TensorFlow Basics

Remember to reference the video for full explanations, this is just a notebook for code reference.

You can import the library:



In [28]:

    
import tensorflow as tf



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print(tf.__version__)

Simple Constants

Let's show how to create a simple constant with Tensorflow, which TF stores as a tensor object:



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hello = tf.constant('Hello World')



In [3]:

    
type(hello)









    Out[3]:





tensorflow.python.framework.ops.Tensor



In [4]:

    
x = tf.constant(100)



In [5]:

    
type(x)









    Out[5]:





tensorflow.python.framework.ops.Tensor

Running Sessions

Now you can create a TensorFlow Session, which is a class for running TensorFlow operations.

A Session object encapsulates the environment in which Operation objects are executed, and Tensor objects are evaluated. For example:



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sess = tf.Session()



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sess.run(hello)









    Out[7]:





b'Hello World'



In [8]:

    
type(sess.run(hello))









    Out[8]:





bytes



In [9]:

    
sess.run(x)









    Out[9]:





100



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type(sess.run(x))









    Out[10]:





numpy.int32

Operations

You can line up multiple Tensorflow operations in to be run during a session:



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x = tf.constant(2)
y = tf.constant(3)



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with tf.Session() as sess:
    print('Operations with Constants')
    print('Addition',sess.run(x+y))
    print('Subtraction',sess.run(x-y))
    print('Multiplication',sess.run(x*y))
    print('Division',sess.run(x/y))









    



Operations with Constants
Addition 5
Subtraction -1
Multiplication 6
Division 0.6666666666666666

Placeholder

You may not always have the constants right away, and you may be waiting for a constant to appear after a cycle of operations. tf.placeholder is a tool for this. It inserts a placeholder for a tensor that will be always fed.

Important: This tensor will produce an error if evaluated. Its value must be fed using the feed_dict optional argument to Session.run(), Tensor.eval(), or Operation.run(). For example, for a placeholder of a matrix of floating point numbers:

x = tf.placeholder(tf.float32, shape=(1024, 1024))

Here is an example for integer placeholders:



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x = tf.placeholder(tf.int32)
y = tf.placeholder(tf.int32)



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x









    Out[14]:





<tf.Tensor 'Placeholder:0' shape=<unknown> dtype=int32>



In [15]:

    
type(x)









    Out[15]:





tensorflow.python.framework.ops.Tensor

Defining Operations



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add = tf.add(x,y)
sub = tf.subtract(x,y)
mul = tf.multiply(x,y)

Running operations with variable input:



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d = {x:20,y:30}



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with tf.Session() as sess:
    print('Operations with Constants')
    print('Addition',sess.run(add,feed_dict=d))
    print('Subtraction',sess.run(sub,feed_dict=d))
    print('Multiplication',sess.run(mul,feed_dict=d))









    



Operations with Constants
Addition 50
Subtraction -10
Multiplication 600

Now let's see an example of a more complex operation, using Matrix Multiplication. First we need to create the matrices:



In [19]:

    
import numpy as np
# Make sure to use floats here, int64 will cause an error.
a = np.array([[5.0,5.0]])
b = np.array([[2.0],[2.0]])



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a









    Out[20]:





array([[5., 5.]])



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a.shape









    Out[21]:





(1, 2)



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b









    Out[22]:





array([[2.],
       [2.]])



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b.shape









    Out[23]:





(2, 1)



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mat1 = tf.constant(a)



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mat2 = tf.constant(b)

The matrix multiplication operation:



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matrix_multi = tf.matmul(mat1,mat2)

Now run the session to perform the Operation:



In [27]:

    
with tf.Session() as sess:
    result = sess.run(matrix_multi)
    print(result)









    



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