rnn_mnist.py

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')

# Hyper-parameters
# input_size = 784 # 28x28
input_size = 28
sequence_length = 28
num_classes = 10
num_epochs = 2
batch_size = 64
learning_rate = 0.001
hidden_size = 128
num_layers = 2

# 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)


# Fully connected neural network with one hidden layer
class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(RNN, self).__init__()
        self.num_layers = num_layers
        self.hidden_size = hidden_size
        self.rnn = nn.RNN(input_size, hidden_size, num_layers, batch_first=True)
        # -> x needs to be: (batch_size, seq, input_size)

        # or:
        # self.gru = nn.GRU(input_size, hidden_size, num_layers, batch_first=True)
        # self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        # Set initial hidden states (and cell states for LSTM)
        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)

        # x: (n, 28, 128), h0: (2, n, 128)
        # x:(batch_size, sequence_length, hidden_size.input),
        # h0:(num_layers, batch_size,hidden_size.output)

        # Forward propagate RNN
        out, _ = self.rnn(x, h0)

        # or:
        # out, _ = self.lstm(x, (h0,c0))

        # out: tensor of shape (batch_size, seq_length, hidden_size)
        # out: (n, 28, 128)

        # Decode the hidden state of the last time step
        out = out[:, -1, :]
        # out: (n, 128)

        out = self.fc(out)
        # out: (n, 10)
        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
n_total_steps = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # origin shape: [N, 1, 28, 28]
        # resized: [N, 28, 28]
        images = images.reshape(-1, sequence_length, input_size).to(device)
        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(f'Epoch [{epoch + 1}/{num_epochs}], Step [{i + 1}/{n_total_steps}], Loss: {loss.item():.4f}')

# Test the model
# In test phase, we don't need to compute gradients (for memory efficiency)
with torch.no_grad():
    n_correct = 0
    n_samples = 0
    for images, labels in test_loader:
        images = images.reshape(-1, sequence_length, input_size).to(device)
        labels = labels.to(device)
        outputs = model(images)
        # max returns (value ,index)
        _, predicted = torch.max(outputs.data, 1)
        n_samples += labels.size(0)
        n_correct += (predicted == labels).sum().item()

    acc = 100.0 * n_correct / n_samples
    print(f'Accuracy of the network on the 10000 test images: {acc} %')