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# import python packages
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
In [3]:
# Set Hyper-parameters
input_size = 1
output_size = 1
num_epochs = 60
learning_rate = 0.001
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# Let's define a 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)
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# Define a Linear model
model = nn.Linear(input_size, output_size)
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# Loss and optimizer
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
In [10]:
# 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()))
Epoch [5/60], Loss: 0.9564 Epoch [10/60], Loss: 0.6338 Epoch [15/60], Loss: 0.5028 Epoch [20/60], Loss: 0.4493 Epoch [25/60], Loss: 0.4273 Epoch [30/60], Loss: 0.4180 Epoch [35/60], Loss: 0.4138 Epoch [40/60], Loss: 0.4118 Epoch [45/60], Loss: 0.4106 Epoch [50/60], Loss: 0.4098 Epoch [55/60], Loss: 0.4090 Epoch [60/60], Loss: 0.4084
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# 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()
In [15]:
# Save the model checkpoint
torch.save(model.state_dict(), 'model.ckpt')
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