又菜又爱玩
639 lines
50 KiB
Plaintext
639 lines
50 KiB
Plaintext
{
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"cells": [
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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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"source": [
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.optim as optim\n",
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"import torch.nn.functional as F\n",
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"from torch.utils.data import Dataset, DataLoader"
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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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"device(type='cpu')"
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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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"device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
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"device"
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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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"\n"
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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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""
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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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"outputs": [],
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"source": [
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"batch_size = 256\n",
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"lr=1e-4\n",
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"epochs=20\n",
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"# 设置好超参数\n",
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"# 因为不是linux系统上跑，所以num_workers就不写，默认0\n"
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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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"下载Fashion-MNIST数据集"
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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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"source": [
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"from torchvision import transforms\n",
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"image_size = 28\n",
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"data_transforms = transforms.Compose([\n",
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" # transforms.ToPILImage(),\n",
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" transforms.Resize(image_size),\n",
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" transforms.ToTensor()\n",
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"])\n",
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"# 如果你使用的内置的dataset，那么ToPILImage这一句可以不要"
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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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"collapsed": true
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},
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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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"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
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"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./FashionMNIST\\raw\\train-images-idx3-ubyte.gz\n"
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]
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},
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{
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"data": {
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"text/plain": [
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" 0%| | 0/26421880 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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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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"Extracting ./FashionMNIST\\raw\\train-images-idx3-ubyte.gz to ./FashionMNIST\\raw\n",
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"\n",
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"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
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"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./FashionMNIST\\raw\\train-labels-idx1-ubyte.gz\n"
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]
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},
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{
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"data": {
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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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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"Extracting ./FashionMNIST\\raw\\train-labels-idx1-ubyte.gz to ./FashionMNIST\\raw\n",
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"\n",
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"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
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"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./FashionMNIST\\raw\\t10k-images-idx3-ubyte.gz\n"
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]
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},
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{
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"data": {
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},
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"metadata": {},
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"output_type": "display_data"
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},
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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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"Extracting ./FashionMNIST\\raw\\t10k-images-idx3-ubyte.gz to ./FashionMNIST\\raw\n",
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"\n",
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"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
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"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./FashionMNIST\\raw\\t10k-labels-idx1-ubyte.gz\n"
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]
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},
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{
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"data": {
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" 0%| | 0/5148 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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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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"Extracting ./FashionMNIST\\raw\\t10k-labels-idx1-ubyte.gz to ./FashionMNIST\\raw\n",
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"\n"
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]
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}
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],
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"source": [
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"from torchvision import datasets\n",
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"train_data=datasets.FashionMNIST(root='./', train=True, download=True, transform=data_transforms)\n",
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"test_data=datasets.FashionMNIST(root='./', train=True, download=True, transform=data_transforms)\n",
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" "
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_loader = DataLoader(train_data,\n",
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" batch_size = batch_size,\n",
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" shuffle = True,\n",
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" drop_last = True)\n",
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"\n",
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"test_loader = DataLoader(test_data,\n",
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" batch_size = batch_size,\n",
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" shuffle = False)"
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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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"这里，我们把上面的测试集与训练集构造好。"
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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": 7,
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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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"Dataset FashionMNIST\n",
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" Number of datapoints: 60000\n",
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" Root location: ./\n",
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" Split: Train\n",
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" StandardTransform\n",
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"Transform: Compose(\n",
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" Resize(size=28, interpolation=bilinear, max_size=None, antialias=None)\n",
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" ToTensor()\n",
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" )"
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]
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},
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"execution_count": 7,
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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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"train_data "
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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": 8,
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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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"torch.Size([256, 1, 28, 28]) torch.Size([256])\n"
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]
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}
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],
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"source": [
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"image, label = next(iter(train_loader))\n",
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"print(image.shape, label.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": 9,
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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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"<matplotlib.image.AxesImage at 0x1b1de03e6b0>"
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]
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},
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"execution_count": 9,
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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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"data": {
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"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"plt.imshow(image[0][0])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"打印随机一张图"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"下面开始搭建模型"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"class CNNnet(nn.Module):\n",
|
||
" def __init__(self):\n",
|
||
" super(CNNnet, self).__init__()\n",
|
||
" self.conv = nn.Sequential(\n",
|
||
" # 黑白图片的in_channels = 1\n",
|
||
" nn.Conv2d(1, 32, 5),\n",
|
||
" nn.ReLU(),\n",
|
||
" nn.MaxPool2d(2, stride = 2),\n",
|
||
" # 防止过拟合\n",
|
||
" nn.Dropout(0.3),\n",
|
||
" nn.Conv2d(32, 64, 5),\n",
|
||
" nn.ReLU(),\n",
|
||
" # 下面第一个2为kernel_size的大小\n",
|
||
" nn.MaxPool2d(2, stride = 2),\n",
|
||
" nn.Dropout(0.3)\n",
|
||
" )\n",
|
||
" self.fc = nn.Sequential(\n",
|
||
" nn.Linear(64 * 4 * 4, 512),\n",
|
||
" nn.ReLU(),\n",
|
||
" nn.Linear(512, 10)\n",
|
||
" )\n",
|
||
" def forward(self, x):\n",
|
||
" x = self.conv(x)\n",
|
||
" x = x.view(-1, 64 * 4 * 4)\n",
|
||
" x = self.fc(x)\n",
|
||
" # x = nn.functional.normalize(x)\n",
|
||
" return x\n",
|
||
"\n",
|
||
"model=CNNnet()\n",
|
||
"print(model)\n",
|
||
"\n",
|
||
"\n",
|
||
"import torchvision.models as models\n",
|
||
"from torchinfo import summary\n",
|
||
"summary(model)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"这么看我们这个CNN的结构，还是比较一目了然的。"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from tensorboardX import SummaryWriter\n",
|
||
"\n",
|
||
"writer = SummaryWriter('./runs')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"writer.add_graph(model,input_to_model = torch.rand(32, 1, 224, 224))\n",
|
||
"writer.close()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"尝试用Tensorboard交互性界面进一步可视化一下CNN的结构"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"model = CNNnet()\n",
|
||
"# 建立模型\n",
|
||
"criterion = nn.CrossEntropyLoss()\n",
|
||
"# 交叉熵为loss function\n",
|
||
"optimizer = optim.Adam(model.parameters(),lr = 0.001)\n",
|
||
"# Adam作为优化算法"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def train(epoch):\n",
|
||
" model.train()\n",
|
||
" running_loss = 0.0\n",
|
||
" for batch_idx, data in enumerate(train_loader, 0):\n",
|
||
" # 1.准备数据\n",
|
||
" inputs, target = data \n",
|
||
" inputs, target = inputs.to(device), target.to(device)\n",
|
||
" # 2.Forward\n",
|
||
" outputs = model(inputs)\n",
|
||
" loss = criterion(outputs, target)\n",
|
||
" # 3.Backward\n",
|
||
" optimizer.zero_grad()\n",
|
||
" loss.backward()\n",
|
||
" # 4.Update parameters\n",
|
||
" optimizer.step()\n",
|
||
" \n",
|
||
" running_loss += loss.item()\n",
|
||
" if batch_idx % 30 == 29:\n",
|
||
" print('[%d, %5d] loss: %.3f'%(\n",
|
||
" epoch + 1,\n",
|
||
" batch_idx + 1,\n",
|
||
" running_loss / 30))\n",
|
||
" running_loss = 0.0\n",
|
||
" # 每30 batch 输出一次\n",
|
||
" print('Finished epoch %d, loss: %.3f'%(epoch + 1, running_loss))\n",
|
||
"# 训练整个CNN"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def test():\n",
|
||
" correct = 0\n",
|
||
" total = 0\n",
|
||
" with torch.no_grad():\n",
|
||
" for data in test_loader:\n",
|
||
" images, labels = data\n",
|
||
" images, labels = images.to(device), labels.to(device)\n",
|
||
" outputs = model(images)\n",
|
||
" # 求出每一行(样本)的最大值的下标，dim=1即行的维度\n",
|
||
" # 返回最大值和最大值所在的下标\n",
|
||
" _, predicted = torch.max(outputs.data, dim = 1)\n",
|
||
" # label矩阵为N × 1\n",
|
||
" total += labels.size(0)\n",
|
||
" correct += (predicted == labels).sum().item()\n",
|
||
" print('accuracy on test set :%d %% ' % (100 * correct / total))\n",
|
||
" return correct / total\n",
|
||
"# 测试"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[1, 30] loss: 1.198\n",
|
||
"[1, 60] loss: 0.718\n",
|
||
"[1, 90] loss: 0.631\n",
|
||
"[1, 120] loss: 0.592\n",
|
||
"[1, 150] loss: 0.558\n",
|
||
"[1, 180] loss: 0.532\n",
|
||
"[1, 210] loss: 0.491\n",
|
||
"Finished epoch 1, loss: 11.346\n",
|
||
"accuracy on test set :82 % \n",
|
||
"[2, 30] loss: 0.458\n",
|
||
"[2, 60] loss: 0.442\n",
|
||
"[2, 90] loss: 0.435\n",
|
||
"[2, 120] loss: 0.417\n",
|
||
"[2, 150] loss: 0.410\n",
|
||
"[2, 180] loss: 0.384\n",
|
||
"[2, 210] loss: 0.399\n",
|
||
"Finished epoch 2, loss: 9.113\n",
|
||
"accuracy on test set :85 % \n",
|
||
"[3, 30] loss: 0.371\n",
|
||
"[3, 60] loss: 0.368\n",
|
||
"[3, 90] loss: 0.371\n",
|
||
"[3, 120] loss: 0.362\n",
|
||
"[3, 150] loss: 0.362\n",
|
||
"[3, 180] loss: 0.361\n",
|
||
"[3, 210] loss: 0.331\n",
|
||
"Finished epoch 3, loss: 8.068\n",
|
||
"accuracy on test set :87 % \n",
|
||
"[4, 30] loss: 0.317\n",
|
||
"[4, 60] loss: 0.331\n",
|
||
"[4, 90] loss: 0.324\n",
|
||
"[4, 120] loss: 0.331\n",
|
||
"[4, 150] loss: 0.320\n",
|
||
"[4, 180] loss: 0.306\n",
|
||
"[4, 210] loss: 0.335\n",
|
||
"Finished epoch 4, loss: 7.704\n",
|
||
"accuracy on test set :88 % \n",
|
||
"[5, 30] loss: 0.310\n",
|
||
"[5, 60] loss: 0.284\n",
|
||
"[5, 90] loss: 0.300\n",
|
||
"[5, 120] loss: 0.304\n",
|
||
"[5, 150] loss: 0.294\n",
|
||
"[5, 180] loss: 0.302\n",
|
||
"[5, 210] loss: 0.296\n",
|
||
"Finished epoch 5, loss: 7.099\n",
|
||
"accuracy on test set :89 % \n",
|
||
"[6, 30] loss: 0.284\n",
|
||
"[6, 60] loss: 0.274\n",
|
||
"[6, 90] loss: 0.286\n",
|
||
"[6, 120] loss: 0.299\n",
|
||
"[6, 150] loss: 0.282\n",
|
||
"[6, 180] loss: 0.289\n",
|
||
"[6, 210] loss: 0.268\n",
|
||
"Finished epoch 6, loss: 6.700\n",
|
||
"accuracy on test set :90 % \n",
|
||
"[7, 30] loss: 0.275\n",
|
||
"[7, 60] loss: 0.254\n",
|
||
"[7, 90] loss: 0.271\n",
|
||
"[7, 120] loss: 0.284\n",
|
||
"[7, 150] loss: 0.257\n",
|
||
"[7, 180] loss: 0.260\n",
|
||
"[7, 210] loss: 0.278\n",
|
||
"Finished epoch 7, loss: 6.579\n",
|
||
"accuracy on test set :90 % \n",
|
||
"[8, 30] loss: 0.259\n",
|
||
"[8, 60] loss: 0.265\n",
|
||
"[8, 90] loss: 0.241\n",
|
||
"[8, 120] loss: 0.262\n",
|
||
"[8, 150] loss: 0.261\n",
|
||
"[8, 180] loss: 0.252\n",
|
||
"[8, 210] loss: 0.249\n",
|
||
"Finished epoch 8, loss: 6.139\n",
|
||
"accuracy on test set :90 % \n",
|
||
"[9, 30] loss: 0.250\n",
|
||
"[9, 60] loss: 0.251\n",
|
||
"[9, 90] loss: 0.245\n",
|
||
"[9, 120] loss: 0.255\n",
|
||
"[9, 150] loss: 0.242\n",
|
||
"[9, 180] loss: 0.248\n",
|
||
"[9, 210] loss: 0.246\n",
|
||
"Finished epoch 9, loss: 5.539\n",
|
||
"accuracy on test set :91 % \n",
|
||
"[10, 30] loss: 0.227\n",
|
||
"[10, 60] loss: 0.241\n",
|
||
"[10, 90] loss: 0.255\n",
|
||
"[10, 120] loss: 0.237\n",
|
||
"[10, 150] loss: 0.237\n",
|
||
"[10, 180] loss: 0.236\n",
|
||
"[10, 210] loss: 0.234\n",
|
||
"Finished epoch 10, loss: 5.765\n",
|
||
"accuracy on test set :91 % \n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"if __name__ == '__main__':\n",
|
||
" epoch_list = []\n",
|
||
" acc_list = []\n",
|
||
" \n",
|
||
" for epoch in range(10):\n",
|
||
" train(epoch)\n",
|
||
" acc = test()\n",
|
||
" epoch_list.append(epoch)\n",
|
||
" acc_list.append(acc)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.plot(epoch_list, acc_list)\n",
|
||
"plt.ylabel('accuracy')\n",
|
||
"plt.xlabel('epoch')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"绘制出每次epoch的准确率的曲线"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"save_path = \"./Model.pkl\"\n",
|
||
"torch.save(model, save_path)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"最后保存模型"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.7"
|
||
},
|
||
"orig_nbformat": 4.0
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|