node = layer._inbound_nodes[node_index] AttributeError: 'NoneType' object has no attribute '_inbound_nodes'

[ssbilakeri]

Hi
Im partitioning different scaled feature map and then concatenating all. but im facing bellow error. please help me to resolve this
node = layer._inbound_nodes[node_index]

AttributeError: 'NoneType' object has no attribute '_inbound_nodes'

here is my code

model_resnet = ResNet50(include_top=False, weights="imagenet", input_shape=(386, 124, 3))

conv1 = Conv2D(128, (2,2), activation = 'relu', strides = (3,3))(model_resnet.output)
conv1 = BatchNormalization()(conv1)
pool1 = MaxPooling2D((2,2), padding='same')(conv1)
relu1 = Activation('relu')(pool1)
drop1 = Dropout(rate = 0.5)(relu1)
from keras.layers import Reshape
flattened1 = Reshape((256,))(drop1)

conv2 = Conv2D(256, (2,2), activation = 'relu', strides = (2,2))(model_resnet.output)
conv2 = BatchNormalization()(conv2)
pool2 = MaxPooling2D((2,2), padding='same')(conv2)
relu2 = Activation('relu')(pool2)
drop2 = Dropout(rate = 0.5)(relu2)
flattened2 = Reshape((768,))(drop2)
##########partitions scale 2#####
b= pool2.shape[1]
n_partitions= 3
delta= b//n_partitions
partitions= []
for i in range(1, 3):
slice= pool2[ :, (i-1)delta:idelta, :]
partitions.append(slice)

p1= partitions[0]
p1 = BatchNormalization()(p1)
p1 = MaxPooling2D((2,2), padding='same')(p1)
p1 = Activation('relu')(p1)
p1 = Dropout(rate = 0.5)(p1)
p1 = Reshape((256,))(p1)

p2= partitions[1]
p2 = BatchNormalization()(p2)
p2 = MaxPooling2D((2,2), padding='same')(p2)
p2 = Activation('relu')(p2)
p2 = Dropout(rate = 0.5)(p2)
p2 = Reshape((256,))(p2)
########endscale2####

conv3 = Conv2D(256, (2,2), activation = 'relu', strides = (1,1))(model_resnet.output)
conv3 = BatchNormalization()(conv3)
pool3 = MaxPooling2D((2,2), padding='same')(conv3)
relu3 = Activation('relu')(pool3)
drop3 = Dropout(rate = 0.5)(relu3)
flattened3 = Reshape((3072,))(drop3)

######partitions scale3#####
b= pool3.shape[1]
n_partitions= 3
delta= b//n_partitions
partitions= []
for i in range(1, 4):
slice= pool3[ :, (i-1)delta:idelta, :]
partitions.append(slice)

p3= partitions[0]
p3 = BatchNormalization()(p3)
p3 = MaxPooling2D((2,2), padding='same')(p3)
p3 = Activation('relu')(p3)
p3 = Dropout(rate = 0.2)(p3)
p3 = Reshape((256,))(p3)

p4= partitions[1]
p4 = BatchNormalization()(p4)
p4 = MaxPooling2D((2,2), padding='same')(p4)
p4 = Activation('relu')(p4)
p4 = Dropout(rate = 0.2)(p4)
p4 = Reshape((256,))(p4)

p5= partitions[2]
p5 = BatchNormalization()(p5)
p5 = MaxPooling2D((2,2), padding='same')(p5)
p5 = Activation('relu')(p5)
p5 = Dropout(rate = 0.2)(p5)
p5 = Reshape((256,))(p5)
#####scale3 end##
#res=tf.concat(axis=0,values=[pool1, pool2, pool3,p1, p2, p3, p4, p5])
F =concatenate([flattened1, flattened2, flattened3, p1, p2, p3, p4, p5],1)

x4 = Dense(units=751, activation='softmax',name='fc8', kernel_initializer=RandomNormal(mean=0.0,stddev=0.001))(F)
model = Model(inputs=model_resnet.input, outputs=x4)