Keras 密集 Model ValueError：logits 和標簽必須具有相同的形狀 ((None, 200, 1) vs (None, 1, 1))

Question

我是機器學習的新手，我正在嘗試訓練 model。我使用這個 Keras 官方示例作為指南來設置我的數據集並將其輸入 model： https://www.tensorflow.org/api_docs /python/tf/keras/utils/序列

從訓練數據中，我為單列創建了一個滑動 windows，對於標簽，我有一個二進制分類（1 或 0）。

這是 model 創建代碼：

n = 200
hidden_units = n
dense_model = Sequential()
dense_model.add(Dense(hidden_units, input_shape=([200,1])))
dense_model.add(Activation('relu'))
dense_model.add(Dropout(dropout))
print(hidden_units)

while hidden_units > 2:
    hidden_units = math.ceil(hidden_units/2)
    dense_model.add(Dense(hidden_units))
    dense_model.add(Activation('relu'))
    dense_model.add(Dropout(dropout))
    print(hidden_units)
dense_model.add(Dense(units = 1, activation='sigmoid'))

這是我用來編譯 model 的函數：

def compile_and_fit(model, window, epochs, patience=2):
    early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',
                                                      patience=patience,
                                                      mode='min')
    model.compile(loss='binary_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    model.fit(window.train , epochs=epochs)
    return history

這是 model 訓練：

break_batchs = find_gaps(df_train, 'date_diff', diff_int_value)
for keys, values in break_batchs.items():
    dense_window = WindowGenerator(data=df_train['price_var'],
                                   data_validation=df_validation['price_var'],
                                   data_test=df_test['price_var'],
                                   input_width=n,
                                   shift=m,
                                   start_index=values[0],
                                   end_index=values[1], 
                                   class_labels=y_buy,
                                   class_labels_train=y_buy_train,
                                   class_labels_test=y_buy_test,
                                   label_width=1,
                                   label_columns=None,
                                   classification=True,
                                   batch_size=batch_size,
                                   seed=None)
    history = compile_and_fit(dense_model, dense_window)

這些是批次的形狀：

(TensorSpec(shape=(None, 200, 1), dtype=tf.float32, name=None), TensorSpec(shape=(None, 1, 1), dtype=tf.float64, name=None))

問題是（我猜），從 model 總結來看，model 是從最后一個維度訓練的，而它應該在第二個維度中工作：

dense_model.summary()

Model: "sequential_21"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
                                          |Model is being applied here
                                          |
                                          v
dense_232 (Dense)            (None, 200, 200)          400       
_________________________________________________________________
                                     |When it should be applied here
                                     |
                                     v
activation_225 (Activation)  (None, 200, 200)          0         
_________________________________________________________________
dropout_211 (Dropout)        (None, 200, 200)          0         
_________________________________________________________________
dense_233 (Dense)            (None, 200, 100)          20100     
_________________________________________________________________
activation_226 (Activation)  (None, 200, 100)          0         
_________________________________________________________________
dropout_212 (Dropout)        (None, 200, 100)          0         
_________________________________________________________________
dense_234 (Dense)            (None, 200, 50)           5050      
_________________________________________________________________
activation_227 (Activation)  (None, 200, 50)           0         
_________________________________________________________________
dropout_213 (Dropout)        (None, 200, 50)           0         
_________________________________________________________________
dense_235 (Dense)            (None, 200, 25)           1275      
_________________________________________________________________
activation_228 (Activation)  (None, 200, 25)           0         
_________________________________________________________________
dropout_214 (Dropout)        (None, 200, 25)           0         
_________________________________________________________________
dense_236 (Dense)            (None, 200, 13)           338       
_________________________________________________________________
activation_229 (Activation)  (None, 200, 13)           0         
_________________________________________________________________
dropout_215 (Dropout)        (None, 200, 13)           0         
_________________________________________________________________
dense_237 (Dense)            (None, 200, 7)            98        
_________________________________________________________________
activation_230 (Activation)  (None, 200, 7)            0         
_________________________________________________________________
dropout_216 (Dropout)        (None, 200, 7)            0         
_________________________________________________________________
dense_238 (Dense)            (None, 200, 4)            32        
_________________________________________________________________
activation_231 (Activation)  (None, 200, 4)            0         
_________________________________________________________________
dropout_217 (Dropout)        (None, 200, 4)            0         
_________________________________________________________________
dense_239 (Dense)            (None, 200, 2)            10        
_________________________________________________________________
activation_232 (Activation)  (None, 200, 2)            0         
_________________________________________________________________
dropout_218 (Dropout)        (None, 200, 2)            0         
_________________________________________________________________
dense_240 (Dense)            (None, 200, 1)            3         
=================================================================
Total params: 27,306
Trainable params: 27,306
Non-trainable params: 0
_________________________________________________________________

正因為如此，我得到了ValueError: logits and labels must have the same shape ((None, 200, 1) vs (None, 1, 1))

我如何告訴 Keras 在第二個維度而不是最后一個維度應用訓練？

編輯

這就是我所理解的正在發生的事情，對嗎？ 我是怎么修好的？

編輯 2

我嘗試按照建議進行修改，使用：

dense_model.add(Dense(hidden_units, input_shape=(None,200,1)))

但我收到以下警告：

WARNING:tensorflow:Model was constructed with shape (None, None, 200, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, None, 200, 1), dtype=tf.float32, name='dense_315_input'), name='dense_315_input', description="created by layer 'dense_315_input'"), but it was called on an input with incompatible shape (None, 200, 1, 1).

Answer 1

您指向的第一個維度是批量大小，正如您在輸入層中指定的那樣（輸入形狀是[batch_size, input_dim]可以在這里看到

dense_model.add(Dense(hidden_units, input_shape=([200,1])))

因此，您的 model 正在輸出 200 個值，因為您的批量大小為 200，但您要比較的目標 label 只有一個值。