 # Python | Tensorflow tan () method

The `tensorflow.math ` module provides support for many basic mathematical operations. The ` tf.tan () ` [alias ` tf.math.tan `] function provides support for the tangent function in Tensorflow. Input in radians is expected. Input type — tensor, and if the input contains more than one element, the element-wise tangent is calculated.

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

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``` # Tensorflow library import import tensorflow as tf    # Constant vector of size 6 a = tf.constant ([ 1.0 , - 0.5 , 3.4 , - 2.1 , 0.0 , - 6.5 ], dtype = tf.float32)    # Applying the tan function and # save the result to & # 39; b & # 39; b = tf.tan (a, name = ` tan` )    # 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)) ```

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Output :

` Input type: T ensor ("Const: 0", shape = (6,), dtype = float32) Input: [1. -0.5 3.4 -2.1 0. -6.5] Return type: Tensor ("tan: 0", shape = (6, ), dtype = float32) Output: [1.5574077 -0.5463025 0.264317 1.7098469 0. -0.2202772] `

Code # 2: Rendering

 ` # Tensorflow library import ` ` import ` ` tensorflow as tf `   ` # Import NumPy library ` ` import ` ` numpy as np `   ` # Import matplotlib.pylot function ` ` import ` ` matplotlib. pyplot as plt `   ` # Vector p size 15 with values ​​from -1 to 1 ` ` a ` ` = ` ` np.linspace (` ` - ` ` 1 ` `, 1 , 15 ) ````   # Applying the tangent function and # save the result to & # 39; b & # 39; b = tf.tan (a, name = `tan` )   # Initiate a session Tensorflow with tf.Session () as sess: ```` print < code class = "plain"> (` ` `Input:` ` `, a) ` ` print ` ` (` ` `Output: `` `, sess.run (b)) ` ` ` ` plt.plot (a, sess.run (b), color ` ` = ` `` red` ` `, marker ` ` = ` ` "o" ` `) ` ` plt.title (` ` "tensorflow .tan "` `) ` ` ` ` plt. xlabel (` ` "X" ` `) ` ` plt.ylabel (` ` "Y" ` `) `   `  plt.show () ```` ```

Output :

` Input: [-1. -0.85714286 -0.71428571 -0.57142857 -0.42857143 -0.28571429 -0.14285714 0. 0.14285714 0.28571429 0.42857143 0.57142857 0.71428571 0.85714286 1.] Output: [-1.55740772 -1.15486601 -0.86700822 -0.64298589 -0.45689311 -0.29375136 -0.14383696 0. 0.14383696 0.29375136 0.45689311 0.64298589 0.86700822 1.15486601 1.55740772] `