tuts7optimization.py

# Optimizing Model Parameters
# Now that we have a model and data it's time to train,
# validate and test our model by optimizing its parameters on our data.
# Training a model is an iterative process; in each iteration the model 
# makes a guess about the output, calculates the error in its guess (loss),
# collects the derivatives of the error with respect to its parameters (as we saw in the previous section),
# and optimizes these parameters using gradient descent.

# https://pytorch.org/tutorials/beginner/basics/optimization_tutorial.html


import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor

training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor()
)

test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor()
)


train_dataloader = DataLoader(training_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)


class NeuralNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(28*28, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 10),
        )

    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits
        
model = NeuralNetwork()


# Hyperparameters
# Hyperparameters are adjustable parameters that let you control the model optimization process.
# Different hyperparameter values can impact model training and convergence rates.
# Learning Rate: how much to update models parameters at each batch/epoch. 
# Smaller values yield slow learning speed, while large values may result in unpredictable behavior
# during training.
# Batch Size: the number of data samples propagated through the network before the parameters are updated.
# Number of Epochs: the number times to iterate over the dataset.

learning_rate = 1e-3
batch_size = 64
epochs = 5


# Optimization Loop
# Once we set our hyperparameters, we can then train and optimize our model with an optimizaiton loop.
# Each iteration of the optimization loop is called an epoch.
# Each epoch consists of two main parts:
# The Train Loop - iterate over the training dataset and try to converge to optimal parameters.
# The Validation / Test Loop - iterate over the test dataset to check if model performance is improving.


# Loss Function
# When presented with some training data, our untrained network is likely not to give the correct answer.
# Loss function measures the degree of dissimilarity of obtained result to the target value,
# and it is the loss function that we want to minimize during training.
# To calculate the loss we make a prediction using the inputs of our given data sample
# and compare it against the true data label value.
# We pass our model's output logits to nn.CrossEntropyLoss, which will normalize the logits and compute the prediction error.

# Initialize the loss function.
loss_fn = nn.CrossEntropyLoss()


# Optimizer
# Optimization is the process of adjusting model parameters to reduce model error in each training step.
# All optimization logic is encapsulate in the optimizer object. Here, we use the SGD optimizer.
# We initialize the optimizer by registering the model's parameters that need to be trained, 
# and passing in the learning rate hyperparameter.

optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)


# Training Loop
# Inside the training loop, optimization happens in three steps:
# Call optimizer.zero_grad() to reset the gradients of model parameters.
# Gradients by default add up; to prevent double-counting, we explicitly zero them at each iteration.
# Backpropagate the prediction loss with a call to loss.backward().
# PyTorch deposits the gradients of the loss w.r.t each parameter.
# Once we have our gradients, we call optimizer.step() to adjust the parameters by the gradients collected in the backward pass.

def train_loop(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)

    # Set the model to training mode as it is required for batch normalization and dropout layers.

    model.train()
    for batch, (X, y) in enumerate(dataloader):
        # Compute prediction and loss.
        pred = model(X)
        loss = loss_fn(pred, y)

        # Backpropagation.
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

        if batch % 100 == 0:
            loss, current = loss.item(), batch * batch_size + len(X)
            print(f"loss: {loss:>7f} [{current: >5d}/{size:>5d}]")


def test_loop(dataloader, model, loss_fn):
    # Set the model to evaluation mode, which is important for batch normalization and dropout layers.

    model.eval()
    size = len(dataloader.dataset)
    num_batches = len(dataloader)
    test_loss, correct = 0, 0


    # Evaluating the model with torch.no_grad() ensures that no gradients are computed during test mode
    # also serves to reduce unnecessary gradient computations and memory usage for tensors with requires_grad=True
    with torch.no_grad():
        for X, y in dataloader:
            pred = model(X)
            test_loss += loss_fn(pred, y).item()
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()

    test_loss /= num_batches
    correct /= size
    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")


# We initialize the loss function and optimizer, and pass it to train_loop and test_loop.
# Increase the number of epochs to track the model's improving performance.

loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

epochs = 10
for t in range(epochs):
    print(f"Epoch {t+1}\n------------------------------")
    train_loop(train_dataloader, model, loss_fn, optimizer)
    test_loop(test_dataloader, model, loss_fn)
print("Done!")