Python | Tensorflow acos() method

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.acos() [alias tf.math.acos] provides support for the inverse cosine function in Tensorflow. It expects the input to be in the range [-1, 1] and gives the output in radian form. It returns nan if the input does not lie in the range [-1, 1]. The input type is tensor and if the input contains more than one element, element-wise inverse cosine is computed.

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

Parameters:
x: A tensor of any of the following types: bfloat16, half, float32, float64, int32, int64, complex64, or complex128.
name (optional): The name for the operation.

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

Code #1:

Python3

# Importing the Tensorflow library
import tensorflow as tf

# A constant vector of size 6
a = tf.constant([1.0, -0.5, 3.4, 0.2, 0.0, -2],
                            dtype = tf.float32)

# Applying the acos function and
# storing the result in 'b'
b = tf.acos(a, name ='acos')

# 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_7:0", shape=(6, ), dtype=float32)
Input: [ 1.  -0.5  3.4  0.2  0.  -2. ]
Return type: Tensor("acos:0", shape=(6, ), dtype=float32)
Output: [0.        2.0943952       nan 1.3694384 1.5707964       nan]

Code #2: Visualization

Python3

# Importing the Tensorflow library
import tensorflow as tf

# Importing the NumPy library
import numpy as np

# Importing the matplotlib.pyplot function
import matplotlib.pyplot as plt

# A vector of size 15 with values from -1 to 1
a = np.linspace(-1, 1, 15)

# Applying the inverse cosine function and
# storing the result in 'b'
b = tf.acos(a, name ='acos')

# 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.acos")
    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: [3.14159265 2.60049313 2.36639928 2.17904191 2.01370737 1.86054803
 1.7141439  1.57079633 1.42744876 1.28104463 1.12788528 0.96255075
 0.77519337 0.54109953 0.        ]