From 05f5366a44f3d9a909586035e3e00a429b9a2427 Mon Sep 17 00:00:00 2001
From: Delorme Antonin <antonin.delorme@etu.ec-lyon.fr>
Date: Fri, 10 Nov 2023 19:38:05 +0000
Subject: [PATCH] Delete ReadMe.md
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-# Image classification
-
-Legrand Frédéric - Project for the Deep Learning Course at ECL
-
-# Usage
-
-To download and run the project, open a terminal in the desired folder and run these commands, they will:
-- Download the project
-- Enter the project folder
-- Add the data folder in the project folder
-- Install the necessary dependancies
-- Enter test folder
-- Test that the code is functionnal with pytest (All 8 tests should run without issues)
-
-```
-git clone https://gitlab.ec-lyon.fr/flegrand/image-classification.git
-cd image-classification
-pip install -r requirements.txt
-cd tests
-pytest
-```
-
-If all tests are succesful, the project has all the necessary dependancies and everything should run normally.
-
-# Description
-
-This section of the readme goes through all the code and explains it.
-
-## Prepare the CIFAR dataset
-
-All the code for this section is in the read_cifar.py file.
-Unit tests have been created in tests/test_read_cifar.py
-Running pytest in tests folder will provide proof that algorithms work as expected.
-
-### 1-
-
-Data folder contains the cifar-10-batches-py folder with the relevant data for the image classification.
-We focus on the cifar image dataset.
-
-### 2-
-
-read_cifar_batch takes in a file path and returns the corresponding data and labels
-We import the data as float32 values to be able to use numpy.
-Since values are between 0 and 256 for the data and between 0-10 for the labels we can import the labels as int64.
-
-```rb
-def read_cifar_batch(filePath: str):
- with open(filePath, 'rb') as fo:
- dict = pickle.load(fo, encoding='bytes')
- return (np.array(dict[b'data']).astype('float64'), np.array(dict[b'labels']).astype('int64'))
-```
-
-### 3-
-
-read_cifar takes in a folder path and returns the corresponding data and labels by making use of the previous read_cifar_batch function.
-
-```rb
-def read_cifar(folderPath: str):
- data, labels = read_cifar_batch(folderPath+"/test_batch")
- for i in range(1,6):
- tempData, tempLabels = read_cifar_batch(folderPath+"/data_batch_"+str(i))
- labels = np.concatenate((labels,tempLabels), axis=0)
- data = np.concatenate((data, tempData), axis=0)
- return data, labels
-```
-
-### 4-
-
-split_dataset takes in some numpy array that represents the data, some corresponding labels and a split pourcentage.
-
-The function finds a random permutation of these values with np.random.shuffle.
-We then mix both data and labels in the same way.
-
-We return the first elements so that we reach the split pourcentage we wanted.
-
-```rb
-def split_dataset(data: np.array, labels: list, split: float):
- if not 0 < split < 1:
- raise ValueError('Split is not a float between 0 and 1')
-
- # Generate random indices for shuffling the data
- num_samples = len(labels)
- indices = np.arange(num_samples)
- np.random.shuffle(indices)
-
- # Shuffle the data and labels using the random indices
- shuffled_data = data[indices]
- shuffled_labels = np.array(labels)[indices]
-
- # Calculate the split index based on the given split ratio
- split_index = int(split * num_samples)
-
- # Split the data and labels into training and testing sets
- data_train, data_test = shuffled_data[:split_index], shuffled_data[split_index:]
- labels_train, labels_test = shuffled_labels[:split_index], shuffled_labels[split_index:]
-
- return data_train, data_test, labels_train, labels_test
-```
-
-## k-nearest neighbors
-
-All the code for this section is in the knn.py file.
-Functions have unit tests in the tests folder
-Running pytest in tests folder will provide proof that algorithms work as expected.
-
-### 1-
-
-distance_matrix takes in two matrices which represents pictures.
-It returns the L2 Euclidian distance of each image in the first set to each image in the second set.
-
-Because of this the result is a numpy array of size: number_of_items_in_the_first_set * number_of_items_in_the_second_set
-
-```rb
-def distance_matrix(matrix1 : np.array, matrix2 : np.array):
- sum_of_squares_matrix1 = np.sum(np.square(matrix1), axis=1, keepdims=True)
- sum_of_squares_matrix2 = np.sum(np.square(matrix2), axis=1, keepdims=True)
-
- dot_product = np.dot(matrix1, matrix2.T)
-
- dists = np.sqrt(sum_of_squares_matrix1 + sum_of_squares_matrix2.T - 2 * dot_product)
- return dists
--
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