pytorch下利用RNN实现mnist数据集的分类 简易代码

使用的模型为LSTM

parameters

EPOCH = 1
BATCH_SIZE = 64
TIME_STEP = 28 # rnn time step / image height
INPUT_SIZE = 28 # rnn input size / image width
LR = 0.01
DOWNLOAD_MNIST = False

RNN 模型搭建：

class RNN(nn.Module):
    def __init__(self):
        super(RNN,self).__init__()
        self.rnn = nn.LSTM(
            input_size=INPUT_SIZE,
            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,:])
        return out
rnn =RNN()

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

完整代码：

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

# Hyper Parameters
EPOCH = 1
BATCH_SIZE = 64
TIME_STEP = 28  # rnn time step / image height
INPUT_SIZE = 28 # rnn input size / image width
LR = 0.01
DOWNLOAD_MNIST = False

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

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

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,
            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,:])
        return out
rnn =RNN()

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

for epoch in range(EPOCH):
    for step,(x,y) in enumerate(train_loader):
        b_x = Variable(x.view(-1,28,28))     # reshape x to (batch,time_step,input_size)
        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) / test_y.size
            print('Epoch: ',epoch,'| train loss: %4.f' %loss.data[0],'| test accuracy: ',accuracy)
# print 10 predictions from test data
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')