[TensorFlow] Basic Usage

Install TensorFlow on mac

Install pip 

# Mac OS X
$ sudo easy_install pip
$ sudo easy_install --upgrade six

Install tensorflow 

# Mac OS X, CPU only, Python 2.7:
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py2-none-any.whl

 

 

Basic Usage

The concepts in TensorFlow

Tensor : a typed multi-dimensional array. Only Tensor are passed between operations in the computation graph.

Graph : a description of computation.

op : (short for operation) the node in the graph

Session : the context in which to execute graphs

Devices : such as CPUs or GPUs, on which Session execute graphs

Variable : maintain state 

numpy ndarray : the result structure returned by Sesson.run(graph), which is produced by ops in graph and transformed from tensor

 

Simple example

import tensorflow as tf

# Start with default graph.
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.],[2.]])
product = tf.matmul(matrix1, matrix2)
# Now the default graph has three nodes: two constant() ops and one matmul() op.

# Launch the default graph in a Session
sess = tf.Session()

result = sess.run(product)
print(result)
sess.close()

The call 'run(product)' causes the execution of three ops in the graph.

 

Fetches : to fech the outputs of operations, execute the graph with a run() call on the Session object. E.g. sess.run(product) in above sample

Feeds : a mechanism for patching tensors directly into operations in graph. Supply feed data as argument to a run() call. tf.placeholder is used to create "feed" operations commonly

input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
output = tf.mul(input1, input2)

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

# output:
# [array([ 14.], dtype=float32)]

 

 

References

Download and Setup, TensorFlow

Basic Usage, TensorFlow