在pycharm平台使用Transformer结构写一段程序，基于seq2seq的对二值图像进行折叠处理，并对该代码添加详细的注释

以下是基于seq2seq的对二值图像进行折叠处理的Transformer代码，代码中注释详尽，希望对你有所帮助：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# 定义Transformer结构中的Multi-Head Attention层
class MultiHeadAttention(layers.Layer):
    def __init__(self, embed_dim, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        # 对输入的query、key、value进行线性变换，得到Q、K、V
        self.query_dense = layers.Dense(units=embed_dim)
        self.key_dense = layers.Dense(units=embed_dim)
        self.value_dense = layers.Dense(units=embed_dim)
        # 将多头注意力的输出进行线性变换，得到最终输出
        self.dense = layers.Dense(units=embed_dim)

    def split_heads(self, inputs, batch_size):
        inputs = tf.reshape(
            inputs, shape=(batch_size, -1, self.num_heads, self.embed_dim // self.num_heads)
        )
        return tf.transpose(inputs, perm=[0, 2, 1, 3])

    def call(self, inputs):
        query, key, value, mask = inputs['query'], inputs['key'], inputs['value'], inputs['mask']
        batch_size = tf.shape(query)[0]

        # 对query、key、value进行线性变换
        query = self.query_dense(query)
        key = self.key_dense(key)
        value = self.value_dense(value)

        # 对query、key、value进行分割
        query = self.split_heads(query, batch_size)
        key = self.split_heads(key, batch_size)
        value = self.split_heads(value, batch_size)

        # 计算注意力权重
        scaled_attention_logits = tf.matmul(query, key, transpose_b=True)
        scaled_attention_logits += tf.cast((mask * -1e9), dtype=scaled_attention_logits.dtype)
        attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
        attention_output = tf.matmul(attention_weights, value)

        # 合并多头注意力的输出
        attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
        concat_attention = tf.reshape(attention_output, (batch_size, -1, self.embed_dim))
        outputs = self.dense(concat_attention)

        return outputs

# 定义Transformer结构中的Positional Encoding层
class PositionalEncoding(layers.Layer):
    def __init__(self, position, embed_dim):
        super(PositionalEncoding, self).__init__()
        self.position = position
        self.embed_dim = embed_dim
        self.pos_encoding = self.positional_encoding(position, embed_dim)

    def get_angles(self, pos, i, embed_dim):
        angle_rates = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(embed_dim, tf.float32))
        return pos * angle_rates

    def positional_encoding(self, position, embed_dim):
        angle_rads = self.get_angles(
            tf.range(position, dtype=tf.float32)[:, tf.newaxis],
            tf.range(embed_dim, dtype=tf.float32)[tf.newaxis, :],
            embed_dim,
        )

        # 将sin应用于偶数索引（从0开始），将cos应用于奇数索引（从1开始）
        sines = tf.math.sin(angle_rads[:, 0::2])
        cosines = tf.math.cos(angle_rads[:, 1::2])

        pos_encoding = tf.concat([sines, cosines], axis=-1)
        pos_encoding = pos_encoding[tf.newaxis, ...]
        return tf.cast(pos_encoding, tf.float32)

    def call(self, inputs):
        return inputs + self.pos_encoding[:, :tf.shape(inputs)[1], :]

# 定义Transformer结构中的Encoder层
def encoder_layer(units, d_model, num_heads, dropout, name="encoder_layer"):
    inputs = keras.Input(shape=(None, d_model), name="inputs")
    # 为了保证模型的学习能力，需要在输入层和输出层之间添加多头注意力层和前向传递层
    attention = MultiHeadAttention(d_model, num_heads)(inputs={"query": inputs, "key": inputs, "value": inputs})
    attention = layers.Dropout(rate=dropout)(attention)
    attention = layers.LayerNormalization(epsilon=1e-6)(inputs + attention)
    outputs = layers.Dense(units=units, activation="relu")(attention)
    outputs = layers.Dense(units=d_model)(outputs)
    outputs = layers.Dropout(rate=dropout)(outputs)
    outputs = layers.LayerNormalization(epsilon=1e-6)(attention + outputs)
    return keras.Model(inputs=inputs, outputs=outputs, name=name)

# 定义Transformer结构中的Decoder层
def decoder_layer(units, d_model, num_heads, dropout, name="decoder_layer"):
    inputs = keras.Input(shape=(None, d_model), name="inputs")
    enc_outputs = keras.Input(shape=(None, d_model), name="encoder_outputs")
    # 为了保证模型的学习能力，需要在输入层和输出层之间添加多头注意力层和前向传递层，以及编码器-解码器注意力层
    attention1 = MultiHeadAttention(d_model, num_heads)(inputs={"query": inputs, "key": inputs, "value": inputs})
    attention1 = layers.Dropout(rate=dropout)(attention1)
    attention1 = layers.LayerNormalization(epsilon=1e-6)(attention1 + inputs)

    attention2 = MultiHeadAttention(d_model, num_heads)(inputs={"query": attention1, "key": enc_outputs, "value": enc_outputs})
    attention2 = layers.Dropout(rate=dropout)(attention2)
    attention2 = layers.LayerNormalization(epsilon=1e-6)(attention2 + attention1)

    outputs = layers.Dense(units=units, activation="relu")(attention2)
    outputs = layers.Dense(units=d_model)(outputs)
    outputs = layers.Dropout(rate=dropout)(outputs)
    outputs = layers.LayerNormalization(epsilon=1e-6)(outputs + attention2)
    return keras.Model(inputs=[inputs, enc_outputs], outputs=outputs, name=name)

# 定义Transformer结构
def transformer(vocab_size, num_layers, units, d_model, num_heads, dropout, name="transformer"):
    inputs = keras.Input(shape=(None,), name="inputs")
    # 定义掩码，用于将填充的部分掩盖掉
    padding_mask = keras.layers.Lambda(lambda x: tf.cast(tf.equal(x, 0), dtype=tf.float32))(inputs)
    # 嵌入层，将输入的单词ID转换为对应的向量表示
    embeddings = layers.Embedding(vocab_size, d_model)(inputs)
    embeddings *= tf.math.sqrt(tf.cast(d_model, tf.float32))
    embeddings = PositionalEncoding(vocab_size, d_model)(embeddings)

    outputs = layers.Dropout(rate=dropout)(embeddings)

    # 定义多个Encoder层
    for i in range(num_layers):
        outputs = encoder_layer(units=units, d_model=d_model, num_heads=num_heads, dropout=dropout, name="encoder_layer_{}".format(i),)(outputs=[outputs, padding_mask])

    # 定义多个Decoder层
    for i in range(num_layers):
        outputs = decoder_layer(units=units, d_model=d_model, num_heads=num_heads, dropout=dropout, name="decoder_layer_{}".format(i),)(inputs=[outputs, padding_mask])

    # 添加一个全连接层，输出二值图像折叠后的结果
    outputs = layers.Dense(units=vocab_size, activation="softmax")(outputs)
    return keras.Model(inputs=inputs, outputs=outputs, name=name)