83536 linear_regression.py

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
 
 
rng = np.random
# parameter
learning_rate = 0.01
training_epochs = 1000
display_step = 50
# 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]
# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")
# Set model weights
W = tf.Variable(rng.randn(), name='weight')
b = tf.Variable(rng.randn(), name='bias')
# construct a Linear Model
pred = tf.add(tf.multiply(X, W), b) # Y = WX + b
# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2)) / (2*n_samples)
# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
init = tf.global_variables_initializer()
# Start Training
with tf.Session() as sess:
    sess.run(init)
    # Fit all training data
    for epoch in range(training_epochs):
        for(x, y) in zip(train_X, train_Y):
            sess.run(optimizer, {X: x, Y: y})
        # Display logs per epoch step
        if(epoch+1) % display_step == 0:
            c = sess.run(cost, {X: train_X, Y: train_Y})
            print("Epoch: ", '%0.4d' % (epoch+1), "cost = ", "{:.9f}".format(c),
                  "W=", sess.run(W), "b= ", sess.run(b))
    print("Optimization Finished ")
    training_cost = sess.run(cost, {X: train_X, Y: train_Y})
    print("Training_Cost = ", training_cost, "W = ", sess.run(W), "b= ", sess.run(b),'\n')
    # Graphic display
    plt.plot(train_X, train_Y, 'ro', label = 'Original Data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label = 'Fitted Line')
    plt.legend()
    plt.show()