# Python – tensorflow.raw_ops.Exp()

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 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 library``import` `tensorflow as tf` `# Initializing the input tensor``a ``=` `tf.constant([``1``, ``2``, ``3``, ``4``, ``5``], dtype ``=` `tf.float64)` `# Printing the input tensor``print``(``'Input: '``, a)` `# Calculating exponential``res ``=` `tf.raw_ops.Exp(x ``=` `a)` `# Printing the result``print``(``'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 library``import` `tensorflow as tf``import` `matplotlib.pyplot as plt` `# Initializing the input tensor``a ``=` `tf.constant([``1``, ``2``, ``3``, ``4``, ``5``], dtype ``=` `tf.float64)` `# Calculating exponential``res ``=` `tf.raw_ops.Exp(x ``=` `a)` `# Plotting the graph``plt.plot(a, res, color ``=``'green'``)``plt.title(``'tensorflow.raw_ops.Exp'``)``plt.xlabel(``'Input'``)``plt.ylabel(``'Result'``)``plt.show()`

Output:

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