Keras实现将两个模型连接到一起

class AE:
 def __init__(self, dim, img_dim, batch_size):
  self.dim = dim
  self.img_dim = img_dim
  self.batch_size = batch_size
  self.encoder = self.encoder_construct()
  self.decoder = self.decoder_construct()
 
 def encoder_construct(self):
  x_in = Input(shape=(self.img_dim, self.img_dim, 3))
  x = x_in
  x = Conv2D(self.dim // 16, kernel_size=(5, 5), strides=(2, 2), padding='SAME')(x)
  x = BatchNormalization()(x)
  x = LeakyReLU(0.2)(x)
  x = Conv2D(self.dim // 8, kernel_size=(5, 5), strides=(2, 2), padding='SAME')(x)
  x = BatchNormalization()(x)
  x = LeakyReLU(0.2)(x)
  x = Conv2D(self.dim // 4, kernel_size=(5, 5), strides=(2, 2), padding='SAME')(x)
  x = BatchNormalization()(x)
  x = LeakyReLU(0.2)(x)
  x = Conv2D(self.dim // 2, kernel_size=(5, 5), strides=(2, 2), padding='SAME')(x)
  x = BatchNormalization()(x)
  x = LeakyReLU(0.2)(x)
  x = Conv2D(self.dim, kernel_size=(5, 5), strides=(2, 2), padding='SAME')(x)
  x = BatchNormalization()(x)
  x = LeakyReLU(0.2)(x)
  x = GlobalAveragePooling2D()(x)
  encoder = Model(x_in, x)
  return encoder
 
 def decoder_construct(self):
  map_size = K.int_shape(self.encoder.layers[-2].output)[1:-1]
  # print(type(map_size))
  z_in = Input(shape=K.int_shape(self.encoder.output)[1:])
  z = z_in
  z_dim = self.dim
  z = Dense(np.prod(map_size) * z_dim)(z)
  z = Reshape(map_size + (z_dim,))(z)
  z = Conv2DTranspose(z_dim // 2, kernel_size=(5, 5), strides=(2, 2), padding='SAME')(z)
  z = BatchNormalization()(z)
  z = Activation('relu')(z)
  z = Conv2DTranspose(z_dim // 4, kernel_size=(5, 5), strides=(2, 2), padding='SAME')(z)
  z = BatchNormalization()(z)
  z = Activation('relu')(z)
  z = Conv2DTranspose(z_dim // 8, kernel_size=(5, 5), strides=(2, 2), padding='SAME')(z)
  z = BatchNormalization()(z)
  z = Activation('relu')(z)
  z = Conv2DTranspose(z_dim // 16, kernel_size=(5, 5), strides=(2, 2), padding='SAME')(z)
  z = BatchNormalization()(z)
  z = Activation('relu')(z)
  z = Conv2DTranspose(3, kernel_size=(5, 5), strides=(2, 2), padding='SAME')(z)
  z = Activation('tanh')(z)
  decoder = Model(z_in, z)
  return decoder
 
 def build_ae(self):
  input_x = Input(shape=(self.img_dim, self.img_dim, 3))
  x = input_x
  for i in range(1, len(self.encoder.layers)):
   x = self.encoder.layers[i](x)
  for j in range(1, len(self.decoder.layers)):
   x = self.decoder.layers[j](x)
  y = x
  auto_encoder = Model(input_x, y)
  return auto_encoder

weights = np.array(model.get_weights())
print(weights)
print(weights[0].shape)
print(weights[1].shape)