train.py

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from NeuralNet import NeuralNet

# Device Config
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# hyper parameters
input_size = 784 # 28*28
hidden_size = 100
num_classes = 10
num_epochs = 20
batch_size = 500
learning_rate = 0.001

# MNIST
training_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())
train_loader = torch.utils.data.DataLoader(dataset=training_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

example = iter(train_loader)
samples, labels = next(example)
print(samples.shape, labels.shape)

# for i in range(6):
#     plt.subplot(2, 3, i+1)
#     plt.imshow(samples[i][0], cmap='gray')
# plt.show()

model = NeuralNet(input_size, hidden_size, num_classes)

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

#training loop
n_total_steps = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 100, 1, 28, 28
        # n, c, h, w
        images = images.reshape(-1, 28*28).to(device)
        labels = labels.to(device)

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

        #backward
        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
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}')
torch.save(model.state_dict(), 'model/model.pt')