Pytorch构建RNN识别MNIST数据集

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
import torchvision
import torch.utils.data as Data

torch.manual_seed(1)    # reproducible

# Hyper Parameters
EPOCH = 1           # 训练整批数据多少次, 为了节约时间, 我们只训练一次
BATCH_SIZE = 64
TIME_STEP = 28      # rnn 时间步数 / 图片高度
INPUT_SIZE = 28     # rnn 每步输入值 / 图片每行像素
LR = 0.01           # learning rate
DOWNLOAD_MNIST = False  # 如果你已经下载好了mnist数据就写上 Fasle

# Mnist 手写数字
train_data = dsets.MNIST(root = 'data_sets/mnist', #选择数据的根目录
                           train = True, # 选择训练集
                           transform = transforms.ToTensor(), #转换成tensor变量
                           download = False) # 不从网络上download图片

test_data = dsets.MNIST(root = 'data_sets/mnist', #选择数据的根目录
                           train = True, # 选择训练集
                           transform = transforms.ToTensor(), #转换成tensor变量
                           download = False) # 不从网络上download图片

# 批训练 50samples, 1 channel, 28x28 (50, 1, 28, 28)
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

# 为了节约时间, 我们测试时只测试前2000个
# 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 RNN(nn.Module):
    def __init__(self):
        super(RNN, self).__init__()

        self.rnn = nn.LSTM(     # LSTM 效果要比 nn.RNN() 好多了
            input_size=28,      # 图片每行的数据像素点
            hidden_size=64,     # rnn hidden unit
            num_layers=1,       # 有几层 RNN layers
            batch_first=True,   # input & output 会是以 batch size 为第一维度的特征集 e.g. (batch, time_step, input_size)
        )

        self.out = nn.Linear(64, 10)    # 输出层

    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)   LSTM 有两个 hidden states, h_n 是分线, h_c 是主线
        # h_c shape (n_layers, batch, hidden_size)
        r_out, (h_n, h_c) = self.rnn(x, None)   # None 表示 hidden state 会用全0的 state

        # 选取最后一个时间点的 r_out 输出
        # 这里 r_out[:, -1, :] 的值也是 h_n 的值
        out = self.out(r_out[:, -1, :])
        return out

rnn = RNN()
print(rnn)

optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all 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):   # gives batch data
        b_x = Variable(x.view(-1, 28, 28))   # reshape x to (batch, time_step, input_size)
        b_y = Variable(y)   # batch y

        output = rnn(b_x)               # rnn output
        loss = loss_func(output, b_y)   # cross entropy loss
        optimizer.zero_grad()           # clear gradients for this training step
        loss.backward()                 # backpropagation, compute gradients
        optimizer.step()
        print(loss)