Tensors generated from library functions

TensorFlow provides various functions to generate tensors with pre-populated values. The generated values from these functions can be stored in a constant or variable tensor. Such generated values can also be provided to the tensor constructor at the time of initialization.

As an example, let's generate a 1-D tensor that's been pre-populated with 100 zeros:

a=tf.zeros((100,))
print(tfs.run(a))

Some of the TensorFlow library functions that populate these tensors with different values at the time of their definition are listed as follows:

• Populating all of the elements of a tensor with similar values: tf.ones_like(), tf.ones(), tf.fill(), tf.zeros(), andtf.zeros_like() 
• Populating tensors with sequences: tf.range(),and tf.lin_space()
• Populating tensors with a probability distribution: tf.random_uniform(), tf.random_normal(), tf.random_gamma(),and tf.truncated_normal()