Python - tensorflow.GradientTape()

Python - tensorflow.constant_initializer()

Last Updated : 10 Jul, 2020

TensorFlow is open-source Python library designed by Google to develop Machine Learning models and deep learning neural networks.

constant_initializer() is initializer that generate a Tensor with constant value.

Syntax: tensorflow.constant_initializer( value )

Parameters:

value: It is the value that needed to be converted to Tensor. It can be Python scalar, list or tuple of values, or a N-dimensional numpy array

Returns: It returns an Initializer instance.

Example 1: From Python list

Python3

# Importing the library
import tensorflow as tf

# Initializing the input
l = [1, 2, 3, 4]

# Printing the input
print('l: ', l)

# Calculating result
x = tf.constant_initializer(l)


# Printing the result
print('x: ', x)

Output:

l:  [1, 2, 3, 4]
x:  tensorflow.python.ops.init_ops_v2.Constant object at 0x7fa7f2e1ab00

Example 2: From Python tuple

Python3

# Importing the library
import tensorflow as tf

# Initializing the input
l = (1, 2, 3, 4)

# Printing the input
print('l: ', l)

# Calculating result
x = tf.constant_initializer(l )

# Printing the result
print('x: ', x)

Output:

l:  (1, 2, 3, 4)
x:  tensorflow.python.ops.init_ops_v2.Constant object at 0x7fa7f2e1ab00

Python - tensorflow.GradientTape()

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