使用Keras Theano我成功地制作了一个复发的双向LSTM神经网络，它能够训练和分类任意长度的DNA序列，使用以下模型（完整工作代码见：http://pastebin.com/jBLv8B72）：
sequence = Input(shape=(None, ONE_HOT_DIMENSION), dtype='float32')
dropout = Dropout(0.2)(sequence)
# bidirectional LSTM
forward_lstm = LSTM(
output_dim=50, init='uniform', inner_init='uniform', forget_bias_init='one', return_sequences=True,
activation='tanh', inner_activation='sigmoid',
)(dropout)
backward_lstm = LSTM(
output_dim=50, init='uniform', inner_init='uniform', forget_bias_init='one', return_sequences=True,
activation='tanh', inner_activation='sigmoid', go_backwards=True,
)(dropout)
blstm = merge([forward_lstm, backward_lstm], mode='concat', concat_axis=-1)
dense = TimeDistributed(Dense(NUM_CLASSES))(blstm)
self.model = Model(input=sequence, output=dense)
self.model.compile(
loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy']
)
为了提高模型的性能，我想添加其他图层 . 最优选卷积和最大池化层 . 我做了几次尝试，但每次都失败了 . 例如，将第2行更改为以下3行：
convolution = Convolution1D(filter_length=6, nb_filter=10)(sequence)
max_pooling = MaxPooling1D(pool_length=2)(convolution)
dropout = Dropout(0.2)(max_pooling)
该模型编译，但抛出一个错误：
ValueError: Input dimension mis-match. (input[0].shape[1] = 111, input[1].shape[1] = 53)
Apply node that caused the error: Elemwise{Composite{((i0 * log(i1)) + (i2 * log(i3)))}}(timedistributed_1_target, Elemwise{clip,no_inplace}.0, Elemwise{sub,no_inplace}.0, Elemwise{sub,no_inplace}.0)
Toposort index: 546
Inputs types: [TensorType(float32, 3D), TensorType(float32, 3D), TensorType(float32, 3D), TensorType(float32, 3D)]
Inputs shapes: [(1L, 111L, 2L), (1L, 53L, 2L), (1L, 111L, 2L), (1L, 53L, 2L)]
Inputs strides: [(888L, 8L, 4L), (424L, 8L, 4L), (888L, 8L, 4L), (424L, 8L, 4L)]
Inputs values: ['not shown', 'not shown', 'not shown', 'not shown']
Outputs clients: [[Sum{axis=[1, 2], acc_dtype=float64}(Elemwise{Composite{((i0 * log(i1)) + (i2 * log(i3)))}}.0)]]
显然尺寸存在问题 . 我已经尝试了重塑层，但没有成功 .
甚至可以在任意序列长度的上下文中使用卷积和/或最大池化层吗？
任何有关此事的帮助将不胜感激！