pytorch RNN循环神经网络

image.png

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

# Hyper parameters
EPOCH = 1 # train the training data n times, to save time, we just train 1 epoch
BATCH_SIZE = 64
TIME_STEP = 28 # rnn time step / image height
INPUT_SIZE = 28 # rnn input size / image width
LR = 0.01 # learning rate
DOWNLOAD_MNIST = False # set to True if haven't download the data

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

# training
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): # gives batch data
        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) / float(test_y.size)
            print('Epoch: ',epoch,
                  '| train loss: %.4f' % loss.data[0],
                  '| test accuracy: %.2f' % 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')