循环神经网络进行分类

"""
此代码是针对手写字体的训练：将图片按行依次输入网络中训练
RNN网络相对于LSTM网络很难收敛
"""
import torch
from torch import nn
from torch.autograd import Variable
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt

# 超参数
EPOCH = 1
BATCH_SIZE = 64
TIME_STEP = 28          # 图片的高度
INPUT_SIZE = 28         # 图片的宽度
LR = 0.01               
DOWNLOAD_MNIST = True

# 训练数据集
train_data = dsets.MNIST(
    root=\'./mnist/\',
    train=True,
    transform=transforms.ToTensor(),
    download=DOWNLOAD_MNIST,
)

print(train_data.train_data.size())     # (60000, 28, 28)
print(train_data.train_labels.size())   # (60000)

# 打印出第一张图片
plt.imshow(train_data.train_data[0].numpy(), cmap=\'gray\')
plt.title(\'%i\' % train_data.train_labels[0])
plt.show()

# 将训练数据集划分为多批
train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

# 测试数据集
test_data = dsets.MNIST(root=\'./mnist/\', train=False, transform=transforms.ToTensor())
test_x = Variable(test_data.test_data, volatile=True).type(torch.FloatTensor)[:2000]/255.
test_y = test_data.test_labels.numpy().squeeze()[:2000]

class RNN(nn.Module):
    def __init__(self):
        super(RNN, self).__init__()

        self.rnn = nn.LSTM(
            input_size=INPUT_SIZE,  # 每一个时间步长需要输入的元素个数
            hidden_size=64,         # 隐藏层单元数
            num_layers=1,           # rnn层数
            batch_first=True,       # 通常输入数据的维度为(batch, time_step, input_size)
                                    # batch_first确保batch是第一维
        )

        self.out = nn.Linear(64, 10)

    def forward(self, x):
        # x shape (batch, time_step, input_size)
        # r_out shape (batch, time_step, output_size)
        # h_n shape (n_layers, batch, hidden_size)
        # h_c shape (n_layers, batch, hidden_size)
        r_out, (h_n, h_c) = self.rnn(x, None) # None代表零初始化隐层状态
                                              # 其中r_out代表了每一个时刻对应的输出
        out = self.out(r_out[:, -1, :])  # 选择最后一个步长对应的输出
        return out

rnn = RNN()
print(rnn)

optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # 优化所有网络参数
loss_func = nn.CrossEntropyLoss()                       # 计算损失值

# 训练和测试
for epoch in range(EPOCH):
    for step, (x, y) in enumerate(train_loader):
        b_x = Variable(x.view(-1, 28, 28))
        b_y = Variable(y)

        output = rnn(b_x)
        loss = loss_func(output, b_y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if step % 50 == 0:
            test_output = rnn(test_x)
            pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
            accuracy = sum(pred_y == test_y) / float(test_y.size)
            print(\'Epoch: \', epoch, \'| train loss: %.4f\' % loss.data[0], \'| test accuracy: %.2f\' % accuracy)

# 打印测试数据的前10个进行预测
test_output = rnn(test_x[:10].view(-1, 28, 28))
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
print(pred_y, \'prediction number\')
print(test_y[:10], \'real number\')