MNIST_RNN_PyTorch.py

import torch
from torch import nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt


EPOCH = 1
BATCH_SIZE = 64
TIME_STEP = 28
INPUT_SIZE = 28
LR = 0.01
DOWNLOAD_MNIST = True


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

print(train_data.train_data.size())    
print(train_data.train_labels.size())  

plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
plt.title('%i' % train_data.train_labels[0])
plt.show()

train_loader = torch.utils.data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)

test_data = dsets.MNIST(root='./mnist/', train=False, transform=transforms.ToTensor())

test_x = test_data.test_data.type(torch.FloatTensor)[:2000]/255.   
test_y = test_data.test_labels.numpy()[:2000] 


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

        self.rnn = nn.LSTM(   

            input_size=INPUT_S
            IZE,
            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()
print(rnn)


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

for epoch in range(EPOCH):
    for step, (b_x, b_y) in enumerate(train_loader):    
        b_x = b_x.view(-1, 28, 28)  

        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()
            accuracy = float((pred_y == test_y).astype(int).sum()) / float(test_y.size)
            print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy(), '| test accuracy: %.2f' % accuracy)


test_output = rnn(test_x[:100].view(-1, 28, 28))
pred_y = torch.max(test_output, 1)[1].data.numpy()
print(pred_y, 'prediction number')
print(test_y[:100], 'real number')