From 73f8c867b754b9c074e54cc1068c02247acfe06d Mon Sep 17 00:00:00 2001 From: Kshitij Date: Mon, 4 May 2026 16:27:06 +0530 Subject: [PATCH] fix: remove function for loading dataset locally and add basic lines of code to load csv instead for practical 3b. --- Codes/Code-3b.md | 35 +++++++---------------------------- 1 file changed, 7 insertions(+), 28 deletions(-) diff --git a/Codes/Code-3b.md b/Codes/Code-3b.md index 34e62d9..ee7508c 100644 --- a/Codes/Code-3b.md +++ b/Codes/Code-3b.md @@ -38,34 +38,13 @@ from sklearn.metrics import confusion_matrix, classification_report # Fashion MNIST is built into Keras, downloads automatically on first run (X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data() -''' -import numpy as np -import gzip -import os - -def load_fashion_mnist(path): - """Load Fashion MNIST from local .gz files (Kaggle Zalando format).""" - files = { - 'X_train': 'train-images-idx3-ubyte.gz', - 'y_train': 'train-labels-idx1-ubyte.gz', - 'X_test': 't10k-images-idx3-ubyte.gz', - 'y_test': 't10k-labels-idx1-ubyte.gz', - } - - with gzip.open(os.path.join(path, files['X_train'])) as f: - X_train = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28) - with gzip.open(os.path.join(path, files['y_train'])) as f: - y_train = np.frombuffer(f.read(), np.uint8, offset=8) - with gzip.open(os.path.join(path, files['X_test'])) as f: - X_test = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28) - with gzip.open(os.path.join(path, files['y_test'])) as f: - y_test = np.frombuffer(f.read(), np.uint8, offset=8) - - return (X_train, y_train), (X_test, y_test) - -# Replace the Keras load line with: -(X_train, y_train), (X_test, y_test) = load_fashion_mnist('./fashion-mnist/') -''' +# import pandas as pd +# train_df = pd.read_csv('fashion-mnist_train.csv') +# test_df = pd.read_csv('fashion-mnist_test.csv') +# y_train = train_df['label'].values +# y_test = test_df['label'].values +# X_train = train_df.drop('label', axis=1).values.reshape(-1, 28, 28) # unflatten pixels to 28x28 +# X_test = test_df.drop('label', axis=1).values.reshape(-1, 28, 28) print("Training set shape:", X_train.shape) # (60000, 28, 28) print("Test set shape: ", X_test.shape) # (10000, 28, 28)