Add hand-crafted features to Keras sequential model

Question

I have 1D sequences which I want to use as input to a Keras VGG classification model, split in x_train and x_test. For each sequence,

Answer 1

Sequential model is not very flexible. You should look into the functional API.

I would try something like this:

from keras.layers import (Conv1D, MaxPool1D, Dropout, Flatten, Dense,
                          Input, concatenate)
from keras.models import Model, Sequential

timesteps = 50
n = 5

def network():
    sequence = Input(shape=(timesteps, 1), name='Sequence')
    features = Input(shape=(n,), name='Features')

    conv = Sequential()
    conv.add(Conv1D(10, 5, activation='relu', input_shape=(timesteps, 1)))
    conv.add(Conv1D(10, 5, activation='relu'))
    conv.add(MaxPool1D(2))
    conv.add(Dropout(0.5, seed=789))

    conv.add(Conv1D(5, 6, activation='relu'))
    conv.add(Conv1D(5, 6, activation='relu'))
    conv.add(MaxPool1D(2))
    conv.add(Dropout(0.5, seed=789))
    conv.add(Flatten())
    part1 = conv(sequence)

    merged = concatenate([part1, features])

    final = Dense(512, activation='relu')(merged)
    final = Dropout(0.5, seed=789)(final)
    final = Dense(2, activation='softmax')(final)

    model = Model(inputs=[sequence, features], outputs=[final])

    model.compile(loss='logcosh', optimizer='adam', metrics=['accuracy'])

    return model

m = network()