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.cosh() provides support for the hyperbolic cosine function in PyTorch. It expects the input in radian form. The input type is tensor and if the input contains more than one element, element-wise hyperbolic cosine is computed.
Syntax: torch.cosh(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, -2.1, 0.0, -6.5]) print(a) # Applying the cosh function and # storing the result in 'b' b = torch.cosh(a) print(b) |
Output:
1.0000 -0.5000 3.4000 -2.1000 0.0000 -6.5000 [torch.FloatTensor of size 6] 1.5431 1.1276 14.9987 4.1443 1.0000 332.5716 [torch.FloatTensor of size 6]
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 hyperbolic cosine function and # storing the result in 'b' b = torch.cosh(torch.FloatTensor(a)) print(b) # Plotting plt.plot(a, b.numpy(), color = 'red', marker = "o") plt.title("torch.cosh") plt.xlabel("X") plt.ylabel("Y") plt.show() |
Output:
1.5431 1.3904 1.2661 1.1678 1.0933 1.0411 1.0102 1.0000 1.0102 1.0411 1.0933 1.1678 1.2661 1.3904 1.5431 [torch.FloatTensor of size 15]
