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pytorch linear regression gpu

Filthy Fowl answered on November 23, 2022 Popularity 3/10 Helpfulness 1/10

answer pytorch linear regression gpu

pytorch linear regression gpu

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import torch
import torch.nn as nn

'''
STEP 1: CREATE MODEL CLASS
'''
class LinearRegressionModel(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LinearRegressionModel, self).__init__()
        self.linear = nn.Linear(input_dim, output_dim)  

    def forward(self, x):
        out = self.linear(x)
        return out

'''
STEP 2: INSTANTIATE MODEL CLASS
'''
input_dim = 1
output_dim = 1

model = LinearRegressionModel(input_dim, output_dim)

'''
STEP 3: INSTANTIATE LOSS CLASS
'''

criterion = nn.MSELoss()

'''
STEP 4: INSTANTIATE OPTIMIZER CLASS
'''

learning_rate = 0.01

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

'''
STEP 5: TRAIN THE MODEL
'''
epochs = 100
for epoch in range(epochs):
    epoch += 1
    # Convert numpy array to torch Variable
    inputs = torch.from_numpy(x_train).requires_grad_()
    labels = torch.from_numpy(y_train)

    # Clear gradients w.r.t. parameters
    optimizer.zero_grad() 

    # Forward to get output
    outputs = model(inputs)

    # Calculate Loss
    loss = criterion(outputs, labels)

    # Getting gradients w.r.t. parameters
    loss.backward()

    # Updating parameters
    optimizer.step()

Popularity 3/10 Helpfulness 1/10 Language python

Source: www.deeplearningwizard.com

Tags: gpu linear-regression python pytorch

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Contributed on Nov 23 2022

Filthy Fowl

0 Answers Avg Quality 2/10

pytorch linear regression gpu

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