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()

Answer 2

The problem is that splitting the input data using inputs[0,:,1] is not done as a keras layer.

You need to create a Lambda layer to be able to accomplish this.

The following code:

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


def tst_1(): 

    num_channels = 3
    inputs = Input(shape=(num_channels, 1000, 1))

    branch_outputs = []
    for i in range(num_channels):
        # Slicing the ith channel:
        out = Lambda(lambda x: x[:, i, :, :], name = "Lambda_" + str(i))(inputs)

        # Setting up your per-channel layers (replace with actual sub-models):
        out = Dense(224, activation='relu', name = "Dense_224_" + str(i))(out)
        out = Dense(112, activation='relu', name = "Dense_112_" + str(i))(out)
        out = Dense(56, activation='relu', name = "Dense_56_" + str(i))(out)
        branch_outputs.append(out)

    # Concatenating together the per-channel results:
    out = Concatenate()(branch_outputs)


    dense1 = Dense(224, activation='relu')(out)
    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()

correctly created the net that you want.