What are the different APIs Tensorflow provides?

TensorFlow provides multiple APIs (Application Programming Interfaces). These can be classified into 2 major categories:

Low-level API:

  • complete programming control
  • recommended for machine learning researchers
  • provides fine levels of control over the models
  • TensorFlow Core is the low-level API of TensorFlow.
    High-level API:
  • built on top of TensorFlow Core
  • easier to learn and use than TensorFlow Core
  • make repetitive tasks easier and more consistent between different users
  • tf.contrib.learn is an example of a high level API.

Given below is an example using Variable:

# importing tensorflow
import tensorflow as tf
# creating nodes in computation graph
node = tf.Variable(tf.zeros([2,2]))
# running computation graph
with tf.Session() as sess:
    # initialize all global variables 
    # evaluating node
    print("Tensor value before addition:\n",sess.run(node))
    # elementwise addition to tensor
    node = node.assign(node + tf.ones([2,2]))
    # evaluate node again
    print("Tensor value after addition:\n", sess.run(node))


Tensor value before addition:
 [[ 0.  0.]
 [ 0.  0.]]
Tensor value after addition:
 [[ 1.  1.]
 [ 1.  1.]]