keras密集的模型值：逻辑和标签必须具有相同的形状（（无，200，1）vs（无，1，1））

Question

我是机器学习的新手，我正在尝试培训模型。 我正在使用这个keras oficial示例作为设置数据集并将其馈入模型的指南： https://www.tensorflow.org/api_docs/python/ppython/tf/keras/utils/sequence

从培训数据中，我有一个为单个列而创建的滑动窗口，我是为单个列创建的滑动窗口具有二进制分类（1或0）。

这是模型创建代码：

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'))

这是我用来编译模型的功能：

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

这是模型培训：

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)

这些是批处理的形状：

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

问题是（我想），从模型摘要中，该模型是模型当时应该在第二个维度工作时从最后一个维度进行训练：

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
_________________________________________________________________

因为那个我得到了value> valueerror：logits和labels必须具有相同的形状（（（无，200，1）vs（无，1，1，1，1 ）

如何告诉Keras在第二维度而不是最后一个维度应用培训？

edit

这是我了解正在发生的事情，对吗？我如何修复它？

编辑2

我尝试按建议进行修改，使用：

dense_model.add（dense（hisding_units，input_shape =（none，200,1）））），

但我会收到以下警告：

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

如您在输入层中指定的那样（输入形状为[batch_size，input_dim]，您可以看到

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

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