02_simple_tf.py

""" Some simple TensorFlow's ops
Author: Chip Huyen
Prepared for the class CS 20SI: "TensorFlow for Deep Learning Research"
cs20si.stanford.edu
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'

import numpy as np
import tensorflow as tf


a = tf.constant(2)
b = tf.constant(3)
x = tf.add(a, b)
with tf.Session() as sess:
	writer = tf.summary.FileWriter('./graphs', sess.graph) 
	print(sess.run(x))
writer.close() # close the writer when you’re done using it


a = tf.constant([2, 2], name='a')
b = tf.constant([[0, 1], [2, 3]], name='b')
x = tf.multiply(a, b, name='dot_product')
with tf.Session() as sess:
	print(sess.run(x))
# >> [[0 2]
#	 [4 6]]

tf.zeros(shape, dtype=tf.float32, name=None)
#creates a tensor of shape and all elements will be zeros (when ran in session)

x = tf.zeros([2, 3], tf.int32) 
y = tf.zeros_like(x, optimize=True)
print(y)
print(tf.get_default_graph().as_graph_def())
with tf.Session() as sess:
	y = sess.run(y)


with tf.Session() as sess:
	print(sess.run(tf.linspace(10.0, 13.0, 4)))
	print(sess.run(tf.range(5)))
	for i in np.arange(5):
		print(i)

samples = tf.multinomial(tf.constant([[1., 3., 1]]), 5)

with tf.Session() as sess:
	for _ in range(10):
		print(sess.run(samples))

t_0 = 19 
x = tf.zeros_like(t_0) # ==> 0
y = tf.ones_like(t_0) # ==> 1

with tf.Session() as sess:
	print(sess.run([x, y]))

t_1 = ['apple', 'peach', 'banana']
x = tf.zeros_like(t_1) # ==> ['' '' '']
y = tf.ones_like(t_1) # ==> TypeError: Expected string, got 1 of type 'int' instead.

t_2 = [[True, False, False],
       [False, False, True],
       [False, True, False]] 
x = tf.zeros_like(t_2) # ==> 2x2 tensor, all elements are False
y = tf.ones_like(t_2) # ==> 2x2 tensor, all elements are True
with tf.Session() as sess:
	print(sess.run([x, y]))

with tf.variable_scope('meh') as scope:
	a = tf.get_variable('a', [10])
	b = tf.get_variable('b', [100])

writer = tf.summary.FileWriter('test', tf.get_default_graph())


x = tf.Variable(2.0)
y = 2.0 * (x ** 3)
z = 3.0 + y ** 2
grad_z = tf.gradients(z, [x, y])
with tf.Session() as sess:
	sess.run(x.initializer)
	print(sess.run(grad_z))