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[P] Combining numerical and text features in (deep) neural networks in keras

Hi folks,

A lot of people ask how to combine NLP based features (or in general sequence embeddings) with standart features. In keras it pretty easy with a multiple input modell:

nlp_input = Input(shape=(seq_length,), name='nlp_input') meta_input = Input(shape=(10,), name='meta_input') emb = Embedding(output_dim=embedding_size, input_dim=100, input_length=seq_length)(nlp_input) nlp_out = Bidirectional(LSTM(128, dropout=0.3, recurrent_dropout=0.3, kernel_regularizer=regularizers.l2(0.01)))(emb) x = concatenate([nlp_out, meta_input]) x = Dense(classifier_neurons, activation='relu')(x) x = Dense(1, activation='sigmoid')(x) model = Model(inputs=[nlp_input , meta_input], outputs=[x]) 

Here is a link, where it more detailed.

Cheers

submitted by /u/ixeption
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.