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March 1, 2017 14:25
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| # -*- coding: UTF-8 -*- | |
| """ | |
| Linear regression using Tensorflow | |
| """ | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import tensorflow as tf | |
| rng = np.random | |
| # Parameters | |
| learning_rate = 0.01 | |
| training_epochs = 100 | |
| # Training data | |
| train_X = np.asarray([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.asarray([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] | |
| # Graph input data | |
| X = tf.placeholder('float') | |
| Y = tf.placeholder('float') | |
| # Optimizable parameters with random initialization | |
| weight = tf.Variable(rng.randn(), name='weight') | |
| bias = tf.Variable(rng.randn(), name='bias') | |
| # Linear model | |
| predictions = (X * weight) + bias | |
| # Loss function: Mean Squared Error | |
| loss = tf.reduce_sum(tf.pow(predictions-Y, 2))/(2*n_samples) | |
| # Gradient descent optimizer | |
| optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) | |
| # Initializing the variables | |
| init = tf.global_variables_initializer() | |
| # Launch the graph | |
| with tf.Session() as sess: | |
| sess.run(init) | |
| for epoch in range(training_epochs): | |
| for (x, y) in zip(train_X, train_Y): | |
| sess.run(optimizer, feed_dict={X: x, Y: y}) | |
| train_error = sess.run(loss, feed_dict={X: train_X, Y: train_Y}) | |
| print('Train error={}'.format(train_error)) | |
| # Test error | |
| test_X = np.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1]) | |
| test_Y = np.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03]) | |
| test_error = sess.run( | |
| tf.reduce_sum(tf.pow(predictions - Y, 2)) / (2 * test_X.shape[0]), | |
| feed_dict={X: test_X, Y: test_Y}) | |
| print('Test error={}'.format(test_error)) | |
| print('Weight={} Bias={}'.format(sess.run(weight), sess.run(bias))) | |
| # Graphic display | |
| plt.plot(train_X, train_Y, 'ro', label='Original data') | |
| plt.plot(train_X, sess.run(weight) * train_X | |
| + sess.run(bias), label='Fitted line') | |
| plt.legend() | |
| plt.show() |
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