不使用pytorch自带的dataloader加载cifar10的代码

以下是使用TensorFlow加载CIFAR-10数据集的示例代码：

import tensorflow as tf
import numpy as np
import os

def unpickle(file):
    import pickle
    with open(file, 'rb') as fo:
        dict = pickle.load(fo, encoding='bytes')
    return dict

def load_data(data_dir):
    train_data = np.empty((50000, 32, 32, 3), dtype='uint8')
    train_labels = np.empty((50000,), dtype='uint8')
    for i in range(1, 6):
        data_dict = unpickle(os.path.join(data_dir, 'data_batch_%d' % i))
        train_data[(i - 1) * 10000:i * 10000, :, :, :] = data_dict[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1)
        train_labels[(i - 1) * 10000:i * 10000] = data_dict[b'labels']
    test_data_dict = unpickle(os.path.join(data_dir, 'test_batch'))
    test_data = test_data_dict[b'data'].reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1)
    test_labels = np.array(test_data_dict[b'labels'])
    return train_data, train_labels, test_data, test_labels

data_dir = '/path/to/cifar-10-batches-py'
train_data, train_labels, test_data, test_labels = load_data(data_dir)

# Normalize the data
train_data = train_data.astype('float32') / 255
test_data = test_data.astype('float32') / 255

# Convert labels to one-hot encoding
train_labels = tf.keras.utils.to_categorical(train_labels, num_classes=10)
test_labels = tf.keras.utils.to_categorical(test_labels, num_classes=10)

# Define the model
model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
model.fit(train_data, train_labels, epochs=10, batch_size=64, validation_data=(test_data, test_labels))

这段代码使用了NumPy和TensorFlow库来加载CIFAR-10数据集，并使用卷积神经网络对图像进行分类。