Load model in PyTorch

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*Memos:

• My post explains how to save a model in PyTorch.
• My post explains Linear Regression in PyTorch.
• 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 Deep Learning Workflow in PyTorch.
• My post explains how to clone a private repository with FGPAT(Fine-Grained Personal Access Token) from Github.
• My post explains how to clone a private repository with PAT(Personal Access Token) from Github.
• My post explains useful IPython magic commands.
• My repo has models.

You can load the saved model of my post with load() as shown below:

*Memos:

• Loading the model's state_dict() which has parameters and buffers is recommended according to the doc. *The doc explains What is a state_dict?.
• My post explains save() and load().
• My post explains Path.mkdir(parents, exist_ok) in Python.

This is the saved model state as shown below:

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This is where the model is saved as shown below:

Colab:

JupyterLab:

Now, load and retrain the saved model as shown below:

# "models/linear_regression_0.ipynb"

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)

torch.manual_seed(42)

my_model = LinearRegressionModel().to(device)

""" Load model """
loaded_model = torch.load(f="models/linear_regression_0__0.pth",
                          weights_only=True)
my_model.load_state_dict(loaded_model)
""" Load model """

loss_fn = nn.L1Loss()

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

""" Train and test model """
epochs = 50

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 """

""" Save model """
from pathlib import Path

MODEL_PATH = Path("models")
MODEL_PATH.mkdir(parents=True, exist_ok=True)

MODEL_NAME = "linear_regression_0__1.pth"
MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME

torch.save(obj=my_model.state_dict(), f=MODEL_SAVE_PATH)
""" Save model """

This is the retrained model state as shown below:

This is where the retrained model is saved as shown below:

Colab:

JupyterLab: