Python – tensorflow.raw_ops.Exp()

• Last Updated : 05 Jun, 2020

TensorFlow is open-source python library designed by Google to develop Machine Learning models and deep learning  neural networks. TensorFlow raw_ops provides low level access to all TensorFlow operations. Exp() is used to find element wise exponential of x.

For complex numbers
e^(x+iy) = e^x * e^iy = e^x * (cos y + i sin y)

Syntax: tf.raw_ops.Exp(x, name)

Parameters:

• x: It’s the input tensor. Allowed dtype for this tensor are bfloat16, half, float32, float64, complex64, complex128.
• name(optional): It’s defines the name for the operation.

Returns:  It returns a tensor of same dtype as x.

Note: It only takes keyword arguments.

Example 1:

Python3

 # Importing the libraryimport tensorflow as tf  # Initializing the input tensora = tf.constant([1, 2, 3, 4, 5], dtype = tf.float64)  # Printing the input tensorprint('Input: ', a)  # Calculating exponentialres = tf.raw_ops.Exp(x = a)  # Printing the resultprint('Result: ', res)

Output:

Input:  tf.Tensor([1. 2. 3. 4. 5.], shape=(5, ), dtype=float64)
Result:  tf.Tensor([  2.71828183   7.3890561   20.08553692  54.59815003 148.4131591 ], shape=(5, ), dtype=float64)

Example 2: Visualization

Python3

 # importing the libraryimport tensorflow as tfimport matplotlib.pyplot as plt  # Initializing the input tensora = tf.constant([1, 2, 3, 4, 5], dtype = tf.float64)  # Calculating exponentialres = tf.raw_ops.Exp(x = a)  # Plotting the graphplt.plot(a, res, color ='green')plt.title('tensorflow.raw_ops.Exp')plt.xlabel('Input')plt.ylabel('Result')plt.show()

Output:

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