Keras 中的 Convolutional1D 在时间步而不是特征上进行卷积？
Convolutional1D in Keras convolves on time steps instead of features?
因此，我的输入的形状为 (1, 893, 463)，或者更一般地说，(None, None, 463)。这对应于 893 个时间步长的 1 个样本，每个时间步长有 463 个特征。输出形状为 (1, 893, 2)，即 (None, None, 2).
我的网络结构是这样的：
model = Sequential()
model.add(Convolution1D(64, 5, input_dim = one_input_length, border_mode = "same", W_regularizer = l2(0.01)))
model.add(MaxPooling1D(10, border_mode = "same"))
model.add(Convolution1D(64, 5, border_mode = "same", W_regularizer = l2(0.01)))
model.add(MaxPooling1D(10, border_mode = "same"))
model.add(GRU(300, return_sequences = True, W_regularizer = l2(0.01), U_regularizer = l2(0.01)))
model.add(TimeDistributed(Dense(2, activation='sigmoid')))
这样编译：
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
问题是，当我执行 model.fit(test_X, test_Y, nb_epochs = ....) 时，出现以下错误：Incompatible shapes: [1,893] vs. [1,9]，追溯到 compile 行。
我使用 this 技术记录了每一层的输出形状，得出以下结论：
Input: (1, 893, 463)
Conv_1: (1, 893, 64)
Pool_1: (1, 90, 64)
Conv_2: (1, 90, 64)
Pool_2: (1, 9, 64)
GRU: (1, 9, 300)
Dense: (1, 9, 2)
我怀疑当模型尝试计算准确性时会发生这种情况，并发现对于 893 个正确的输出，它只有 9 个预测。出于某种原因，第二个 Convolutional1D 层开始在时间步长上进行卷积，而不是像第一个层那样在特征上进行卷积。
这是为什么，我该如何解决？
编辑：
模型总结：
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
convolution1d_1 (Convolution1D) (None, None, 64) 148224 convolution1d_input_1[0][0]
____________________________________________________________________________________________________
maxpooling1d_1 (MaxPooling1D) (None, None, 64) 0 convolution1d_1[0][0]
____________________________________________________________________________________________________
convolution1d_2 (Convolution1D) (None, None, 64) 20544 maxpooling1d_1[0][0]
____________________________________________________________________________________________________
maxpooling1d_2 (MaxPooling1D) (None, None, 64) 0 convolution1d_2[0][0]
____________________________________________________________________________________________________
gru_1 (GRU) (None, None, 300) 328500 maxpooling1d_2[0][0]
____________________________________________________________________________________________________
timedistributed_1 (TimeDistribut (None, None, 2) 602 gru_1[0][0]
====================================================================================================
Total params: 497,870
Trainable params: 497,870
Non-trainable params: 0
____________________________________________________________________________________________________
我正在尝试制作一个 CNN-LSTM 分类器，在给定时间序列数据的情况下，该分类器将为每个时间步提供一个输出。
完整错误信息：
Traceback (most recent call last):
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1021, in _do_call
return fn(*args)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1003, in _run_fn
status, run_metadata)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 469, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [1,893] vs. [1,9]
[[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](ArgMax, ArgMax_1)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "Stock_CNN_LSTM.py", line 89, in <module>
model.fit(test_X, test_Y, nb_epoch=nb_epoch, verbose = 2, callbacks=[TestCallback((test_X, test_Y)), ModelCheckpoint("cnn_lstm_model-{epoch:02d}.h5")], initial_epoch = initial_epoch)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/models.py", line 672, in fit
initial_epoch=initial_epoch)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/engine/training.py", line 1192, in fit
initial_epoch=initial_epoch)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/engine/training.py", line 892, in _fit_loop
outs = f(ins_batch)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 1900, in __call__
feed_dict=feed_dict)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 766, in run
run_metadata_ptr)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 964, in _run
feed_dict_string, options, run_metadata)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1014, in _do_run
target_list, options, run_metadata)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1034, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [1,893] vs. [1,9]
[[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](ArgMax, ArgMax_1)]]
Caused by op 'Equal', defined at:
File "Stock_CNN_LSTM.py", line 71, in <module>
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/models.py", line 594, in compile
**kwargs)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/engine/training.py", line 713, in compile
append_metric(i, 'acc', acc_fn(y_true, y_pred))
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/metrics.py", line 11, in categorical_accuracy
K.argmax(y_pred, axis=-1)))
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 1275, in equal
return tf.equal(x, y)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/ops/gen_math_ops.py", line 728, in equal
result = _op_def_lib.apply_op("Equal", x=x, y=y, name=name)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
op_def=op_def)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Incompatible shapes: [1,893] vs. [1,9]
[[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](ArgMax, ArgMax_1)]]
谢谢！
现在所有 convolution/pooling 层都在随时间变化。如果您想将它们应用到特征 space 中，您需要将它们设为 TimeDistributed 并在输入的末尾添加一个额外的维度。然后，您需要在将数据传递到 GRU 层之前删除额外的维度。但是，如果每个卷积只有一个通道输出，则只能这样做：
import keras.backend as K
model = Sequential()
model.add(Lambda(lambda x: K.expand_dims(x, -1)))
model.add(TimeDistributed(Convolution1D(1, 5, input_dim = one_input_length, border_mode = "same", W_regularizer = l2(0.01))))
model.add(TimeDistributed(MaxPooling1D(10, border_mode = "same")))
model.add(TimeDistributed(Convolution1D(1, 5, border_mode = "same", W_regularizer = l2(0.01))))
model.add(TimeDistributed(MaxPooling1D(10, border_mode = "same")))
model.add(Lambda(lambda x: K.squeeze(x, -1)))
model.add(GRU(300, return_sequences = True, W_regularizer = l2(0.01), U_regularizer = l2(0.01)))
model.add(TimeDistributed(Dense(2, activation='sigmoid')))
如果您想在卷积中使用多个输出通道，那么您需要创建某种 "matrix" 的 GRU 单元。
因此，我的输入的形状为 (1, 893, 463)，或者更一般地说，(None, None, 463)。这对应于 893 个时间步长的 1 个样本，每个时间步长有 463 个特征。输出形状为 (1, 893, 2)，即 (None, None, 2).
我的网络结构是这样的：
model = Sequential()
model.add(Convolution1D(64, 5, input_dim = one_input_length, border_mode = "same", W_regularizer = l2(0.01)))
model.add(MaxPooling1D(10, border_mode = "same"))
model.add(Convolution1D(64, 5, border_mode = "same", W_regularizer = l2(0.01)))
model.add(MaxPooling1D(10, border_mode = "same"))
model.add(GRU(300, return_sequences = True, W_regularizer = l2(0.01), U_regularizer = l2(0.01)))
model.add(TimeDistributed(Dense(2, activation='sigmoid')))
这样编译：
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
问题是，当我执行 model.fit(test_X, test_Y, nb_epochs = ....) 时，出现以下错误：Incompatible shapes: [1,893] vs. [1,9]，追溯到 compile 行。
我使用 this 技术记录了每一层的输出形状，得出以下结论：
Input: (1, 893, 463)
Conv_1: (1, 893, 64)
Pool_1: (1, 90, 64)
Conv_2: (1, 90, 64)
Pool_2: (1, 9, 64)
GRU: (1, 9, 300)
Dense: (1, 9, 2)
我怀疑当模型尝试计算准确性时会发生这种情况，并发现对于 893 个正确的输出，它只有 9 个预测。出于某种原因，第二个 Convolutional1D 层开始在时间步长上进行卷积，而不是像第一个层那样在特征上进行卷积。
这是为什么，我该如何解决？
编辑：
模型总结：
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
convolution1d_1 (Convolution1D) (None, None, 64) 148224 convolution1d_input_1[0][0]
____________________________________________________________________________________________________
maxpooling1d_1 (MaxPooling1D) (None, None, 64) 0 convolution1d_1[0][0]
____________________________________________________________________________________________________
convolution1d_2 (Convolution1D) (None, None, 64) 20544 maxpooling1d_1[0][0]
____________________________________________________________________________________________________
maxpooling1d_2 (MaxPooling1D) (None, None, 64) 0 convolution1d_2[0][0]
____________________________________________________________________________________________________
gru_1 (GRU) (None, None, 300) 328500 maxpooling1d_2[0][0]
____________________________________________________________________________________________________
timedistributed_1 (TimeDistribut (None, None, 2) 602 gru_1[0][0]
====================================================================================================
Total params: 497,870
Trainable params: 497,870
Non-trainable params: 0
____________________________________________________________________________________________________
我正在尝试制作一个 CNN-LSTM 分类器，在给定时间序列数据的情况下，该分类器将为每个时间步提供一个输出。
完整错误信息：
Traceback (most recent call last):
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1021, in _do_call
return fn(*args)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1003, in _run_fn
status, run_metadata)
File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 469, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [1,893] vs. [1,9]
[[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](ArgMax, ArgMax_1)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "Stock_CNN_LSTM.py", line 89, in <module>
model.fit(test_X, test_Y, nb_epoch=nb_epoch, verbose = 2, callbacks=[TestCallback((test_X, test_Y)), ModelCheckpoint("cnn_lstm_model-{epoch:02d}.h5")], initial_epoch = initial_epoch)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/models.py", line 672, in fit
initial_epoch=initial_epoch)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/engine/training.py", line 1192, in fit
initial_epoch=initial_epoch)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/engine/training.py", line 892, in _fit_loop
outs = f(ins_batch)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 1900, in __call__
feed_dict=feed_dict)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 766, in run
run_metadata_ptr)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 964, in _run
feed_dict_string, options, run_metadata)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1014, in _do_run
target_list, options, run_metadata)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1034, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [1,893] vs. [1,9]
[[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](ArgMax, ArgMax_1)]]
Caused by op 'Equal', defined at:
File "Stock_CNN_LSTM.py", line 71, in <module>
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/models.py", line 594, in compile
**kwargs)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/engine/training.py", line 713, in compile
append_metric(i, 'acc', acc_fn(y_true, y_pred))
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/metrics.py", line 11, in categorical_accuracy
K.argmax(y_pred, axis=-1)))
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py", line 1275, in equal
return tf.equal(x, y)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/ops/gen_math_ops.py", line 728, in equal
result = _op_def_lib.apply_op("Equal", x=x, y=y, name=name)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
op_def=op_def)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/Users/user/.pyenvs/MLPy3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Incompatible shapes: [1,893] vs. [1,9]
[[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](ArgMax, ArgMax_1)]]
谢谢！
现在所有 convolution/pooling 层都在随时间变化。如果您想将它们应用到特征 space 中，您需要将它们设为 TimeDistributed 并在输入的末尾添加一个额外的维度。然后，您需要在将数据传递到 GRU 层之前删除额外的维度。但是，如果每个卷积只有一个通道输出，则只能这样做：
import keras.backend as K
model = Sequential()
model.add(Lambda(lambda x: K.expand_dims(x, -1)))
model.add(TimeDistributed(Convolution1D(1, 5, input_dim = one_input_length, border_mode = "same", W_regularizer = l2(0.01))))
model.add(TimeDistributed(MaxPooling1D(10, border_mode = "same")))
model.add(TimeDistributed(Convolution1D(1, 5, border_mode = "same", W_regularizer = l2(0.01))))
model.add(TimeDistributed(MaxPooling1D(10, border_mode = "same")))
model.add(Lambda(lambda x: K.squeeze(x, -1)))
model.add(GRU(300, return_sequences = True, W_regularizer = l2(0.01), U_regularizer = l2(0.01)))
model.add(TimeDistributed(Dense(2, activation='sigmoid')))
如果您想在卷积中使用多个输出通道，那么您需要创建某种 "matrix" 的 GRU 单元。