如何在keras中使用Conv1D和双向LSTM对每个时间步进行多类分类？

Question

I am trying to use a Conv1D and Bidirectional LSTM in keras (much like in this question ) for signal processing, but doing a multiclass classification of each time step.

The problem is that even though the shapes used by Conv1D and LSTM are somewhat equivalent:

Conv1D: (batch, length, channels)
LSTM: (batch, timeSteps, features)

The output of the Conv1D is = (length - (kernel_size - 1)/strides), and therefore doesn't match the LSTM shape anymore, even without using MaxPooling1D and Dropout.

To be more specific, my training set X has n samples with 1000 time steps and one channel (n_samples, 1000, 1), and I used LabelEncoder and OneHotEncoder so y has n samples, 1000 time steps and 5 one hot encoded classes (n_samples, 1000, 5).

Since one class is much more prevalent than the others (is actually the absence of signal), I am using loss='sparse_categorical_crossentropy', sample_weight_mode="temporal" and sample_weight to give a higher weight to time steps containing meaningful classes.

model = Sequential()
model.add(Conv1D(128, 3, strides=1, input_shape = (1000, 1), activation = 'relu'))
model.add(Bidirectional(LSTM(128, return_sequences=True)))
model.add(TimeDistributed(Dense(5, activation='softmax')))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['categorical_accuracy'], sample_weight_mode="temporal")
print(model.summary())

Model

When I try to fit the model I get this error message:

Error when checking target: expected time_distributed_1 to have shape (None, 998, 1) but got array with shape (100, 1000, 5).

Is there a way to make such a neural network configuration work?

Answer 1

Your convolution is cutting the tips of the sequence. Use padding='same' in the convolutional layers.

The message, though, seems not to fit your model. Your model clearly has 5 output features (because of Dense(5) ), but the massage says it expects 1. Maybe this is happening because of "sparse" crossentropy. You should probably, by the format of your data, use a "categorical_crossentropy".