From 4e10c5f96618f0e7b5e321d58dd7c384525ad56a Mon Sep 17 00:00:00 2001
From: widad174 <azzouzi.widad.17@gmail.com>
Date: Tue, 24 Oct 2023 14:55:07 +0200
Subject: [PATCH] fichier read_cifar.py
---
classifi1.ipynb | 20 -----------
read_cifar.py | 88 +++++++++++++++++++++++++++++++++++++++++++++++++
2 files changed, 88 insertions(+), 20 deletions(-)
delete mode 100644 classifi1.ipynb
create mode 100644 read_cifar.py
diff --git a/classifi1.ipynb b/classifi1.ipynb
deleted file mode 100644
index 9a1c163..0000000
--- a/classifi1.ipynb
+++ /dev/null
@@ -1,20 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np "
- ]
- }
- ],
- "metadata": {
- "language_info": {
- "name": "python"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/read_cifar.py b/read_cifar.py
new file mode 100644
index 0000000..ad16d24
--- /dev/null
+++ b/read_cifar.py
@@ -0,0 +1,88 @@
+import pickle
+import numpy as np
+
+#read_cifaar_batch
+def read_cifar_batch(file) :
+ with open(file, 'rb') as fo:
+ dictionnaire = pickle.load(fo, encoding='bytes')
+ data=dictionnaire[b'data'].astype(np.float32)
+ labels=np.array(dictionnaire[b'labels'],np.int64)
+ return data,labels
+'''
+EXPLICATION DE LA FONCTION:
+read_cifaar_batch function: read the path of a single batch.
+
+Arguments:
+- The path of a single batch as a string,
+
+Returns:
+- Matrix data of size (batch_size x data_size)
+- Vector labels of size batch_size
+
+The data must be np.float32 array and labels must be np.int64 array.
+'''
+
+
+
+
+# read_cifar
+
+def read_cifar(folder):
+ batch_file=["data_batch_1","data_batch_2","data_batch_3","data_batch_4","data_batch_5","test_batch"]
+ for i in range(len(batch_file)):
+ path= folder +'/'+batch_file[i]
+ if i==0:
+ data,labels=read_cifar_batch(path)
+ else:
+ x,y=read_cifar_batch(path)
+ data =np.vstack([data ,x]) # all data for all batches is in variable "data"
+ labels=np.hstack([labels,y]) # All labels for all batches is in variable "labels"
+ return data ,labels
+
+'''
+EXPLICATION DE LA FONCTION:
+read_cifaar function: read the path of the directory containing all batches (including test_batch).
+
+Arguments:
+- the path of the directory containing the six batches (five data_batch and one test_batch) as a string
+
+Returns:
+- Matrix data of size (batch_size x data_size)
+- Vector labels of size batch_size<
+
+The data must be np.float32 array and labels must be np.int64 array.
+'''
+
+
+# split_dataset
+
+def split_dataset(data,labels,split):
+ labels=labels.reshape(data.shape[0],1)
+ # Stack our Data and labels
+ con = np.hstack((data, labels))
+ k=int(split*con.shape[0])
+ # Shuffle all our Data stack it
+ np.random.shuffle(con)
+ # Train
+ X_train=con[:k,:-1]
+ y_train=np.array(con[:k,-1],np.int64)
+ # Test
+ X_test=con[k:,:-1]
+ y_test=np.array(con[k:,-1],np.int64)
+ return X_train,y_train,X_test,y_test
+
+'''
+EXPLICATION DE LA FONCTION:
+split_dataset function: splits the dataset into a training set and a test set.
+
+Arguments:
+- data and labels, two arrays that have the same size in the first dimension.
+- split, a float between 0 and 1 which determines the split factor of the training set with respect to the test set.
+
+Returns:
+- data_train: the training data,
+- labels_train: the corresponding labels,
+- data_test: the testing data, and
+- labels_test: the corresponding labels.
+'''
+
--
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