是否可以在 Keras/TensorFlow 中提取多輸入 model 的一部分？

Question

我有這個多輸入網絡：

def build_model():
  inputRGB = tf.keras.Input(shape=(128,128,3), name='train_ds')
  inputFixed = tf.keras.Input(shape=(128,128,3), name='fixed_ds')
  inputDinamic = tf.keras.Input(shape=(128,128,3), name='dinamic_ds')

    # rete per Immagini RGB
  rgb = models.Sequential()  
  rgb = layers.Conv2D(32, (5, 5), padding='SAME')(inputRGB)
  rgb = layers.PReLU()(rgb)
  rgb = layers.MaxPooling2D((2, 2))(rgb)
  rgb = layers.BatchNormalization()(rgb)
  rgb = layers.Conv2D(64, (3, 3))(rgb)
  rgb = layers.PReLU()(rgb)
  rgb = layers.Conv2D(64, (3, 3))(rgb)
  rgb = layers.PReLU()(rgb)
  rgb = layers.Conv2D(64, (3, 3))(rgb)
  rgb = layers.PReLU()(rgb)
  rgb = layers.Dropout(0.5)(rgb)
  rgb = layers.GlobalAvgPool2D()(rgb)
  rgb = Model(inputs = inputRGB, outputs=rgb)

    # rete per Density Map con "Pallini"
  fixed = models.Sequential()
  fixed = layers.Conv2D(32, (5, 5), padding='SAME')(inputFixed)
  fixed = layers.PReLU()(fixed)
  fixed = layers.MaxPooling2D((2, 2))(fixed)
  fixed = layers.BatchNormalization()(fixed)
  fixed = layers.Conv2D(64, (3, 3))(fixed)
  fixed = layers.PReLU()(fixed)
  fixed = layers.Conv2D(64, (3, 3))(fixed)
  fixed = layers.PReLU()(fixed)
  fixed = layers.Conv2D(64, (3, 3))(fixed)
  fixed = layers.PReLU()(fixed)
  fixed = layers.Dropout(0.5)(fixed)
  fixed = layers.GlobalAvgPool2D()(fixed)
  fixed = Model(inputs = inputFixed, outputs=fixed)

    # rete per Density map per "assembramenti"
  dinamic = models.Sequential()  
  dinamic = layers.Conv2D(32, (5, 5), padding='SAME')(inputDinamic)
  dinamic = layers.PReLU()(dinamic)
  dinamic = layers.MaxPooling2D((2, 2))(dinamic)
  dinamic = layers.BatchNormalization()(dinamic)
  dinamic = layers.Conv2D(64, (3, 3))(dinamic)
  dinamic = layers.PReLU()(dinamic)
  dinamic = layers.Conv2D(64, (3, 3))(dinamic)
  dinamic = layers.PReLU()(dinamic)
  dinamic = layers.Conv2D(64, (3, 3))(dinamic)
  dinamic = layers.PReLU()(dinamic)
  dinamic = layers.Dropout(0.5)(dinamic)
  dinamic = layers.GlobalAvgPool2D()(dinamic)
  dinamic = Model(inputs = inputDinamic, outputs=dinamic)

  concat = layers.concatenate([rgb.output, fixed.output, dinamic.output])  # merge the outputs of the two models
  k = layers.Dense(1)(concat)

  modelFinal = Model(inputs={'train_ds':inputRGB, 'fixed_ds':inputFixed, 'dinamic_ds':inputDinamic}, outputs=[k])

  opt = tf.keras.optimizers.Adam(learning_rate=0.001, amsgrad=False)


  modelFinal.compile(optimizer=opt , loss='mae', metrics=['mae'])
  return modelFinal

我想從最好的 model 中提取，我已經使用以下代碼行保存並重新加載：

best_model = tf.keras.models.load_model(checkpoint_path + 'regression_count_128.30-1.11.hdf5')

只是前面展示的多輸入神經網絡的第一部分。 具體來說，我想提取將 RGB 圖像作為輸入的部分，以便僅在 RGB 測試圖像上測試 model（使用 3 種不同類型的圖像進行訓練）。

Answer 1

使用輸入層的名稱（即'train_ds' ）和 RGB 部分的 output 層的名稱（您尚未命名，但可以使用best_model.summary()找到它；它以'global_average_pooling2d'開頭）像這樣構造一個新的 model ：

rgb_model = Model(
      best_model.get_layer('train_ds').output,
      best_model.get_layer(name_of_rgb_output_layer).output
)

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旁注：行... = model.Sequential()是多余的，可以刪除，因為您正在使用 keras 的功能 API 來定義您的模型。