import torch
import torch.nn as nn
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
EPOCH = 1
BATCH_SIZE = 64
LR = 0.01
DOWNLOAD_MNIST = False
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 = 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.input = nn.Linear(28,128)
self.rnn = nn.LSTM(
input_size=128,
hidden_size=64,
num_layers=2,
batch_first = True
)
self.out = nn.Linear(64, 10)
def forward(self, x):
x = x.view(-1,28)
x = self.input(x)
x = x.view(-1,28,128)
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):
print(b_x.size())
b_x = b_x.permute(0, 2, 3, 1)
output = rnn(b_x)
loss = loss_func(output, b_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()