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Minor adjustments to the documentation #156

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Minor adjustments to the documentation #156

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9 changes: 5 additions & 4 deletions ML/Pytorch/Basics/pytorch_simple_fullynet.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,9 @@
"""
A simple walkthrough of how to code a fully connected neural network
using the PyTorch library. For demonstration we train it on the very
common MNIST dataset of handwritten digits. In this code we go through
how to create the network as well as initialize a loss function, optimizer,
using the PyTorch library. This code is designed to solve a classic problem in machine learning: recognizing handwritten digits.
We use the MNIST dataset, which contains images of handwritten digits from 0 to 9.
The goal is to train a neural network to recognize these digits with high accuracy.
In this code we go through how to create the network as well as initialize a loss function, optimizer,
check accuracy and more.

Programmed by Aladdin Persson
Expand All @@ -22,7 +23,7 @@
from torch.utils.data import (
DataLoader,
) # Gives easier dataset managment by creating mini batches etc.
from tqdm import tqdm # For nice progress bar!
from tqdm import tqdm # For nice progress bar! (Type "pip install tqdm" in your Terminal if u don't have the module to install it)

# Here we create our simple neural network. For more details here we are subclassing and
# inheriting from nn.Module, this is the most general way to create your networks and
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