wehub-resource-sync
171 lines
3.4 KiB
Plaintext
171 lines
3.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 5.2 填充和步幅"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.4.1\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"from torch import nn\n",
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"\n",
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"print(torch.__version__)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5.2.1 填充"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([8, 8])"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 定义一个函数来计算卷积层。它对输入和输出做相应的升维和降维\n",
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"def comp_conv2d(conv2d, X):\n",
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" # (1, 1)代表批量大小和通道数（“多输入通道和多输出通道”一节将介绍）均为1\n",
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" X = X.view((1, 1) + X.shape)\n",
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" Y = conv2d(X)\n",
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" return Y.view(Y.shape[2:]) # 排除不关心的前两维：批量和通道\n",
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"\n",
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"# 注意这里是两侧分别填充1行或列，所以在两侧一共填充2行或列\n",
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"conv2d = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3, padding=1)\n",
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"\n",
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"X = torch.rand(8, 8)\n",
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"comp_conv2d(conv2d, X).shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([8, 8])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 使用高为5、宽为3的卷积核。在高和宽两侧的填充数分别为2和1\n",
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"conv2d = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=(5, 3), padding=(2, 1))\n",
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"comp_conv2d(conv2d, X).shape"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5.2.2 步幅"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([4, 4])"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"conv2d = nn.Conv2d(1, 1, kernel_size=3, padding=1, stride=2)\n",
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"comp_conv2d(conv2d, X).shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([2, 2])"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"conv2d = nn.Conv2d(1, 1, kernel_size=(3, 5), padding=(0, 1), stride=(3, 4))\n",
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"comp_conv2d(conv2d, X).shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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