feedforward.py

# MNIST
# DataLoader, Transformation
# Multilayer Neural Net, activation function
# Loss and Optimizer
# Training Loop (batch training)
# Model evaluation
# GPU support

from random import shuffle
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt

# device config
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Hyperparameters
input_size = 784 # 28x28
hidden_size = 100
num_classes = 10
num_epochs = 2
batch_size = 64
learning_rate = 0.001


# 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(), download=True)

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=True)



def plot_images(num_imgs):
    examples = iter(train_loader)
    samples, labels = examples.next()
    print(samples.shape)
    print(labels.shape)
    for i in range(num_imgs):
        plt.subplot(2, 3, i+1)
        plt.imshow(samples[i][0], cmap='gray')
    
    plt.show()

# plot_images(5)


class NeuralNetwork(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes) -> None:
        super().__init__()
        self.l1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.l2 = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        x = self.l1(x)
        x = self.relu(x)
        # no softmax at the last layer
        x = self.l2(x)
        return x


model = NeuralNetwork(input_size, hidden_size, num_classes)

####################
# Loss and Optimizer
####################
criterion = nn.CrossEntropyLoss()
optimizer  = torch.optim.Adam(model.parameters(), lr=learning_rate)


#######################
# Training
#######################
n_total_steps = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # images: 64, 1, 28, 28 -> 64, 784
        images = images.reshape(-1, 28*28).to(device)
        labels = labels.to(device)

        # forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

        # backward pass
        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():.2f}')


    # evaluation
    with torch.no_grad():
        n_correct = 0
        n_samples = 0
        for images, labels in test_loader:
            images = images.reshape(-1, 28*28).to(device)
            labels = labels.to(device)
            outputs = model(images)

            # value, index
            _, predictions = torch.max(outputs, 1)
            n_samples += labels.shape[0]
            n_correct += (predictions == labels).sum().item()
        
        acc = 100.0 * n_correct / n_samples
        print(f'accuracy = {acc}')