如何将EMNIST数据加载到Tensorflow

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

data_path = 'path/to/emnist/dataset/'
train_images_file = data_path + 'emnist-byclass-train-images-idx3-ubyte'
train_labels_file = data_path + 'emnist-byclass-train-labels-idx1-ubyte'
test_images_file = data_path + 'emnist-byclass-test-images-idx3-ubyte'
test_labels_file = data_path + 'emnist-byclass-test-labels-idx1-ubyte'

def load_emnist_images(file_path):
    return tf.data.FixedLengthRecordDataset(file_path, 28 * 28, header_bytes=16).map(
        lambda s: tf.reshape(tf.io.decode_raw(s, tf.uint8), (28, 28, 1))
    )

def load_emnist_labels(file_path):
    return tf.data.FixedLengthRecordDataset(file_path, 1, header_bytes=8).map(
        lambda s: tf.reshape(tf.io.decode_raw(s, tf.uint8), ())
    )

train_images = load_emnist_images(train_images_file)
train_labels = load_emnist_labels(train_labels_file)
test_images = load_emnist_images(test_images_file)
test_labels = load_emnist_labels(test_labels_file)

train_images = train_images / 255.0
test_images = test_images / 255.0

plt.figure(figsize=(10, 10))
for i, (image, label) in enumerate(zip(train_images.take(25), train_labels.take(25))):
    plt.subplot(5, 5, i + 1)
    plt.imshow(image[:, :, 0], cmap='gray')
    plt.title(chr(label + 96))
    plt.axis('off')
plt.show()

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(26, activation='softmax')
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(),
              metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))