我一直遇到这个错误，我不确定如何解决。我知道我的输入期望3维，但是我的形状是4维。有人可以指导我如何缩小范围吗？我已经读过3维的数组，数组的形状小于3个左右。但是我还没有看到更大的形状和维的溢出解决方案...
def generator(batch_size,from_list_x,from_list_y):
assert len(from_list_x) == len(from_list_y)
total_size = len(from_list_x)
while True: #keras generators should be infinite
for i in range(0,total_size,batch_size):
yield np.array(from_list_x[i:i+batch_size]), np.array(from_list_y[i:i+batch_size])
model = Sequential()
model.add(LSTM(1, input_shape=(1,16),return_sequences=True))
model.add(Flatten())
model.add(Dense(1, activation='tanh'))
model.compile(loss='mae', optimizer='adam', metrics=['accuracy'])
model.summary()
# fit network
pyplot.figure(figsize=(16, 25))
# for i in range(len(train)):
history = model.fit_generator(generator(8, train_X, train_Y), epochs=20, steps_per_epoch = 7, verbose=0, shuffle=True)
print('loss ', str(i), history.history['loss'][len(history.history['loss'])-1],'\n')
此行出现错误：
history = model.fit_generator(generator(8, train_X, train_Y), epochs=20, steps_per_epoch = 7, verbose=0, shuffle=True)
跟踪：
ValueError
Traceback (most recent call last)
<ipython-input-41-17bb96b50588> in <module>()
2 pyplot.figure(figsize=(16, 25))
3 # for i in range(len(train)):
----> 4 history = model.fit_generator(generator(8, train_X, train_Y), epochs=20, steps_per_epoch = 7, verbose=0, shuffle=True)
5
6 # plot history
C:\ProgramData\Anaconda3\envs\dh\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name +
90 '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
C:\ProgramData\Anaconda3\envs\dh\lib\site-packages\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)
1424 use_multiprocessing=use_multiprocessing,
1425 shuffle=shuffle,
-> 1426 initial_epoch=initial_epoch)
1427
1428 @interfaces.legacy_generator_methods_support
C:\ProgramData\Anaconda3\envs\dh\lib\site-packages\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)
189 outs = model.train_on_batch(x, y,
190 sample_weight=sample_weight,
--> 191 class_weight=class_weight)
192
193 if not isinstance(outs, list):
C:\ProgramData\Anaconda3\envs\dh\lib\site-packages\keras\engine\training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1212 x, y,
1213 sample_weight=sample_weight,
-> 1214 class_weight=class_weight)
1215 if self._uses_dynamic_learning_phase():
1216 ins = x + y + sample_weights + [1.]
C:\ProgramData\Anaconda3\envs\dh\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
752 feed_input_shapes,
753 check_batch_axis=False, # Don't enforce the batch size.
--> 754 exception_prefix='input')
755
756 if y is not None:
C:\ProgramData\Anaconda3\envs\dh\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
124 ': expected ' + names[i] + ' to have ' +
125 str(len(shape)) + ' dimensions, but got array '
--> 126 'with shape ' + str(data_shape))
127 if not check_batch_axis:
128 data_shape = data_shape[1:]
ValueError: Error when checking input: expected lstm_4_input to have 3 dimensions, but got array with shape (8, 23, 1, 16)
<Figure size 1152x1800 with 0 Axes>