pytorch CNN卷积神经网络

image.png

import torch
import torch.utils.data as Data
from torch.autograd import Variable
import matplotlib.pyplot as plt
import torch.nn as nn
import torchvision

# Hyper parameters
EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 50
LR = 0.001 # learning rate
DOWNLOAD_MNIST = False

train_data = torchvision.datasets.MNIST(
    root='./mnist',
    train=True,
    transform=torchvision.transforms.ToTensor(), # (0,1) (0-255)
    download=DOWNLOAD_MNIST,
)

# plot one example
# print(train_data.train_data.size()) # (60000,28,28)
# print(train_data.train_labels.size()) # (60000)
# plt.imshow(train_data.train_data[0].numpy(),cmap='gray')
# plt.title('%i' % train_data.train_labels[0])
# plt.show()

train_loader = Data.DataLoader(
    dataset=train_data,
    batch_size=BATCH_SIZE,
    shuffle=True,
    num_workers=2,
)

test_data = torchvision.datasets.MNIST(root='./mnist',train=False)
test_x = Variable(torch.unsqueeze(test_data.test_data,dim=1),volatile=True).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]

class CNN(nn.Module):
    def __init__(self):
        super(CNN,self).__init__()
        # 卷积层
        self.conv1 = nn.Sequential(
            nn.Conv2d( # (1,28,28)
                in_channels=1,
                out_channels=16,
                kernel_size=5,
                stride=1, # 跳度
                padding=2, # if stride = 1, padding = (kernel_size-1)/2 = (5-1)/2
            ), # 卷积层 过滤器 -> (16,28,28)
            nn.ReLU(), # 神经网络 -> (16,28,28)
            nn.MaxPool2d(kernel_size=2), # -> (16,14,14)
        )
        self.conv2 = nn.Sequential( # (16,14,14)
            nn.Conv2d(16,32,5,1,2), # -> (32,14,14)
            nn.ReLU(), # -> (32,14,14)
            nn.MaxPool2d(2), # -> (32,7,7)
        )
        self.out = nn.Linear(32*7*7,10)

    def forward(self,x):
        x = self.conv1(x)
        x = self.conv2(x) # (batch,32,7,7)
        x = x.view(x.size(0),-1) # (batch,32*7*7)
        output = self.out(x)
        return output

cnn = CNN()
# print(cnn) # net architecture
optimizer = torch.optim.Adam(cnn.parameters(),lr=LR) # optimizer all cnn parameters
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted

# training and testing
for epoch in range(EPOCH):
    for step,(x,y) in enumerate(train_loader):
        b_x = Variable(x)
        b_y = Variable(y)

        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.squeeze()
            accuracy = sum(pred_y == test_y) / float(test_y.size(0))
            print('Epoch: ',epoch,'| train loss: %.4f' % loss.data[0],'| test accuracy: %.2f' % accuracy)

# print 10 predictions from test data
test_output = cnn(test_x[:10])
pred_y = torch.max(test_output,1)[1].data.numpy().squeeze()
print(pred_y,'prediction number')
print(test_y[:10].numpy(),'real number')