"""
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')
