Pytorch build convolutional neural network CNN RNN Recurrent Neural Networks and prediction data sets on the GPU MNIST

Convolution neural network CNN

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

# 下载MNIST数据集
# 若已有该数据集，需改为DOWNLOAD_MNIST = False
DOWNLOAD_MNIST = True
train_data = torchvision.datasets.MNIST(
    root='./mnist',
    train=True,
    transform=torchvision.transforms.ToTensor(),
    download = DOWNLOAD_MNIST
)
test_data = torchvision.datasets.MNIST(root='./mnist/',train=False)
with torch.no_grad():
    test_x = Variable(torch.unsqueeze(test_data.data, dim=1)).type(torch.FloatTensor).cuda()
test_y = test_data.targets[:10000].cuda()

# 配置批处理
train_loader = Data.DataLoader(
    dataset=train_data,
    batch_size=50,
    shuffle=True,
    num_workers=3
)

# CNN网络
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        # 第一个卷积层
        self.conv1 = nn.Sequential(
            nn.Conv2d(# 原图(1, 28, 28)
                in_channels=1,
                out_channels=16,
                kernel_size=5,
                stride=1,
                padding=2 # (kernel_size-1)/2
            ),# (1, 28, 28) -> (16, 28, 28)
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2) # (16, 28, 28) -> (16, 14, 14)
        )
        # 第二个卷积层
        self.conv2 = nn.Sequential(
            nn.Conv2d(16, 32, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )# (16, 14, 14) -> (32, 7, 7)
        self.out = nn.Linear(32 * 7 * 7, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0), -1)
        output = self.out(x)
        return output
cnn = CNN().cuda()

# 配置网络优化器
optimizer = torch.optim.Adam(cnn.parameters(), lr = 0.001)
loss_func = nn.CrossEntropyLoss()

plt.ion()
plt.show()
plt.figure()

steplist = []
losslist = []
accuracylist = []
for epoch in range(1):
    for step, (x, y) in enumerate(train_loader):
        b_x = Variable(x).cuda()
        b_y = Variable(y).cuda()
        output = cnn(b_x)
        loss = loss_func(output, b_y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if step % 10 == 0:
            test_output = cnn(test_x[:10000])
            pred_y = torch.max(test_output, 1)[1].cpu().data.numpy().squeeze()
            accuracy = sum(pred_y == test_y.cpu().numpy()) / test_y.size(0)
            print('Epoch:', epoch, ' | train loss:%.4f'%loss.item(), ' | test accuracy:%.2f'%accuracy)
            steplist.append(step)
            losslist.append(loss.item())
            accuracylist.append(accuracy)

            plt.subplot(121)
            plt.title('loss')
            plt.plot(steplist, losslist, 'r-', lw=1)
            plt.subplot(122)
            plt.title('accuracy')
            plt.plot(steplist, accuracylist, 'b-', lw=1)
            plt.pause(0.1)

plt.ioff()
plt.show()

result:

Epoch: 0  | train loss:1.4696  | test accuracy:0.69
Epoch: 0  | train loss:0.7815  | test accuracy:0.80
Epoch: 0  | train loss:0.5405  | test accuracy:0.79
...
Epoch: 0  | train loss:0.0603  | test accuracy:0.98
Epoch: 0  | train loss:0.0451  | test accuracy:0.98
Epoch: 0  | train loss:0.0594  | test accuracy:0.98

See, CNN MNIST prediction data set can reach 98% accuracy, which is a fairly good results

The entire prediction visualization process:

Recurrent neural network RNN

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

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

train_loader = Data.DataLoader(
    dataset=train_data,
    batch_size=64,
    shuffle=True,
)

test_data = torchvision.datasets.MNIST(root='./mnist/',train=False)
with torch.no_grad():
    test_x = (torch.unsqueeze(test_data.data, dim=1).type(torch.FloatTensor)/255.).cuda()
test_y = test_data.targets[:10000].cuda()

class RNN(nn.Module):
    def __init__(self):
        super(RNN, self).__init__()
        self.rnn = nn.LSTM(
            input_size=28,
            hidden_size=64,
            num_layers=1,
            batch_first=True
        )
        self.out = nn.Linear(64, 10)

    def forward(self, x):
        r_out, (h_n, h_c) = self.rnn(x, None)
        out = self.out(r_out[:, -1, :]) # (batch, time_step[-1], input_size)
        return out

rnn = RNN().cuda()

optimizer = torch.optim.Adam(rnn.parameters(), lr = 0.001)
loss_func = nn.CrossEntropyLoss()

plt.ion()
plt.show()
plt.figure()

steplist = []
losslist = []
accuracylist = []
for epoch in range(1):
    for step, (x, y) in enumerate(train_loader):
        b_x = Variable(x.view(-1, 28, 28)).cuda()
        b_y = Variable(y).cuda()
        output = rnn(b_x)
        loss = loss_func(output, b_y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if step % 10 == 0:
            test_output = rnn(test_x[:10000].view(-1, 28, 28))
            pred_y = torch.max(test_output, 1)[1].cpu().data.numpy().squeeze()
            accuracy = sum(pred_y == test_y.cpu().numpy()) / test_y.size(0)
            print('Epoch:', epoch, ' | train loss:%.4f'%loss.item(), ' | test accuracy:%.2f'%accuracy)
            steplist.append(step)
            losslist.append(loss.item())
            accuracylist.append(accuracy)

            plt.subplot(121)
            plt.title('loss')
            plt.plot(steplist, losslist, 'r-', lw=1)
            plt.subplot(122)
            plt.title('accuracy')
            plt.plot(steplist, accuracylist, 'b-', lw=1)
            plt.pause(0.1)

plt.ioff()
plt.show()

test_output = rnn(test_x[:10])
pred_y = torch.max(test_output, 1)[1].cpu().data.numpy().squeeze()
print(pred_y, 'prediction number')
print(test_y[:10].cpu().numpy(), 'real number')

result:

Epoch: 0  | train loss:2.2996  | test accuracy:0.13
Epoch: 0  | train loss:2.2977  | test accuracy:0.15
Epoch: 0  | train loss:2.2768  | test accuracy:0.21
...
Epoch: 0  | train loss:0.1875  | test accuracy:0.94
Epoch: 0  | train loss:0.2653  | test accuracy:0.94
Epoch: 0  | train loss:0.3055  | test accuracy:0.94