": [
"class Residual(nn.Block):\n",
" def __init__(self, num_channels, use_1x1conv=False, strides=1, **kwargs):\n",
" super(Residual, self).__init__(**kwargs)\n",
" self.conv1 = nn.Conv2D(num_channels, kernel_size=3, padding=1, strides=strides)\n",
" self.conv2 = nn.Conv2D(num_channels, kernel_size=3, padding=1)\n",
" self.conv3 = None\n",
" if use_1x1conv:\n",
" self.conv3 = nn.Conv2D(num_channels, kernel_size=1, strides=strides)\n",
" self.bn1 = nn.BatchNorm()\n",
" self.bn2 = nn.BatchNorm()\n",
"\n",
" def forward(self, X):\n",
" Y = npx.relu(self.bn1(self.conv1(X)))\n",
" Y = self.bn2(self.conv2(Y))\n",
" if self.conv3:\n",
" X = self.conv3(X)\n",
" return npx.relu(Y + X)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"A situation where the input and output are of the same shape."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2019-07-03T22:37:50.119721Z",
"start_time": "2019-07-03T22:37:50.085475Z"
},
"attributes": {
"classes": [],
"id": "",
"n": "2"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(4, 3, 6, 6)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"blk = Residual(3)\n",
"blk.initialize()\n",
"X = np.random.uniform(size=(4, 3, 6, 6))\n",
"blk(X).shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"Halve the output height and width while increasing the number of output channels"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2019-07-03T22:37:50.171045Z",
"start_time": "2019-07-03T22:37:50.122531Z"
},
"attributes": {
"classes": [],
"id": "",
"n": "3"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(4, 6, 3, 3)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"blk = Residual(6, use_1x1conv=True, strides=2)\n",
"blk.initialize()\n",
"blk(X).shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The ResNet block"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2019-07-03T22:37:50.187029Z",
"start_time": "2019-07-03T22:37:50.175512Z"
},
"attributes": {
"classes": [],
"id": "",
"n": "4"
}
},
"outputs": [],
"source": [
"def resnet_block(num_channels, num_residuals, first_block=False):\n",
" blk = nn.Sequential()\n",
" for i in range(num_residuals):\n",
" if i == 0 and not first_block:\n",
" blk.add(Residual(num_channels, use_1x1conv=True, strides=2))\n",
" else:\n",
" blk.add(Residual(num_channels))\n",
" return blk"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2019-07-03T22:37:50.213229Z",
"start_time": "2019-07-03T22:37:50.194294Z"
}
},
"outputs": [],
"source": [
"net = nn.Sequential()\n",
"net.add(nn.Conv2D(64, kernel_size=7, strides=2, padding=3),\n",
" nn.BatchNorm(), nn.Activation('relu'),\n",
" nn.MaxPool2D(pool_size=3, strides=2, padding=1),\n",
" resnet_block(64, 2, first_block=True),\n",
" resnet_block(128, 2),\n",
" resnet_block(256, 2),\n",
" resnet_block(512, 2),\n",
" nn.GlobalAvgPool2D(), \n",
" nn.Dense(10))"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Training"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2019-07-03T22:40:13.823984Z",
"start_time": "2019-07-03T22:37:50.216572Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loss 0.011, train acc 0.997, test acc 0.894\n",
"12520.9 exampes/sec on gpu(0)\n"
]
},
{
"data": {
"image/svg+xml": [
"\n",
"\n",
"\n",
"\n"
],
"text/plain": [
"