PyTorch is an open-source machine learning library developed by Facebook. It is used for deep neural network and natural language processing purposes.
The function torch.atan() provides support for the inverse tangent function in PyTorch. It gives the output in radian form. The input type is tensor and if the input contains more than one element, element-wise inverse tangent is computed.
Syntax: torch.atan(x, out=None)
Parameters:
x: Input tensor
name (optional): Output tensorReturn type: A tensor with the same type as that of x.
Code #1:
Python3
# Importing the PyTorch library import torch   # A constant tensor of size 6 a = torch.FloatTensor([1.0, -0.5, 3.4, 0.2, 0.0, -2]) print(a)   # Applying the inverse tan function and # storing the result in 'b' b = torch.atan(a) print(b) |
Output:
tensor([ 1.0000, -0.5000, 3.4000, 0.2000, 0.0000, -2.0000]) tensor([ 0.7854, -0.4636, 1.2847, 0.1974, 0.0000, -1.1071])
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Code #2: Visualization
Python3
# Importing the PyTorch library import torch   # 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 -5 to 5 a = np.linspace(-5, 5, 15)   # Applying the inverse tangent function and # storing the result in 'b' b = torch.atan(torch.FloatTensor(a))   print(b)   # Plotting plt.plot(a, b.numpy(), color = 'red', marker = "o") plt.title("torch.atan") plt.xlabel("X") plt.ylabel("Y")   plt.show() |
Output:
tensor([-1.3734, -1.3416, -1.2978, -1.2341, -1.1342, -0.9601, -0.6202, 0.0000,
0.6202, 0.9601, 1.1342, 1.2341, 1.2978, 1.3416, 1.3734])
