How to use additional features along with word embeddings in Keras ? : <p>I am training a LSTM model with Keras on the dataset which looks like following. The variable "Description" is a text field and "Age" and "Gender" are categorical and continuous fields. </p>

<pre><code>Age, Gender, Description
22, M, "purchased a phone"
35, F, "shopping for kids"
</code></pre>

<p>I am using word-embedding to convert the text fields to word vectors and then input it in the keras model. The code is given below: </p>

<pre><code>model = Sequential()
model.add(Embedding(word_index, 300, weights=[embedding_matrix], input_length=70, trainable=False))

model.add(LSTM(300, dropout=0.3, recurrent_dropout=0.3))
model.add(Dropout(0.6))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics['accuracy'])
</code></pre>

<p>This model is running successfully but I want to input "age" and "gender" variables as features as well. What changes are required in the code to use these features as well ? </p>
