Delete read_cifar.py

read_cifar.py

-import numpy as np
-import pickle
-import os
-
-def read_cifar_batch(batch_path):
-    with open(batch_path, 'rb') as file:
-        # Load the batch data
-        batch_data = pickle.load(file, encoding='bytes')
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-    # Extract data and labels from the batch
-    data = batch_data[b'data']  # CIFAR-10 data
-    labels = batch_data[b'labels']  # Class labels
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-    # Convert data and labels to the desired data types
-    data = np.array(data, dtype=np.float32)
-    labels = np.array(labels, dtype=np.int64)
-
-    return data, labels
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-
-def read_cifar(directory_path):
-    data_batches = []
-    label_batches = []
-
-    # Iterate through the batch files in the directory
-    for batch_file in ['data_batch_1', 'data_batch_2', 'data_batch_3', 'data_batch_4', 'data_batch_5', 'test_batch']:
-        batch_path = os.path.join(directory_path, batch_file)
-
-        with open(batch_path, 'rb') as file:
-            # Load the batch data
-            batch_data = pickle.load(file, encoding='bytes')
-
-        # Extract data and labels from the batch
-        data = batch_data[b'data']  # CIFAR-10 data
-        labels = batch_data[b'labels']  # Class labels
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-        data_batches.append(data)
-        label_batches.extend(labels)
-
-    # Combine all batches into a single data matrix and label vector
-    data = np.concatenate(data_batches, axis=0)
-    labels = np.array(label_batches, dtype=np.int64)
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-    # Convert data to the desired data type
-    data = data.astype(np.float32)
-
-    return data, labels
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-def split_dataset(data, labels, split):
-    # Check if the split parameter is within the valid range (0 to 1)
-    if split < 0 or split > 1:
-        raise ValueError("Split must be a float between 0 and 1.")
-
-    # Get the number of samples in the dataset
-    num_samples = len(data)
-
-    # Calculate the number of samples for training and testing
-    num_train_samples = int(num_samples * split)
-    num_test_samples = num_samples - num_train_samples
-
-    # Create a random shuffle order for the indices
-    shuffle_indices = np.random.permutation(num_samples)
-
-    # Use the shuffled indices to split the data and labels
-    data_train = data[shuffle_indices[:num_train_samples]]
-    labels_train = labels[shuffle_indices[:num_train_samples]]
-    data_test = data[shuffle_indices[num_train_samples:]]
-    labels_test = labels[shuffle_indices[num_train_samples:]]
-
-    return data_train, labels_train, data_test, labels_test
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-if __name__ == '__main__':
-    batch_path = "data/cifar-10-python\cifar-10-batches-py\data_batch_1"  # Update with your path
-    data, labels = read_cifar_batch(batch_path)
-    print("Data shape:", data.shape)
-    print("Labels shape:", labels.shape)
-
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