pytorch：使用CNN识别数字（分类）_torch.unsqueeze(test_data.test_data, dim=1).type(t

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.utils.data as Data
import torchvision
import matplotlib.pyplot as plt
 
EPOCH = 1  # 训练整批数据多少次
BATCH_SIZE = 50  # 每批多少
LR = 0.001  # 学习率
DOWNLOAD_MNIST = False  # 是否下载mnist数据，第一次True，下载好后改为False
 
# Mnist 手写数字
# 训练集
train_data = torchvision.datasets.MNIST(
    root='./mnist',  # 保存或者提取位置
    train=True,  # this is training data
    transform=torchvision.transforms.ToTensor(),
    download=DOWNLOAD_MNIST
)
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
# # 展示数据集
# print(train_data.train_data.size())
# print(train_data.train_labels.size())
# plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
# plt.title('%i' % train_data.train_labels[0])
# plt.show()
 
 
# 测试集
test_data = torchvision.datasets.MNIST(
    root='./mnist',  # 保存或者提取位置
    train=False,  # this is training data
)
test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[
         :2000] / 255.  # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
test_y = test_data.test_labels[:2000]
 
 
# 建立CNN网络
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential(  # 卷积层  (1, 28, 28)
            nn.Conv2d(  # 卷积层：过滤器
                in_channels=1,  # rgb:3, 灰度:1
                out_channels=16,  # 通道数
                kernel_size=5,  # 卷积核大小
                stride=1,  # 步长
                padding=2,  # 零填充 28=（28+2p-5）/1 + 1
            ),  # ->(16, 28, 28)
            nn.ReLU(),   # 激活函数
            nn.MaxPool2d(kernel_size=2),  # 池化层  ->(16, 14, 14)
        )
 
        self.conv2 = nn.Sequential(  # 卷积层  (16, 14, 14)
            nn.Conv2d(  # 卷积层：过滤器
                in_channels=16,  # rgb:3, 灰度:1
                out_channels=32,  # 通道数
                kernel_size=5,  # 卷积核大小
                stride=1,  # 步长
                padding=2,  # 零填充 28=（28+2p-5）/1 + 1
            ),  # ->(32, 14, 14)
            nn.ReLU(),  # 激活函数
            nn.MaxPool2d(kernel_size=2),  # 池化层 ->(32, 7, 7)
        )
 
        self.out = nn.Linear(32*7*7, 10)  # 0-9
 
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)  # (batch_size, 32, 7, 7)
        x = x.view(x.size(0), -1)  # 展平多维的卷积图成 (batch_size, 32 * 7 * 7)
        output = self.out(x)
        return output
        return x
 
 
cnn = CNN()
# print(cnn)
 
# 训练
optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)
loss_func = nn.CrossEntropyLoss()
 
for epoch in range(EPOCH):
    for step, (b_x, b_y) in enumerate(train_loader):
        output = cnn(b_x)
        loss = loss_func(output, b_y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
 
        if step % 50 == 0:
            test_output = cnn(test_x)
            pred_y = torch.max(test_output, 1)[1].data.numpy()
            accuracy = float((pred_y == test_y.data.numpy()).astype(int).sum()) / float(test_y.size(0))
            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)
 
test_output = cnn(test_x[:10])
pred_y = torch.max(test_output, 1)[1].data.numpy()
print(pred_y, 'prediction number')
print(test_y[:10].numpy(), 'real number')

运行结果；
 

Epoch:  0 | train loss: 2.2940 | test accuracy: 0.10
Epoch:  0 | train loss: 0.4439 | test accuracy: 0.82
Epoch:  0 | train loss: 0.3219 | test accuracy: 0.90
Epoch:  0 | train loss: 0.3983 | test accuracy: 0.92
Epoch:  0 | train loss: 0.3778 | test accuracy: 0.93
Epoch:  0 | train loss: 0.0972 | test accuracy: 0.94
Epoch:  0 | train loss: 0.1544 | test accuracy: 0.95
Epoch:  0 | train loss: 0.0502 | test accuracy: 0.95
Epoch:  0 | train loss: 0.1359 | test accuracy: 0.96
Epoch:  0 | train loss: 0.0192 | test accuracy: 0.97
Epoch:  0 | train loss: 0.2811 | test accuracy: 0.97
Epoch:  0 | train loss: 0.1555 | test accuracy: 0.96
Epoch:  0 | train loss: 0.0601 | test accuracy: 0.96
Epoch:  0 | train loss: 0.0407 | test accuracy: 0.97
Epoch:  0 | train loss: 0.2625 | test accuracy: 0.97
Epoch:  0 | train loss: 0.0915 | test accuracy: 0.98
Epoch:  0 | train loss: 0.1415 | test accuracy: 0.98
Epoch:  0 | train loss: 0.1100 | test accuracy: 0.98
Epoch:  0 | train loss: 0.0870 | test accuracy: 0.97
Epoch:  0 | train loss: 0.0553 | test accuracy: 0.98
Epoch:  0 | train loss: 0.0267 | test accuracy: 0.97
Epoch:  0 | train loss: 0.0187 | test accuracy: 0.97
Epoch:  0 | train loss: 0.1733 | test accuracy: 0.97
Epoch:  0 | train loss: 0.0438 | test accuracy: 0.98
[7 2 1 0 4 1 4 9 5 9] prediction number
[7 2 1 0 4 1 4 9 5 9] real number