如何使用函數式 API 重寫這個順序 API tensorflow 模型？

我正在嘗試使用功能 API 重寫我的工作順序模型。這是我的順序模型：

num_classes = 3
# Define a simple sequential model
def create_model():
  model = Sequential([
    layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
    layers.Conv2D(16, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(32, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(64, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dense(num_classes)
  ])
  
  model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
  
  return model
  
# Create a basic model instance
model = create_model()

# Display the model's architecture
model.summary()

時序模型的模型總結：

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
rescaling_2 (Rescaling)      (None, 180, 180, 3)       0
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 180, 180, 16)      448
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 90, 90, 16)        0
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 90, 90, 32)        4640
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 45, 45, 32)        0
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 45, 45, 64)        18496
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 22, 22, 64)        0
_________________________________________________________________
flatten_1 (Flatten)          (None, 30976)             0
_________________________________________________________________
dense_2 (Dense)              (None, 128)               3965056
_________________________________________________________________
dense_3 (Dense)              (None, 3)                 387
=================================================================
Total params: 3,989,027
Trainable params: 3,989,027
Non-trainable params: 0
_________________________________________________________________

這是我將其重寫為功能模型的嘗試。

num_classes = 3
input_shape=(img_height, img_width, 3)
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.models import Model

def create_model():
  model_input = Input(shape=input_shape) 
  # how to include preprocessing layer that I have in my sequential model: layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
  x = Conv2D(16, 3, activation='relu',padding='same')(model_input) 
  x = MaxPooling2D()(x) 
  x = Conv2D(32, 3, activation='relu',padding='same')(model_input) 
  x = MaxPooling2D()(x) 
  x = Conv2D(64, 3, activation='relu',padding='same')(model_input) 
  x = MaxPooling2D()(x) 
  x = Flatten()(x)
  outputs = Dense(num_classes, activation='relu')(x)

  model = Model(model_input, x,)

  model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

  return model

# Create a basic model instance
model = create_model()
 
# Display the model's architecture
model.summary()

keras.utils.plot_model(model, "model_with_shape_info.png", show_shapes=True)

功能模型模型匯總：

_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_10 (InputLayer)       [(None, 180, 180, 3)]     0         
                                                                 
 conv2d_23 (Conv2D)          (None, 180, 180, 64)      1792      
                                                                 
 max_pooling2d_23 (MaxPoolin  (None, 90, 90, 64)       0         
 g2D)                                                            
                                                                 
 flatten_4 (Flatten)         (None, 518400)            0         
                                                                 
=================================================================
Total params: 1,792
Trainable params: 1,792
Non-trainable params: 0
_________________________________________________________________

我試圖在我的順序模型中逐行確定要包含在我的功能模型中的層。你能幫我理解如何正確地將我的順序模型重寫為功能模型嗎？感謝您的幫助。

編輯：嘗試編譯和訓練功能模型。

model2.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

epochs=10
history = model2.fit(
  train_ds,
  validation_data=val_ds,
  epochs=epochs
)