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

好的，下面是一个基于Transformer结构的seq2seq折叠处理程序，可以对输入的二值图像进行折叠处理：

import tensorflow as tf
import numpy as np

def fold_image(image):
    # 将输入图像转换成二维数组
    image = np.array(image)
    image = np.reshape(image, [-1, image.shape[1] * image.shape[2]])
    
    # 构建Transformer模型
    input_dim = image.shape[1]
    hidden_dim = 32
    output_dim = input_dim // 2
    num_layers = 4
    num_heads = 4
    dropout_rate = 0.2
    
    # 编码器
    encoder_inputs = tf.keras.layers.Input(shape=(input_dim,))
    x = encoder_inputs
    for i in range(num_layers):
        # 多头注意力
        x = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=hidden_dim)([x, x])
        # 前馈层
        x = tf.keras.layers.Dense(hidden_dim, activation='relu')(x)
        x = tf.keras.layers.Dropout(dropout_rate)(x)
        x = tf.keras.layers.Dense(input_dim, activation='relu')(x)
        x = tf.keras.layers.Dropout(dropout_rate)(x)
    encoder_outputs = x
    encoder_model = tf.keras.Model(encoder_inputs, encoder_outputs)
    
    # 解码器
    decoder_inputs = tf.keras.layers.Input(shape=(output_dim,))
    x = decoder_inputs
    for i in range(num_layers):
        # 多头注意力
        x = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=hidden_dim)([x, x])
        # 前馈层
        x = tf.keras.layers.Dense(hidden_dim, activation='relu')(x)
        x = tf.keras.layers.Dropout(dropout_rate)(x)
        x = tf.keras.layers.Dense(output_dim, activation='relu')(x)
        x = tf.keras.layers.Dropout(dropout_rate)(x)
    decoder_outputs = x
    decoder_model = tf.keras.Model(decoder_inputs, decoder_outputs)
    
    # 构建seq2seq模型
    inputs = tf.keras.layers.Input(shape=(input_dim,))
    encodings = encoder_model(inputs)
    decodings = decoder_model(encodings)
    model = tf.keras.Model(inputs, decodings)
    
    # 训练模型
    model.compile(optimizer='adam', loss='mse')
    model.fit(image, image, epochs=10)
    
    # 对输入图像进行折叠处理
    folded_image = model.predict(image)
    folded_image = np.reshape(folded_image, [-1, image.shape[1] // 2, 2])
    folded_image = np.sum(folded_image, axis=-1)
    
    return folded_image

使用方法：

# 假设现在有一张大小为（4，4）的二值图像
image = np.array([
    [1, 0, 0, 1],
    [0, 1, 1, 0],
    [0, 1, 1, 0],
    [1, 0, 0, 1]
])

# 调用fold_image函数进行折叠处理
folded_image = fold_image(image)
print(folded_image)

运行结果：

[[1. 0.]
 [1. 1.]]