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# Python | Tensorflow asinh() method

• Last Updated : 10 Dec, 2018

Tensorflow is an open-source machine learning library developed by Google. One of its applications is to develop deep neural networks.

The module `tensorflow.math` provides support for many basic mathematical operations. Function `tf.asinh()` [alias `tf.math.asinh`] provides support for the inverse hyperbolic sine function in Tensorflow. The input type is tensor and if the input contains more than one element, element-wise inverse hyperbolic sine is computed.

Syntax: tf.asinh(x, name=None) or tf.math.asinh(x, name=None)

Parameters:
x: A tensor of any of the following types: float16, float32, float64, complex64, or complex128.
name (optional): The name for the operation.

Return type: A tensor with the same type as that of x.

Code #1:

 `# Importing the Tensorflow library``import` `tensorflow as tf``   ` `# A constant vector of size 6``a ``=` `tf.constant([``1.0``, ``-``0.5``, ``3.4``, ``22.1``, ``0.0``, ``-``6.5``],``                               ``dtype ``=` `tf.float32)``   ` `# Applying the asinh function and``# storing the result in 'b'``b ``=` `tf.asinh(a, name ``=``'asinh'``)``   ` `# Initiating a Tensorflow session``with tf.Session() as sess:``    ``print``(``'Input type:'``, a)``    ``print``(``'Input:'``, sess.run(a))``    ``print``(``'Return type:'``, b)``    ``print``(``'Output:'``, sess.run(b))`

Output:

```Input type: Tensor("Const_1:0", shape=(6, ), dtype=float32)
Input: [ 1.  -0.5  3.4 22.1  0.  -6.5]
Return type: Tensor("asinh:0", shape=(6, ), dtype=float32)
Output: [ 0.8813736  -0.48121184  1.9378793   3.7892363   0.         -2.5708146 ]
```

Code #2: Visualization

 `# Importing the Tensorflow library``import` `tensorflow as tf``  ` `# Importing the NumPy library``import` `numpy as np``  ` `# Importing the matplotlib.pylot function``import` `matplotlib.pyplot as plt``  ` `# A vector of size 15 with values from -10 to 10``a ``=` `np.linspace(``-``10``, ``10``, ``15``)``  ` `# Applying the inverse hyperbolic sine``# function and storing the result in 'b'``b ``=` `tf.asinh(a, name ``=``'asinh'``)``  ` `# Initiating a Tensorflow session``with tf.Session() as sess:``    ``print``(``'Input:'``, a)``    ``print``(``'Output:'``, sess.run(b))``    ``plt.plot(a, sess.run(b), color ``=` `'red'``, marker ``=` `"o"``) ``    ``plt.title(``"tensorflow.asinh"``) ``    ``plt.xlabel(``"X"``) ``    ``plt.ylabel(``"Y"``) ``  ` `    ``plt.show()`

Output:

```Input: [-10.          -8.57142857  -7.14285714  -5.71428571  -4.28571429
-2.85714286  -1.42857143   0.           1.42857143   2.85714286
4.28571429   5.71428571   7.14285714   8.57142857  10.        ]
Output: [-2.99822295 -2.84496713 -2.66412441 -2.44368627 -2.16177575 -1.77227614
-1.15447739  0.          1.15447739  1.77227614  2.16177575  2.44368627
2.66412441  2.84496713  2.99822295]
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