TensorFlow for deep learning

TensorFlow provides multiple APIs. The lowest level API--TensorFlow Core-- provides you with complete programming control.
The higher level APIs are built on top of TensorFlow Core.

Tensor
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The central unit of data in TensorFlow is the tensor.
 A tensor consists of a set of primitive values shaped into an array of any number of dimensions.
 A tensor's rank is its number of dimensions.
 example:
 3 # a rank 0 tensor; a scalar with shape []
[1., 2., 3.] # a rank 1 tensor; a vector with shape [3]
[[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3]
[[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3]


Comutational Graph
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You might think of TensorFlow Core programs as consisting of two discrete sections:

    Building the computational graph.
    Running the computational graph.

A computational graph is a series of TensorFlow operations arranged into a graph of nodes. 
Each node takes zero or more tensors as inputs and produces a tensor as an output. 
W
e can create two floating point Tensors node1 and node2 as follows:

node1 = tf.constant(3.0, dtype=tf.float32)
node2 = tf.constant(4.0) # also tf.float32 implicitly
print(node1, node2);



A session encapsulates the control and state of the TensorFlow runtime.
sess = tf.Session()
print(sess.run([node1, node2]))

a = tf.placeholder(tf.float32)
b = tf.placeholder(tf.float32)
adder_node = a + b  # + provides a shortcut for tf.add(a, b)

print(sess.run(adder_node, {a: 3, b: 4.5}))
print(sess.run(adder_node, {a: [1, 3], b: [2, 4]}))

add_and_triple = adder_node * 3.
print(sess.run(add_and_triple, {a: 3, b: 4.5}))