Linear Regression in PyTorch

#python #pytorch #linearregression #model

*Memos:

My post explains Batch, Mini-Batch and Stochastic Gradient Descent with DataLoader() in PyTorch.
My post explains Batch Gradient Descent without DataLoader() in PyTorch.
My post explains Batch, Mini-Batch and Stochastic Gradient Descent in PyTorch.
My post explains how to save a model in PyTorch.
My post explains how to load a saved model in PyTorch.
My post explains Deep Learning Workflow in PyTorch.
My repo has models.

Linear Regression is the method to predict unknown data using another related known(true) data, making linear function as shown below:

*Memos:

You can create Linear Regression model without nn.Linear() as the uncommented code in the example below: *Memos:
- The results are different from the case with nn.Linear().
- My post explains nn.Linear().
My post explains cuda.is_available()
My post explains Module().
My post explains L1Loss() and MSELoss().
My post explains SGD().
My post explains Adam().

import torch
from torch import nn
from torch import optim

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

""" Prepare dataset """
weight = 0.8
bias = 0.5

X = torch.tensor([[0.00], [0.02], [0.04], [0.06], [0.08], # Size(50, 1)
                  [0.10], [0.12], [0.14], [0.16], [0.18],
                  [0.20], [0.22], [0.24], [0.26], [0.28],
                  [0.30], [0.32], [0.34], [0.36], [0.38],
                  [0.40], [0.42], [0.44], [0.46], [0.48],
                  [0.50], [0.52], [0.54], [0.56], [0.58],
                  [0.60], [0.62], [0.64], [0.66], [0.68],
                  [0.70], [0.72], [0.74], [0.76], [0.78],
                  [0.80], [0.82], [0.84], [0.86], [0.88],
                  [0.90], [0.92], [0.94], [0.96], [0.98]], device=device)
Y = weight * X + bias

l = int(0.8 * len(X))
X_train, Y_train, X_test, Y_test = X[:l], Y[:l], X[l:], Y[l:]
""" Prepare dataset """

""" Prepare model, loss function and optimizer """
class LinearRegressionModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear_layer = nn.Linear(in_features=1, out_features=1)

    def forward(self, x):
        return self.linear_layer(x)

    """ Without `nn.Linear()` """
    # def __init__(self):
    #     super().__init__()
    #     self.weight = nn.Parameter(torch.randn(1))
    #     self.bias = nn.Parameter(torch.randn(1))

    # def forward(self, x):
    #     return self.weight * x + self.bias
    """ Without `nn.Linear()` """

torch.manual_seed(42)

my_model = LinearRegressionModel().to(device)

loss_fn = nn.L1Loss()
# loss_fn = nn.MSELoss()

optimizer = optim.SGD(params=my_model.parameters(), lr=0.01)
# optimizer = optim.Adam(params=my_model.parameters(), lr=0.01)
""" Prepare model, loss function and optimizer """

""" Train and test model """
epochs = 100 # Try 0, 50, 100, 150

epoch_count = []
loss_values = []
test_loss_values = []

for epoch in range(epochs):

    """ Train """
    my_model.train()

    # 1. Calculate predictions(Forward propagation)
    Y_pred = my_model(X_train)

    # 2. Calculate loss
    loss = loss_fn(Y_pred, Y_train)

    # 3. Zero out gradients
    optimizer.zero_grad()

    # 4. Calculate a gradient(Backpropagation)
    loss.backward()

    # 5. Update parameters
    optimizer.step()
    """ Train """

    """ Test """
    my_model.eval()

    with torch.inference_mode():
        Y_test_pred = my_model(x=X_test)
        test_loss = loss_fn(Y_test_pred, Y_test)
    if epoch % 10 == 0:
        epoch_count.append(epoch)
        loss_values.append(loss)
        test_loss_values.append(test_loss)
        # print(f"Epoch: {epoch} | Loss: {loss} | Test loss: {test_loss}")
        # ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ Uncomment it to see the details ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
    """ Test """
""" Train and test model """

""" Visualize train and test data and predictions"""
import matplotlib.pyplot as plt

with torch.inference_mode():
    Y_pred = my_model(x=X_test)

def plot_predictions(X_train, Y_train, X_test, Y_test, predictions=None):
    plt.figure(figsize=[6, 4])
    plt.scatter(x=X_train, y=Y_train, c='g', s=5, label='Train data(Green)')
    plt.scatter(x=X_test, y=Y_test, c='b', s=15, label='Test data(Blue)')
    if predictions is not None:
        plt.scatter(x=X_test, y=predictions, c='r',
                    s=15, label='Predictions(Red)')
    plt.title(label="Train and test data and predictions", fontsize=14)
    plt.legend(fontsize=14)

plot_predictions(X_train=X_train.cpu(),
                 Y_train=Y_train.cpu(),
                 X_test=X_test.cpu(),
                 Y_test=Y_test.cpu(),
                 predictions=Y_pred.cpu())
""" Visualize train and test data, predictions"""

""" Visualize train and test loss """
def plot_loss_curves(epoch_count, loss_values, test_loss_values):
    plt.figure(figsize=[6, 4])
    plt.plot(epoch_count, loss_values, label="Train loss")
    plt.plot(epoch_count, test_loss_values, label="Test loss")
    plt.title(label="Train and test loss curves", fontsize=14)
    plt.ylabel(ylabel="Loss", fontsize=14)
    plt.xlabel(xlabel="Epochs", fontsize=14)
    plt.legend(fontsize=14)

plot_loss_curves(epoch_count=epoch_count,
                 loss_values=torch.tensor(loss_values).cpu(),
                 test_loss_values=torch.tensor(test_loss_values).cpu())
""" Visualize train and test loss """