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.acos() provides support for the inverse cosine function in PyTorch. 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: torch.acos(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 cos function and # storing the result in 'b' b = torch.acos(a) print(b) |
Output:
tensor([ 1.0000, -0.5000, 3.4000, 0.2000, 0.0000, -2.0000]) tensor([0.0000, 2.0944, nan, 1.3694, 1.5708, nan])
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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 -1 to 1 a = np.linspace(-1, 1, 15)   # Applying the inverse cosine function and # storing the result in 'b' b = torch.acos(torch.FloatTensor(a))   print(b)   # Plotting plt.plot(a, b.numpy(), color = 'red', marker = "o") plt.title("torch.acos") plt.xlabel("X") plt.ylabel("Y")   plt.show() |
Output:
tensor([3.1416, 2.6005, 2.3664, 2.1790, 2.0137, 1.8605, 1.7141, 1.5708, 1.4274,
1.2810, 1.1279, 0.9626, 0.7752, 0.5411, 0.0000])
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