tf_begin

# -*- coding: utf-8 -*-

import tensorflow as tf

# Create a constant op that produces a 1x2 matrix.  The op is
# added as a node to the default graph.
#
# The value returned by the constructor represents the output
# of the constant op.
matrix1 = tf.constant([[3., 3.]])

# Create another constant that produces a 2x1 matrix.
matrix2 = tf.constant([[2.],[2.]])

# Create a matmul op that takes 'matrix1' and 'matrix2' as inputs.
# The returned value, 'product', represents the result of the matrix
# multiplication.
product = tf.matmul(matrix1, matrix2)
print(product)

# Launch the default graph.

#上記に書いた演算をここで初めて実行する。
sess = tf.Session()

# To run the matmul op we call the session 'run()' method, passing 'product'
# which represents the output of the matmul op.  This indicates to the call
# that we want to get the output of the matmul op back.
#
# All inputs needed by the op are run automatically by the session.  They
# typically are run in parallel.
#
# The call 'run(product)' thus causes the execution of three ops in the
# graph: the two constants and matmul.
#
# The output of the matmul is returned in 'result' as a numpy `ndarray` object.
result = sess.run(product) #get result of the session; product
print(result)
sess.close()

with tf.Session() as sess:
  result = sess.run([product])
  print(result)
  

#Variables  
state=tf.Variable(0,name="counter")
one=tf.constant(1)
new_value=tf.add(state,one)
update=tf.assign(state,new_value)

init_op=tf.global_variables_initializer()

#Launch graphs
with tf.Session() as sess:
    sess.run(init_op)
    print(sess.run(state))
    for _ in range(3):
        sess.run(update)
        print(sess.run(state))

#Fetches
input1=tf.constant([3.0])
input2=tf.constant([2.0])
input3=tf.constant([5.0])
intermed=tf.add(input2,input3)
mul=tf.multiply(input1,intermed)

with tf.Session() as sess:
    result=sess.run([mul,intermed])
    print(result)



#Feeds
input1=tf.placeholder(tf.float32)
input2=tf.placeholder(tf.float32)
output=input1*input2
with tf.Session() as sess:
    print(sess.run([output],feed_dict={input1:[7.], input2:[2.]}))