# recurrent neural network
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # cuda or cpu
# Hyper-parameters
sequence_length = 28
input_size = 28
hidden_size = 128
num_layers = 2
num_classes = 10
batch_size = 100
num_epochs = 2
learning_rate = 0.01
# MNIST dataset
train_dataset = torchvision.datasets.MNIST(
root='data',
train=True,
transform=transforms.ToTensor(),
download=True
)
test_dataset = torchvision.datasets.MNIST(
root='data',
train=False,
transform=transforms.ToTensor()
)
# Data loader
train_loader = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=batch_size,
shuffle=True
)
test_loader = torch.utils.data.DataLoader(
dataset=test_dataset,
batch_size=batch_size,
shuffle=False
)
# Recurrent neural network (many to one)
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes):
super(RNN, self).__init__() # super avoid many times to running the base class
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(
input_size,
hidden_size,
num_layers,
batch_first=True) # batch_first for parallel computing
self.fc = nn.Linear(hidden_size, num_classes)
def forward(self, x): # __call__ method to use forward automately
# Set initial hidden and cell states
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) #
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
# Forward propagate LSTM
out, _ = self.lstm(x, (h0, c0)) # out: tensor of shape (batch_size, seq_length, hidden_size)
# Decode the hidden state of the last time step
out = self.fc(out[:, -1, :])
return out
model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Train the model
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.reshape(-1, sequence_length, input_size).to(device) # images size is (100, 28, 28)
labels = labels.to(device)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, i + 1, total_step,
loss.item()))
# Test the model
model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.reshape(-1, sequence_length, input_size).to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
# Save the model checkpoint
torch.save(model.state_dict(), 'model.ckpt')