python - Keras 中的 Convolutional1D 根据时间步长而不是特征进行卷积？

Question

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)。

我的网络结构如下所示:

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')))

编译如下:

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，我收到以下错误:不兼容的形状:[1,893] 与 [ 1,9]，追溯到 compile 行。

我使用 this 记录了每个层的输出形状技术，想出这个:

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 个正确输出，它只有 9 个预测。由于某种原因，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，我该如何解决这个问题？

编辑:

模型摘要:

____________________________________________________________________________________________________
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，它将给出每个时间步长的输出。

完整错误消息:

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!

Answer 1

现在所有的卷积/池化层都随着时间的推移而起作用。如果您想将它们应用到特征空间中，您需要将它们设为 TimeDistributed 并在输入末尾添加额外的维度。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，只有每个卷积有一个 channel 输出时，您才能做到这一点:

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 channel ，那么您需要创建某种 GRU 单元“矩阵”。