Credits: Forked from TensorFlow-Examples by Aymeric Damien

Refer to the setup instructions

In [1]:

```
import tensorflow as tf
```

In [2]:

```
# Basic constant operations
# The value returned by the constructor represents the output
# of the Constant op.
a = tf.constant(2)
b = tf.constant(3)
```

In [3]:

```
# Launch the default graph.
with tf.Session() as sess:
print "a=2, b=3"
print "Addition with constants: %i" % sess.run(a+b)
print "Multiplication with constants: %i" % sess.run(a*b)
```

a=2, b=3 Addition with constants: 5 Multiplication with constants: 6

In [5]:

```
# Basic Operations with variable as graph input
# The value returned by the constructor represents the output
# of the Variable op. (define as input when running session)
# tf Graph input
a = tf.placeholder(tf.int16)
b = tf.placeholder(tf.int16)
```

In [6]:

```
# Define some operations
add = tf.add(a, b)
mul = tf.mul(a, b)
```

In [7]:

```
# Launch the default graph.
with tf.Session() as sess:
# Run every operation with variable input
print "Addition with variables: %i" % sess.run(add, feed_dict={a: 2, b: 3})
print "Multiplication with variables: %i" % sess.run(mul, feed_dict={a: 2, b: 3})
```

Addition with variables: 5 Multiplication with variables: 6

In [8]:

```
# ----------------
# More in details:
# Matrix Multiplication from TensorFlow official tutorial
# 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.]])
```

In [9]:

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

In [10]:

```
# 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)
```

In [11]:

```
# 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 threes ops in the
# graph: the two constants and matmul.
#
# The output of the op is returned in 'result' as a numpy `ndarray` object.
with tf.Session() as sess:
result = sess.run(product)
print result
```

[[ 12.]]

In [ ]:

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```