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python-09-TensorFlow
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| # python-09-TensorFlow |
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| ################################################################################################ | |
| # name: TensorFlow_Linear_Regression_01.py | |
| # desc: Linear Regression using TensorFlow | |
| # date: 2019-02-03 | |
| # Author: conquistadorjd | |
| ################################################################################################ | |
| import tensorflow as tf | |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| print('*** Program Started ***') | |
| ########## Input Data Creation | |
| n = 20 | |
| x = np.arange(-n/2,n/2,1,dtype=np.float64) | |
| m_real = np.random.uniform(0.8,0.9,(n,)) | |
| b_real = np.random.uniform(5,10,(n,)) | |
| print('m_real', type(m_real[0])) | |
| y = x*m_real +b_real | |
| ########## Variables definition | |
| m = tf.Variable(np.random.uniform(5,15,(1,))) | |
| b = tf.Variable(np.random.uniform(5,15,(1,))) | |
| ########## display inout data and datatypes | |
| print('x', x, type(x), type(x[0])) | |
| print('y', y, type(y), type(y[0])) | |
| print('m', m, type(m)) | |
| print('b', b, type(b)) | |
| ########## Plot input to see the data | |
| # plt.scatter(x,y,s=None, marker='o',color='g',edgecolors='g',alpha=0.9,label="Linear Relation") | |
| # plt.grid(color='black', linestyle='--', linewidth=0.5,markevery=int) | |
| # plt.legend(loc=2) | |
| # plt.axis('scaled') | |
| # plt.show() | |
| ########## Compute model and loss | |
| model = tf.add(tf.multiply(x,m), b) | |
| loss = tf.reduce_mean(tf.pow(model - y, 2)) | |
| ########## Use following model if you get TypeError | |
| # model = tf.add(tf.multiply(x, tf.cast(m, tf.float64)), tf.cast(b, tf.float64)) | |
| # loss = tf.reduce_mean(tf.pow(model - tf.cast(y, tf.float64), 2)) | |
| ########################################################################################### | |
| # Create optimizer | |
| learn_rate = 0.01 # you can use 0.1/0.01/0.001 to test the output | |
| num_epochs = 500 # Test output accuracy for different epochs | |
| num_batches = n | |
| optimizer = tf.train.GradientDescentOptimizer(learn_rate).minimize(loss) | |
| ########## Initialize variables | |
| init = tf.global_variables_initializer() | |
| ########## Launch session | |
| with tf.Session() as sess: | |
| sess.run(init) | |
| print('*** Initialize') | |
| ########## This is where training happens | |
| for epoch in range(num_epochs): | |
| for batch in range(num_batches): | |
| sess.run(optimizer) | |
| ########## Display and plot results | |
| print('m = ', sess.run(m)) | |
| print('b = ', sess.run(b)) | |
| x1 = np.linspace(-10,10,50) | |
| y1 = sess.run(m)*x1+sess.run(b) | |
| plt.scatter(x,y,s=None, marker='o',color='g',edgecolors='g',alpha=0.9,label="Linear Relation") | |
| plt.grid(color='black', linestyle='--', linewidth=0.5,markevery=int) | |
| plt.legend(loc=2) | |
| plt.axis('scaled') | |
| plt.plot(x1, y1, 'r') | |
| plt.savefig('TensorFlow_Linear_Regression_01.png') | |
| plt.show() | |
| print('*** Program ended ***') |
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