The keras model looks like this
features_input = Input(shape=(features.shape,)) 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?