I split one image of MNIST into 5 segments, for example, 28*28 --> 28x5x5 (delete the left three columns). In this step, I used “fold” function to do this.
import torch
from torch import nn
from torch.autograd import Variable
import torchvision.datasets as dsets
import torch.utils.data as Data
import torchvision
torch.manual_seed(1)
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=torchvision.transforms.ToTensor(),
download=DOWNLOAD_MNIST)
test_data = torchvision.datasets.MNIST(root='./mnist', train=False)
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1), volatile=True).type(torch.FloatTensor) / 255
test_y = test_data.test_labels
class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.rnn = nn.LSTM(
input_size=100,
hidden_size=64,
num_layers=1,
batch_first=True,
)
self.lstm_skb = LSTMTCN(100, 1, 10, 10, 100, 3, 100)
self.out = nn.Linear(64, 10)
def forward(self, x):
x = self.lstm_skb(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, (x, y) in enumerate(train_loader):
b_x = Variable(x.view(-1, 28, 28))
b_y = Variable(y)
output = rnn(b_x)
loss = loss_func(output, b_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('Epoch: ', epoch, '| train loss:%.4f' % loss.item())