CNN-LSTM Timeseries input for TimeDistributed layer

Question

I created a CNN-LSTM for survival prediction of web sessions, my training data looks as follows:

print(x_train.shape)
(288, 3, 393)

with (samples, timesteps, features) and my model:

model = Sequential()
model.add(TimeDistributed(Conv1D(128, 5, activation='relu'), 
                                 input_shape=(x_train.shape[1], x_train.shape[2])))
model.add(TimeDistributed(MaxPooling1D()))
model.add(TimeDistributed(Flatten()))
model.add(LSTM(64, stateful=True, return_sequences=True))
model.add(LSTM(16, stateful=True))
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer=Adam(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])

However, the TimeDistributed Layer requires a minimum of 3 dimensions, how should I transform the data to get it work?

Thanks a lot!

Asked By: Justus Erker

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Source

Answer 1

your data are in 3d format and this is all you need to feed a conv1d or an LSTM. if your target is 2D remember to set return_sequences=False in your last LSTM cell.

using a flatten before an LSTM is a mistake because you are destroying the 3D dimensionality

pay attention also on the pooling operation in order to not have a negative time dimension to reduce (I use ‘same’ padding in the convolution above in order to avoid this)

below is an example in a binary classification task

n_sample, time_step, n_features = 288, 3, 393
X = np.random.uniform(0,1, (n_sample, time_step, n_features))
y = np.random.randint(0,2, n_sample)

model = Sequential()
model.add(Conv1D(128, 5, padding='same', activation='relu', 
                 input_shape=(time_step, n_features)))
model.add(MaxPooling1D())
model.add(LSTM(64, return_sequences=True))
model.add(LSTM(16, return_sequences=False))
model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X,y, epochs=3)

Answered By: Marco Cerliani

CNN-LSTM Timeseries input for TimeDistributed layer

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