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March 1, 2017 14:51
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| # -*- coding: UTF-8 -*- | |
| """ | |
| Linear regression using Autograd | |
| """ | |
| import autograd.numpy as np | |
| import matplotlib.pyplot as plt | |
| from autograd import elementwise_grad | |
| rng = np.random | |
| # Parameters | |
| learning_rate = 0.01 | |
| training_epochs = 100 | |
| # Training data | |
| train_X = 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, 5.654, 9.27, 3.1]) | |
| train_Y = 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, 2.42, 2.94, 1.3]) | |
| n_samples = train_X.shape[0] | |
| def loss((weight, bias)): | |
| """ Loss function: Mean Squared Error """ | |
| predictions = (train_X * weight) + bias | |
| return np.sum(np.power(predictions - train_Y, 2) / (2 * n_samples)) | |
| # Function that returns gradients of loss function | |
| gradient_fun = elementwise_grad(loss) | |
| # Optimizable parameters with random initialization | |
| weight = rng.randn() | |
| bias = rng.randn() | |
| for epoch in range(training_epochs): | |
| gradients = gradient_fun((weight, bias)) | |
| weight -= gradients[0] * learning_rate | |
| bias -= gradients[1] * learning_rate | |
| print('Train error={}'.format(loss((weight, bias)))) | |
| # Test error | |
| test_X = np.array([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1]) | |
| test_Y = np.array([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03]) | |
| predictions = (test_X * weight) + bias | |
| print('Test error={}'.format( | |
| np.sum(np.power(predictions - test_Y, 2) / (2 * n_samples)))) | |
| print('Weight={} Bias={}'.format(weight, bias)) | |
| # Graphic display | |
| plt.plot(train_X, train_Y, 'ro', label='Original data') | |
| plt.plot(train_X, weight * train_X + bias, label='Fitted line') | |
| plt.legend() | |
| plt.show() |
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