PolynomialModel.py

import tensorflow as tf 
import numpy as np 
import matplotlib.pyplot as plt 
learning_rate = 0.01 
training_epochs = 40 
trX = np.linspace(-1, 1, 101) 
num_coeffs = 6 
trY_coeffs = [1, 2, 3, 4, 5, 6] 
trY = 0 
for i in range(num_coeffs): 
	trY += trY_coeffs[i] * np.power(trX, i) 
trY += np.random.randn(*trX.shape) * 1.5 
#plt.scatter(trX, trY) 
#plt.show()
X = tf.placeholder(tf.float32) 
Y = tf.placeholder(tf.float32) 
def model(X, w): 
	terms = [] 
	for i in range(num_coeffs): 
		term = tf.multiply(w[i], tf.pow(X, i)) 
		terms.append(term) 
	return tf.add_n(terms) 
w = tf.Variable([0.] * num_coeffs, name="parameters") 
y_model = model(X, w) 
cost = (tf.pow(Y-y_model, 2)) 
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) 
sess = tf.Session() 
init = tf.global_variables_initializer() 
sess.run(init) 
for epoch in range(training_epochs): 
	for (x, y) in zip(trX, trY): 
		sess.run(train_op, feed_dict={X: x, Y: y}) 
w_val = sess.run(w) 
sess.close() 
plt.scatter(trX, trY) 
trY_new = 0 
for i in range(num_coeffs): 
	trY_new += w_val[i] * np.power(trX, i) 
plt.plot(trX, trY_new, 'r') 
plt.show()