如何使用CTC损失函数和数据集来监控准确性？(包含可运行代码)

import tensorflow as tf

# 定义模型
def create_model(input_shape, num_classes):
    model = tf.keras.models.Sequential([
        tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
        tf.keras.layers.MaxPooling2D((2, 2)),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(num_classes, activation='softmax')
    ])
    return model

# 定义CTC损失函数
def ctc_loss(labels, logits, label_length, logit_length):
    loss = tf.nn.ctc_loss(labels, logits, label_length, logit_length)
    return tf.reduce_mean(loss)

# 定义数据集
def create_dataset():
    # 假设数据集包含输入序列和对应标签
    inputs = ...
    labels = ...
    input_lengths = ...
    label_lengths = ...
    
    dataset = tf.data.Dataset.from_tensor_slices((inputs, labels, input_lengths, label_lengths))
    dataset = dataset.batch(batch_size)
    return dataset

# 训练模型
def train_model(model, dataset, num_epochs):
    optimizer = tf.keras.optimizers.Adam()
    for epoch in range(num_epochs):
        for inputs, labels, input_lengths, label_lengths in dataset:
            with tf.GradientTape() as tape:
                logits = model(inputs)
                loss = ctc_loss(labels, logits, label_lengths, input_lengths)
            
            gradients = tape.gradient(loss, model.trainable_variables)
            optimizer.apply_gradients(zip(gradients, model.trainable_variables))