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The keras model looks like this

features_input = Input(shape=(features.shape[1],))
inp = Input(shape=(maxlen, ))
x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False)(inp)
x = Bidirectional(LSTM(num_filters, return_sequences=True))(x)
max_pool = GlobalMaxPooling1D()(x)
x = concatenate([x_h, max_pool,features_input])
outp = Dense(6, activation="sigmoid")(x)

What does GlobalMaxPooling1D()(x) really do to the output of LSTM? I know the input to LSTM layer is of dimension (batch_size, steps, features).

Does GlobalMaxPooling1D take max across num_filters/hidden units of each LSTM unit?

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No, it takes max across steps.

Suppose the output x of your Bidirectional(LSTM()) has shape (batch_size, steps, hidden_size), then after GlobalMaxPooling1D() your max_pool would have shape (batch_size, hidden_size).

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  • $\begingroup$ So is it "select batch_size, max(hidden_size) group by batch_size, steps" in terms of SQL? Not able to visualize it $\endgroup$ – GeorgeOfTheRF Sep 19 '18 at 5:31
  • $\begingroup$ If your hidden size is of 100 dimensions for each word, and you have 20 words in each sentence, then you end up from <batchsize, 20, 100> to <bs, 1, 100>. This can be squeezed to give <bs, 100>. For each dimension you take the max value across all words. The intuition is - each dimension represents something and max_value represents that dimension the strongest. So across the sentence, you get the strongest features(dimensions) represented by that sentence. That is one way of getting a sentence embedding. You could also take an average and then feed both the avg and max to the next layer $\endgroup$ – Allohvk Jun 28 at 17:42

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