AttributeError: 'NoneType' object has no attribute '_inbound_nodes' while trying to add multiple keras Dense layers

Question

The input are 3 independent channels of 1000 features. I\'m trying to pass each channel through a independent NN path, then concatenate them into a flat layer. Then apply a FCN

Answer 1

Thanks to @CAta.RAy

I solved it in this way:

import numpy as np
from keras import layers
from keras.layers import Input, Add, Dense,Dropout, Lambda
from keras.layers import Flatten
from keras.optimizers import Adam
from keras.models import Model
import keras.backend as K




def tst_1(): 
    inputs = Input((3, 1000))

    x1 = Lambda(lambda x:x[:,0])(inputs)
    dense10 = Dense(224, activation='relu')(x1)
    dense11 = Dense(112, activation='relu')(dense10)
    dense12 = Dense(56, activation='relu')(dense11)

    x2 = Lambda(lambda x:x[:,1])(inputs)
    dense20 = Dense(224, activation='relu')(x2)
    dense21 = Dense(112, activation='relu')(dense20)
    dense22 = Dense(56, activation='relu')(dense21)

    x3 = Lambda(lambda x:x[:,2])(inputs)
    dense30 = Dense(224, activation='relu')(x3)
    dense31 = Dense(112, activation='relu')(dense30)
    dense32 = Dense(56, activation='relu')(dense31)

    flat = Add()([dense12, dense22, dense32])

    dense1 = Dense(224, activation='relu')(flat)
    drop1 = Dropout(0.5)(dense1)
    dense2 = Dense(112, activation='relu')(drop1)
    drop2 = Dropout(0.5)(dense2)
    dense3 = Dense(32, activation='relu')(drop2)
    densef = Dense(1, activation='sigmoid')(dense3)

    model = Model(inputs = inputs, outputs = densef)

    return model

Net = tst_1()
Net.compile(optimizer=Adam(), loss='binary_crossentropy', metrics=['accuracy'])

Net.summary()