我在尝试对时间序列数据使用自编码器。当我在数据上使用填充时，一切正常，但当我使用可变长度数据时，会遇到一些小的数据形状问题： Incompatible shapes: [1,125,4] vs. [1,126,4]
input_series = Input(shape=(None, 4))x = Conv1D(4, 2, activation='relu', padding='same')(input_series)x = MaxPooling1D(1, padding='same')(x)x = Conv1D(4, 3, activation='relu', padding='same')(x)x = MaxPooling1D(1, padding='same')(x)x = Conv1D(4, 3, activation='relu', padding='same')(x)encoder = MaxPooling1D(1, padding='same', name='encoder')(x)x = Conv1D(4, 3, activation='relu', padding='same')(encoder)x = UpSampling1D(1)(x)x = Conv1D(4, 3, activation='relu', padding='same')(x)x = UpSampling1D(1)(x)x = Conv1D(16, 2, activation='relu')(x)x = UpSampling1D(1)(x)decoder = Conv1D(4, 2, activation='sigmoid', padding='same')(x)autoencoder = Model(input_series, decoder)autoencoder.compile(loss='mse', optimizer='adam')autoencoder.summary()摘要：
_________________________________________________________________Layer (type) Output Shape Param # =================================================================input_25 (InputLayer) (None, None, 4) 0 _________________________________________________________________conv1d_169 (Conv1D) (None, None, 4) 36 _________________________________________________________________max_pooling1d_49 (MaxPooling (None, None, 4) 0 _________________________________________________________________conv1d_170 (Conv1D) (None, None, 4) 52 _________________________________________________________________max_pooling1d_50 (MaxPooling (None, None, 4) 0 _________________________________________________________________conv1d_171 (Conv1D) (None, None, 4) 52 _________________________________________________________________encoder (MaxPooling1D) (None, None, 4) 0 _________________________________________________________________conv1d_172 (Conv1D) (None, None, 4) 52 _________________________________________________________________up_sampling1d_73 (UpSampling (None, None, 4) 0 _________________________________________________________________conv1d_173 (Conv1D) (None, None, 4) 52 _________________________________________________________________up_sampling1d_74 (UpSampling (None, None, 4) 0 _________________________________________________________________conv1d_174 (Conv1D) (None, None, 16) 144 _________________________________________________________________up_sampling1d_75 (UpSampling (None, None, 16) 0 _________________________________________________________________conv1d_175 (Conv1D) (None, None, 4) 132 =================================================================Total params: 520Trainable params: 520Non-trainable params: 0_________________________________________________________________错误：
Epoch 1/50---------------------------------------------------------------------------InvalidArgumentError Traceback (most recent call last)C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args) 1321 try:-> 1322 return fn(*args) 1323 except errors.OpError as e:C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata) 1306 return self._call_tf_sessionrun(-> 1307 options, feed_dict, fetch_list, target_list, run_metadata) 1308 C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata) 1408 self._session, options, feed_dict, fetch_list, target_list,-> 1409 run_metadata) 1410 else:InvalidArgumentError: Incompatible shapes: [1,125,4] vs. [1,126,4] [[Node: loss_22/conv1d_175_loss/sub = Sub[T=DT_FLOAT, _class=["loc:@training_18/Adam/gradients/loss_22/conv1d_175_loss/sub_grad/Reshape"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](conv1d_175/Sigmoid, _arg_conv1d_175_target_0_1/_4489)]] [[Node: loss_22/mul/_4613 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_1245_loss_22/mul", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]During handling of the above exception, another exception occurred:InvalidArgumentError Traceback (most recent call last)<ipython-input-101-a6e405699326> in <module>() 6 train_generator(X_train), 7 epochs=50,----> 8 steps_per_epoch=len(X_train)) 9 10 C:\ProgramData\Anaconda3\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 wrapperC:\ProgramData\Anaconda3\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) 2228 outs = self.train_on_batch(x, y, 2229 sample_weight=sample_weight,-> 2230 class_weight=class_weight) 2231 2232 if not isinstance(outs, list):C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py in train_on_batch(self, x, y, sample_weight, class_weight) 1881 ins = x + y + sample_weights 1882 self._make_train_function()-> 1883 outputs = self.train_function(ins) 1884 if len(outputs) == 1: 1885 return outputs[0]C:\ProgramData\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in __call__(self, inputs) 2480 session = get_session() 2481 updated = session.run(fetches=fetches, feed_dict=feed_dict,-> 2482 **self.session_kwargs) 2483 return updated[:len(self.outputs)] 2484 C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata) 898 try: 899 result = self._run(None, fetches, feed_dict, options_ptr,--> 900 run_metadata_ptr) 901 if run_metadata: 902 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata) 1133 if final_fetches or final_targets or (handle and feed_dict_tensor): 1134 results = self._do_run(handle, final_targets, final_fetches,-> 1135 feed_dict_tensor, options, run_metadata) 1136 else: 1137 results = []C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata) 1314 if handle is None: 1315 return self._do_call(_run_fn, feeds, fetches, targets, options,-> 1316 run_metadata) 1317 else: 1318 return self._do_call(_prun_fn, handle, feeds, fetches)C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args) 1333 except KeyError: 1334 pass-> 1335 raise type(e)(node_def, op, message) 1336 1337 def _extend_graph(self):InvalidArgumentError: Incompatible shapes: [1,125,4] vs. [1,126,4] [[Node: loss_22/conv1d_175_loss/sub = Sub[T=DT_FLOAT, _class=["loc:@training_18/Adam/gradients/loss_22/conv1d_175_loss/sub_grad/Reshape"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](conv1d_175/Sigmoid, _arg_conv1d_175_target_0_1/_4489)]] [[Node: loss_22/mul/_4613 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_1245_loss_22/mul", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]Caused by op 'loss_22/conv1d_175_loss/sub', defined at: File "C:\ProgramData\Anaconda3\lib\runpy.py", line 193, in _run_module_as_main "__main__", mod_spec) File "C:\ProgramData\Anaconda3\lib\runpy.py", line 85, in _run_code exec(code, run_globals) File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py", line 16, in <module> app.launch_new_instance() File "C:\ProgramData\Anaconda3\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance app.start() File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 478, in start self.io_loop.start() File "C:\ProgramData\Anaconda3\lib\site-packages\zmq\eventloop\ioloop.py", line 177, in start super(ZMQIOLoop, self).start() File "C:\ProgramData\Anaconda3\lib\site-packages\tornado\ioloop.py", line 888, in start handler_func(fd_obj, events) File "C:\ProgramData\Anaconda3\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper return fn(*args, **kwargs) File "C:\ProgramData\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events self._handle_recv() File "C:\ProgramData\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv self._run_callback(callback, msg) File "C:\ProgramData\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback callback(*args, **kwargs) File "C:\ProgramData\Anaconda3\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper return fn(*args, **kwargs) File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher return self.dispatch_shell(stream, msg) File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 233, in dispatch_shell handler(stream, idents, msg) File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request user_expressions, allow_stdin) File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 208, in do_execute res = shell.run_cell(code, store_history=store_history, silent=silent) File "C:\ProgramData\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 537, in run_cell return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2728, in run_cell interactivity=interactivity, compiler=compiler, result=result) File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2850, in run_ast_nodes if self.run_code(code, result): File "C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2910, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-100-ddd3b57d5f0b>", line 22, in <module> autoencoder.compile(loss='mse', optimizer='adam') File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 830, in compile sample_weight, mask) File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 429, in weighted score_array = fn(y_true, y_pred) File "C:\ProgramData\Anaconda3\lib\site-packages\keras\losses.py", line 14, in mean_squared_error return K.mean(K.square(y_pred - y_true), axis=-1) File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\math_ops.py", line 979, in binary_op_wrapper return func(x, y, name=name) File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 8582, in sub "Sub", x=x, y=y, name=name) File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper op_def=op_def) File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3392, in create_op op_def=op_def) File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1718, in __init__ self._traceback = self._graph._extract_stack() # pylint: disable=protected-accessInvalidArgumentError (see above for traceback): Incompatible shapes: [1,125,4] vs. [1,126,4] [[Node: loss_22/conv1d_175_loss/sub = Sub[T=DT_FLOAT, _class=["loc:@training_18/Adam/gradients/loss_22/conv1d_175_loss/sub_grad/Reshape"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](conv1d_175/Sigmoid, _arg_conv1d_175_target_0_1/_4489)]] [[Node: loss_22/mul/_4613 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_1245_loss_22/mul", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]回答：
你的一个 Conv1D 层没有使用 padding='same'。
但这里有一个非常奇怪的地方：为什么你要使用 MaxPooling 并且 pool_size=1？它没有任何作用。
现在假设你使用 pool_size=2，那么你仍然需要对输入进行填充，因为你需要输入的长度是8（2³）的倍数，才能在上采样后得到相同的形状。
对于可变长度的自编码器，这里有一个示例： Keras中的可变长度输出
实际上，LSTM层处理形状的方式与Conv1D层完全相同。
