In [1]:

    

import tensorflow as tf
import numpy as np

trainX = np.linspace(-1, 1, 101)
trainY = 3 * trainX + np.random.randn(*trainX.shape) * 0.33


    

In [12]:

    

X = tf.placeholder("float")
Y = tf.placeholder("float")
# Linear regression model is y_model=w*x and we have to calculate the value of w through our model. 
# Lets' initialize w to 0 and create a model to solve this problem.
w = tf.Variable(0.0, name="weights")
y_model = tf.multiply(X, w)

cost = (tf.pow(Y - y_model, 2))
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(cost)


    

In [13]:

    

# create init_op to initialize all variables
init = tf.global_variables_initializer()


    

In [24]:

    

with tf.Session() as sess:
    sess.run(init)
    for i in range(100):
        for (x, y) in zip(trainX, trainY):
            sess.run(train_op, feed_dict={X: x, Y: y})
            #print("round " + str(i) + ": " + str(sess.run(w)))
    print(sess.run(w))


    

    




2.9627

In [26]:

    

with tf.Session() as sess:
    sess.run(init)
    print(sess.run(w))


    

    




0.0

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