TensorFlow provides multiple APIs (Application Programming Interfaces). These can be classified into 2 major categories:
- 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.
- 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 sess.run(tf.global_variables_initializer()) # 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.]]