PyTorchNLPLessons/LinearRegressionFunction.py at main · ahritik/PyTorchNLPLessons · GitHub

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# +
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt

# Hyper-parameters
input_size = 1
output_size = 1
num_epochs = 60
learning_rate = 0.001

# Toy dataset
x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168],
                    [9.779], [6.182], [7.59], [2.167], [7.042],
                    [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)

y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573],
                    [3.366], [2.596], [2.53], [1.221], [2.827],
                    [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)


# -

class linReg:

    def __init__(self, input_size, output_size, learning_rate = 0.001):
        self.model = nn.Linear(input_size, output_size)
        self.criterion = nn.MSELoss()
        self.optimizer = torch.optim.SGD(self.model.parameters(), lr=learning_rate)

    def getModel(self):
        return self.model

    def train(self, numEpochs,xTrain, yTrain):
        for epoch in range(numEpochs):
            # Convert numpy arrays to torch tensors
            inputs = torch.from_numpy(xTrain)
            targets = torch.from_numpy(yTrain)

            # Forward pass
            outputs = model(inputs)
            loss = self.criterion(outputs, targets)

            # Backward and optimize
            self.optimizer.zero_grad()
            loss.backward()
            self.optimizer.step()

    def predict(self, xTest):
        return self.model(torch.from_numpy(xTest)).detach().numpy()


# +
# Linear regression model
model = nn.Linear(input_size, output_size)

# Loss and optimizer
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# -

# Train the model
for epoch in range(num_epochs):
    # Convert numpy arrays to torch tensors
    inputs = torch.from_numpy(x_train)
    targets = torch.from_numpy(y_train)

    # Forward pass
    outputs = model(inputs)
    loss = criterion(outputs, targets)

    # Backward and optimize
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (epoch+1) % 5 == 0:
        print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# Plot the graph
predicted = model(torch.from_numpy(x_train)).detach().numpy()
plt.plot(x_train, y_train, 'ro', label='Original data')
plt.plot(x_train, predicted, label='Fitted line')
plt.legend()
plt.show()

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