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#!/usr/bin/env python

import tensorflow as tf

import numpy as np

trX = np.linspace(-1, 1, 101)

trY = 2 * trX + np.random.randn(*trX.shape) * 0.33 # create a y value which is approximately linear but with some random noise

X = tf.placeholder("float") # create symbolic variables

Y = tf.placeholder("float")

def model(X, w):

return tf.mul(X, w) # lr is just X*w so this model line is pretty simple

w = tf.Variable(0.0, name="weights") # create a shared variable (like theano.shared) for the weight matrix

y_model = model(X, w)

cost = tf.square(Y - y_model) # use square error for cost function

train_op = tf.train.GradientDescentOptimizer(0.01).minimize(cost) # construct an optimizer to minimize cost and fit line to my data

# Launch the graph in a session

with tf.Session() as sess:

# you need to initialize variables (in this case just variable W)

tf.global_variables_initializer().run()

for i in range(100):

for (x, y) in zip(trX, trY):

sess.run(train_op, feed_dict={X: x, Y: y})

print(sess.run(w)) # It should be something around 2