Save model in PyTorch

#python #pytorch #save #model

Buy Me a Coffee☕

*Memos:

My post explains how to load the saved model which I show in this post 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 save a model with save() after training and testing it as shown below:

*Memos:

Saving the model's state_dict() which has parameters and buffers is recommended according to the doc. *The doc explains What is a state_dict?.
.pth or .pt extension is used for PyTorch files in convention.
If you want, you can set the version _<number>, _<number>_<number>, etc just before .ipynb and the version __<number>, __<number>_<number>, etc just before .pth. *The double underscore __ is to separate a model version and the model result version.
My post explains save() and load().
My post explains Path.mkdir(parents, exist_ok) in Python.

# "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)

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__0.pth"
MODEL_SAVE_PATH = MODEL_PATH / MODEL_NAME

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