PNEUMONIA DETECTION USING X-RAY IMAGES

*This notebook is used for ZERO to GANs Online course fainal Project
Here we are applying CNN on datasets

System Setup

This tutorial takes a code-first approach, and you should try to follow along by running and experimenting with the code yourself. The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Kaggle". This will run the notebook on Kaggle, a free online service for running Jupyter notebooks (you might need to create an account).

Running on your computer locally

(Skip this if you are running on Kaggle) You can clone this notebook, install the required dependencies using conda, and start Jupyter by running the following commands on the terminal:

pip install jovian --upgrade                # Install the jovian library 
jovian clone aakashns/04-feedforward-nn     # Download notebook
cd 04-feedforward-nn                        # Enter the created directory 
conda create -n 04-feedfoward-nn python=3.8 # Create a conda environment
conda activate 04-feedforward-nn            # Activate virtual environment
conda install jupyter                       # Install Jupyter
jupyter notebook                            # Start Jupyter

On older versions of conda, you might need to run source activate 04-feedfoward-nn to activate the virtual environment. For a more detailed explanation of the above steps, check out the System setup section in the first notebook.

Preparing the Data

We begin by importing the required modules & classes.

# Uncomment and run the commands below if imports fail
# !conda install numpy pandas pytorch torchvision cpuonly -c pytorch -y
# !pip install matplotlib --upgrade --quiet

import os
import torch
import torchvision
import numpy as np
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
from torchvision.utils import make_grid
from torch.utils.data.dataloader import DataLoader
from torch.utils.data import random_split
from torchvision.datasets import ImageFolder
import torchvision.transforms as T
%matplotlib inline