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fix: remove function for loading dataset locally and add basic lines of code to load csv instead for practical 3b.

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2026-05-04 16:27:06 +05:30
parent a201069006
commit 73f8c867b7
1 changed files with 7 additions and 28 deletions
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Codes/Code-3b.md
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@@ -38,34 +38,13 @@ from sklearn.metrics import confusion_matrix, classification_report
# Fashion MNIST is built into Keras, downloads automatically on first run # 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() (X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
''' # import pandas as pd
import numpy as np # train_df = pd.read_csv('fashion-mnist_train.csv')
import gzip # test_df = pd.read_csv('fashion-mnist_test.csv')
import os # y_train = train_df['label'].values
# y_test = test_df['label'].values
def load_fashion_mnist(path): # X_train = train_df.drop('label', axis=1).values.reshape(-1, 28, 28) # unflatten pixels to 28x28
"""Load Fashion MNIST from local .gz files (Kaggle Zalando format).""" # X_test = test_df.drop('label', axis=1).values.reshape(-1, 28, 28)
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/')
'''
print("Training set shape:", X_train.shape) # (60000, 28, 28) print("Training set shape:", X_train.shape) # (60000, 28, 28)
print("Test set shape: ", X_test.shape) # (10000, 28, 28) print("Test set shape: ", X_test.shape) # (10000, 28, 28)