python-tensorflowWhat is TensorFlow in Python?

TensorFlow is an open-source library for numerical computation and machine learning using data flow graphs in Python. It is used for a variety of tasks such as classification, regression, and clustering. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

Example code

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)

# Launch the default graph.
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 op is returned in 'result' as a numpy `ndarray` object.
result = sess.run(product)
print(result)
# ==> [[ 12.]]

Output example

[[12.]]

Code explanation

1. import tensorflow as tf - imports the TensorFlow library as the tf module.
2. matrix1 = tf.constant([[3., 3.]]) - creates a constant op that produces a 1x2 matrix.
3. matrix2 = tf.constant([[2.],[2.]]) - creates a constant op that produces a 2x1 matrix.
4. product = tf.matmul(matrix1, matrix2) - creates a Matmul op that takes matrix1 and matrix2 as inputs and returns the result of the matrix multiplication as product.
5. sess = tf.Session() - launches the default graph.
6. result = sess.run(product) - runs the matmul op and returns the output in result as a numpy ndarray object.
7. print(result) - prints the output.

Helpful links

1. TensorFlow Official Website
2. TensorFlow Tutorials
3. TensorFlow Documentation

Edit this code on GitHub