Version of Python

!python -V

Python 3.12.6

Import Required Packages

# Suppress warnings
import warnings
for warn in [UserWarning, FutureWarning]: warnings.filterwarnings("ignore", category = warn)

import os
import numpy as np
import torch
import torch.nn as nn
import pandas as pd
import jupyterlab as jlab

Versions of Required Libraries

packages = [
    "Torch", "NumPy", "Pandas", "JupyterLab",
]

package_objects = [
    torch, np, pd, jlab
]

versions = list(map(lambda obj: obj.__version__, package_objects))

pkgs = {"Package": packages, "Version": versions}
df_pkgs = pd.DataFrame(data = pkgs)
df_pkgs.index.name = "#"
df_pkgs.index += 1

display(df_pkgs)

path_to_reqs = "."
reqs_name = "requirements.txt"

def get_packages_and_versions():
    """Generate strings with libraries and their versions in the format: package==version"""
    
    for package, version in zip(packages, versions):
        yield f"{package.lower()}=={version}\n"

with open(os.path.join(path_to_reqs, reqs_name), "w", encoding = "utf-8") as f:
    f.writelines(get_packages_and_versions())

	Package	Version
#
1	Torch	2.2.2
2	NumPy	1.26.4
3	Pandas	2.2.3
4	JupyterLab	4.2.5

Example of a Dense Layer

# Determine the size of the input and output layers
input_size = 10
output_size = 5

# Create a random tensor
input = torch.randn(2, input_size)

# Create a Dense layer (fully connected)
dense = nn.Linear(in_features = input_size, out_features = output_size, bias = True)

# Apply a Dense layer to the input data
output = dense(input)

# Output of the resulting tensor after passing through the Dense layer
print("Resultant tensor after passing through the Dense layer:")
print(output)

# Output the size of the resulting tensor
print("Size of the resulting tensor:")
print(output.size())

Resultant tensor after passing through the Dense layer:
tensor([[ 0.0159, -0.3479,  0.3961,  0.3081, -0.5841],
        [-0.8313,  0.1779, -0.0857,  0.6841, -0.2625]],
       grad_fn=<AddmmBackward0>)
Size of the resulting tensor:
torch.Size([2, 5])