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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()
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