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Standardizes code formatting and improves efficiency #253

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72 changes: 40 additions & 32 deletions tutorials/01-basics/feedforward_neural_network/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,90 +5,98 @@


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

# Hyper-parameters
# Hyper-parameters
input_size = 784
hidden_size = 500
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001

# MNIST dataset
train_dataset = torchvision.datasets.MNIST(root='../../data',
train=True,
transform=transforms.ToTensor(),
download=True)
# 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())
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)
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
)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)

# Fully connected neural network with one hidden layer
class NeuralNet(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(NeuralNet, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc1 = nn.Linear(input_size, hidden_size)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(hidden_size, num_classes)
self.fc2 = nn.Linear(hidden_size, num_classes)

def forward(self, x):
out = self.fc1(x)
out = self.relu(out)
out = self.fc2(out)
return out


model = NeuralNet(input_size, hidden_size, num_classes).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
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):
for i, (images, labels) in enumerate(train_loader):
# Move tensors to the configured device
images = images.reshape(-1, 28*28).to(device)
images = images.reshape(-1, 28 * 28).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 ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, i+1, total_step, loss.item()))

if (i + 1) % 100 == 0:
print(
"Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}".format(
epoch + 1, num_epochs, i + 1, total_step, loss.item()
)
)

# Test the model
# In test phase, we don't need to compute gradients (for memory efficiency)
with torch.no_grad():
with torch.inference_mode():
correct = 0
total = 0
for images, labels in test_loader:
images = images.reshape(-1, 28*28).to(device)
images = images.reshape(-1, 28 * 28).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('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))
print(
"Accuracy of the network on the 10000 test images: {} %".format(
100 * correct / total
)
)

# Save the model checkpoint
torch.save(model.state_dict(), 'model.ckpt')
torch.save(model.state_dict(), "model.ckpt")
58 changes: 32 additions & 26 deletions tutorials/01-basics/logistic_regression/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,63 +4,65 @@
import torchvision.transforms as transforms


# Hyper-parameters
input_size = 28 * 28 # 784
# Hyper-parameters
input_size = 28 * 28 # 784
num_classes = 10
num_epochs = 5
batch_size = 100
learning_rate = 0.001

# MNIST dataset (images and labels)
train_dataset = torchvision.datasets.MNIST(root='../../data',
train=True,
transform=transforms.ToTensor(),
download=True)
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())
test_dataset = torchvision.datasets.MNIST(
root="../../data", train=False, transform=transforms.ToTensor()
)

# Data loader (input pipeline)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=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=False)
test_loader = torch.utils.data.DataLoader(
dataset=test_dataset, batch_size=batch_size, shuffle=False
)

# Logistic regression model
model = nn.Linear(input_size, num_classes)

# Loss and optimizer
# nn.CrossEntropyLoss() computes softmax internally
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(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):
# Reshape images to (batch_size, input_size)
images = images.reshape(-1, input_size)

# 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()))

if (i + 1) % 100 == 0:
print(
"Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}".format(
epoch + 1, num_epochs, i + 1, total_step, loss.item()
)
)

# Test the model
# In test phase, we don't need to compute gradients (for memory efficiency)
with torch.no_grad():
with torch.inference_mode():
correct = 0
total = 0
for images, labels in test_loader:
Expand All @@ -70,7 +72,11 @@
total += labels.size(0)
correct += (predicted == labels).sum()

print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
print(
"Accuracy of the model on the 10000 test images: {} %".format(
100 * correct / total
)
)

# Save the model checkpoint
torch.save(model.state_dict(), 'model.ckpt')
torch.save(model.state_dict(), "model.ckpt")
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