Tensorflow 101



In [1]:

    
import numpy as np
import tensorflow as tf

Taken from Andrew Ng's Coursera Deep Learning series

https://www.coursera.org/learn/deep-neural-network/lecture/zcZlH/tensorflow



In [4]:

    
# Implement forward path
# TensorFlow figures out backprop
w = tf.Variable(0, dtype=tf.float32)
cost = tf.add(tf.add(w ** 2, tf.multiply(-10.0, w)), 25)
learning_rate = 0.01
train = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)



In [5]:

    
init = tf.global_variables_initializer()
session = tf.Session()
session.run(init)
print(session.run(w))

0.0



In [6]:

    
session.run(train)
print(session.run(w))

0.1



In [7]:

    
for _ in range(1000):
    session.run(train)
print(session.run(w))

An alternative way of identifying the cost is to use overloaded operations



In [8]:

    
# This is equivalent to
# tf.add(tf.add(w ** 2, tf.multiply(-10.0, w)), 25)
cost = w ** 2 - 10 * w + 25

Training data (with input)



In [12]:

    
coefficients = np.array([[1.], [-10.], [25.]])
x = tf.placeholder(tf.float32, [3, 1])
cost = x[0][0] * w ** 2 + x[1][0] * w + x[2][0]
train = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

init = tf.global_variables_initializer()
session = tf.Session()
session.run(init)

for _ in range(1000):
    session.run(train, feed_dict={x: coefficients})
print(session.run(w))



In [14]:

    
with tf.Session() as session:
    session.run(init)
    for _ in range(1000):
        session.run(train, feed_dict={x: coefficients})
    print(session.run(w))