我是机器学习的新手，并且发现难以使用Keras为TensorFlow掌握模型的训练。我正在尝试使用TensorFlow进行时间序列预测。我有一个生成器功能，可以生成训练数据和标签：
x_batch, y_batch = next(generator)
print(x_batch.shape)
print(y_batch.shape)
(256, 60, 9)
(256, 60, 3)
我通过以下方式构建模型：
model = Sequential()
model.add(LSTM(128, input_shape=(None, num_x_signals,), return_sequences=True))
model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(LSTM(128, return_sequences=True))
model.add(Dropout(0.1))
model.add(BatchNormalization())
model.add(LSTM(128))
model.add(Dropout(0.2))
model.add(BatchNormalization())
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_y_signals, activation='relu'))
opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)
# Compile model
model.compile(
loss='sparse_categorical_crossentropy',
optimizer=opt,
metrics=['accuracy']
)
我的模型摘要如下所示：
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_19 (LSTM) (None, None, 128) 70656
_________________________________________________________________
dropout_23 (Dropout) (None, None, 128) 0
_________________________________________________________________
batch_normalization_18 (Batc (None, None, 128) 512
_________________________________________________________________
lstm_20 (LSTM) (None, None, 128) 131584
_________________________________________________________________
dropout_24 (Dropout) (None, None, 128) 0
_________________________________________________________________
batch_normalization_19 (Batc (None, None, 128) 512
_________________________________________________________________
lstm_21 (LSTM) (None, 128) 131584
_________________________________________________________________
dropout_25 (Dropout) (None, 128) 0
_________________________________________________________________
batch_normalization_20 (Batc (None, 128) 512
_________________________________________________________________
dense_12 (Dense) (None, 32) 4128
_________________________________________________________________
dropout_26 (Dropout) (None, 32) 0
_________________________________________________________________
dense_13 (Dense) (None, 3) 99
=================================================================
Total params: 339,587
Trainable params: 338,819
Non-trainable params: 768
这是我尝试训练模型的方式：
tensorboard = TensorBoard(log_dir="logs/{}".format(NAME))
filepath = "RNN_Final-{epoch:02d}-{val_acc:.3f}" # unique file name that will include the epoch and the validation acc for that epoch
checkpoint = ModelCheckpoint("models/{}.model".format(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')) # saves only the best ones
# Train model
history = model.fit_generator(
generator=generator,
epochs=EPOCHS,
steps_per_epoch=100,
validation_data=validation_data,
callbacks=[tensorboard, checkpoint],
)
# Score model
score = model.evaluate(validation_x, validation_y, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# Save model
model.save("models/{}".format(NAME))
但是，当我尝试训练模型时，出现以下错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-67-f5263636596b> in <module>()
10 steps_per_epoch=100,
11 validation_data=validation_data,
---> 12 callbacks=[tensorboard, checkpoint],
13 )
14
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1777 use_multiprocessing=use_multiprocessing,
1778 shuffle=shuffle,
-> 1779 initial_epoch=initial_epoch)
1780
1781 def evaluate_generator(self,
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
134 'or `(val_x, val_y)`. Found: ' + str(validation_data))
135 val_x, val_y, val_sample_weights = model._standardize_user_data(
--> 136 val_x, val_y, val_sample_weight)
137 val_data = val_x + val_y + val_sample_weights
138 if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split)
915 feed_output_shapes,
916 check_batch_axis=False, # Don't enforce the batch size.
--> 917 exception_prefix='target')
918
919 # Generate sample-wise weight values given the `sample_weight` and
C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
180 ': expected ' + names[i] + ' to have ' +
181 str(len(shape)) + ' dimensions, but got array '
--> 182 'with shape ' + str(data_shape))
183 if not check_batch_axis:
184 data_shape = data_shape[1:]
ValueError: Error when checking target: expected dense_13 to have 2 dimensions, but got array with shape (1, 219, 3)
答案 0 :(得分：2)
正如@yhenon在注释部分中提到的那样，由于模型的每个时间步都有一些输出，因此对于最后一个LSTM层，也必须使用return_sequences=True。
但是，不清楚任务是什么（即分类或回归）。如果是分类任务，则必须使用'categorical_crossentropy'作为损失函数（而不是当前使用的'sparse_categorical_crossentropy'），并使用'softmax'作为最后一层的激活函数。
另一方面，如果这是一个回归任务，则需要使用诸如'mse'或'mae'之类的回归损失，并根据输出值正确设置最后一层的激活函数（例如，如果输出值的范围同时包含负数和正数，则使用'linear'。
答案 1 :(得分：-1)
在将输出作为密集层的输入之前，您需要先将其展平。