How to implement handmade features in a Keras Sequential model? I wanted to know how to implement my handmade features in my Keras model.
Below I have some code where the embedding_matrix is the Stanford Glove NLP dataset. 

def BidLstm(maxlen, max_features, embed_size, embedding_matrix):
   inp = Input(shape=(maxlen, ))
   x = Embedding(max_features, embed_size, weights=[embedding_matrix],
                 trainable=False)(inp)
   x = Bidirectional(LSTM(300, return_sequences=True, dropout=0.25,
                          recurrent_dropout=0.25))(x)
   x = Attention(maxlen)(x)
   x = Dense(256, activation="relu")(x)
   x = Dropout(0.25)(x)
   x = Dense(6, activation="sigmoid")(x)
   model = Model(inputs=inp, outputs=x)


I want to implement some extra features (preferably numpy array of features) into my keras model.

Any suggestions?
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Congratulations  for this  question  man