A Smooth Introduction to Linear Regression and its Implementation in PyTorch (Part-II)

import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
input_size = 1
output_size = 1
epochs = 50
learning_rate = 0.0001
h_train = np.array([[0],[1],[2],[3],[4],[5]],dtype=np.float32)
r_train = np.array([[10],[12],[7],[13],[5],[9]],dtype=np.float32)
model = nn.Linear(input_size,output_size)
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(),lr=learning_rate)
# train the model
for epoch in range(epochs):
inputs = torch.from_numpy(h_train)
targets = torch.from_numpy(r_train)
# forward propagation
predictions = model(inputs)
loss = loss_function(predictions,targets)
# backward propagation
# optimization
print 'Epoch: ' + str(epoch) + '.....' + 'Loss: ' + str(loss.item())
prediction = model(torch.from_numpy(h_train)).detach().numpy()
plt.plot(h_train, r_train, 'v--g', label='original data')
plt.plot(h_train, prediction, 'r', label='best fit line')



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Dr. Abder-Rahman Ali

Dr. Abder-Rahman Ali

Leveraging machine/deep learning and image processing in medical image analysis | https://abder.me