how do I solve this error : AttributeError: 'NoneType' object has no attribute '_inbound_nodes'?

Question

I have an autoencoder and I try to use the specific value of input layer in in intermediate layer using lambda and produce a new tensor and send to next layers but it produces this error:

Traceback (most recent call last):

File "", line 99, in model=Model(inputs=[image,wtm],outputs=decoded)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\legacy\\interfaces.py", line 91, in wrapper return func(*args, **kwargs)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\engine\\network.py", line 93, in init self._init_graph_network(*args, **kwargs)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\engine\\network.py", line 231, in _init_graph_network self.inputs, self.outputs)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\engine\\network.py", line 1366, in _map_graph_network tensor_index=tensor_index)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\engine\\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\engine\\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\engine\\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\engine\\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\engine\\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\engine\\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\engine\\network.py", line 1353, in build_map node_index, tensor_index)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\engine\\network.py", line 1325, in build_map node = layer._inbound_nodes[node_index]

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

this is my code and after adding the first lambda layer it produces this error! could you please tell why this error has happened? I appreciate your help? the thing I need is sth like this: wtm={[0,1,1,0],[0,1,1,0],[0,0,0,0],[0,1,0,0]} I choose wtm[:,i,j] and produce new tensor with shape (28,28,1) and with value wtm[:,i,j].

wt_random=np.random.randint(2, size=(49999,4,4))
w_expand=wt_random.astype(np.float32)
wv_random=np.random.randint(2, size=(9999,4,4))
wv_expand=wv_random.astype(np.float32)
#w_expand[:,:4,:4]=wt_random
#wv_expand[:,:4,:4]=wv_random
x,y,z=w_expand.shape
w_expand=w_expand.reshape((x,y,z,1))
x,y,z=wv_expand.shape
wv_expand=wv_expand.reshape((x,y,z,1))

#-----------------building w test---------------------------------------------
w_test = np.random.randint(2,size=(1,4,4))
w_test=w_test.astype(np.float32)
#wt_expand=np.zeros((1,28,28),dtype='float32')
#wt_expand[:,0:4,0:4]=w_test
w_test=w_test.reshape((1,4,4,1))

wtm=Input((4,4,1))
        image = Input((28, 28, 1))
        conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e')(image)
        conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e')(conv1)
        conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e')(conv2)
        BN=BatchNormalization()(conv3)
        encoded =  Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I')(BN)

   rep=Kr.layers.Lambda(lambda x:Kr.backend.repeat(x,28))
    a=rep(Kr.layers.Lambda(lambda x:x[1,1])(wtm))

    add_const = Kr.layers.Lambda(lambda x: x[0] + x[1])
    encoded_merged = add_const([encoded,a])


    #-----------------------decoder------------------------------------------------
    #------------------------------------------------------------------------------
    deconv1 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl1d')(encoded_merged)
    deconv2 = Conv2D(64, (5, 5), activation='elu', padding='same', name='convl2d')(deconv1)
    deconv3 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl3d')(deconv2)
    deconv4 = Conv2D(64, (5, 5), activation='elu',padding='same', name='convl4d')(deconv3)
    BNd=BatchNormalization()(deconv4)
    #DrO2=Dropout(0.25,name='DrO2')(BNd)

    decoded = Conv2D(1, (5, 5), activation='sigmoid', padding='same', name='decoder_output')(BNd) 
    #model=Model(inputs=image,outputs=decoded)

    model=Model(inputs=[image,wtm],outputs=decoded)

    decoded_noise = GaussianNoise(0.5)(decoded)

    #----------------------w extraction------------------------------------
    convw1 = Conv2D(64, (5,5), activation='relu', name='conl1w')(decoded_noise)#24
    convw2 = Conv2D(64, (5,5), activation='relu', name='convl2w')(convw1)#20
    #Avw1=AveragePooling2D(pool_size=(2,2))(convw2)
    convw3 = Conv2D(64, (5,5), activation='relu' ,name='conl3w')(convw2)#16
    convw4 = Conv2D(64, (5,5), activation='relu' ,name='conl4w')(convw3)#12
    #Avw2=AveragePooling2D(pool_size=(2,2))(convw4)
    convw5 = Conv2D(64, (5,5), activation='relu', name='conl5w')(convw4)#8
    convw6 = Conv2D(64, (5,5), activation='relu', name='conl6w')(convw5)#4
    convw7 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl7w',dilation_rate=(2,2))(convw6)#4
    convw8 = Conv2D(64, (5,5), activation='relu', padding='same',name='conl8w',dilation_rate=(2,2))(convw7)#4
    convw9 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl9w',dilation_rate=(2,2))(convw8)#4
    convw10 = Conv2D(64, (5,5), activation='relu',padding='same', name='conl10w',dilation_rate=(2,2))(convw9)#4
    BNed=BatchNormalization()(convw10)
    pred_w = Conv2D(1, (1, 1), activation='sigmoid', padding='same', name='reconstructed_W',dilation_rate=(2,2))(BNed)  

    w_extraction=Model(inputs=[image,wtm],outputs=[decoded,pred_w])

    w_extraction.summary()
(x_train, _), (x_test, _) = mnist.load_data()
x_validation=x_train[1:10000,:,:]
x_train=x_train[10001:60000,:,:]
#
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_validation = x_validation.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))  # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if using `channels_first` image data format
x_validation = np.reshape(x_validation, (len(x_validation), 28, 28, 1))

#---------------------compile and train the model------------------------------
opt=SGD(momentum=0.99)
w_extraction.compile(optimizer='adam', loss={'decoder_output':'mse','reconstructed_W':'binary_crossentropy'}, loss_weights={'decoder_output': 0.2, 'reconstructed_W': 1.0},metrics=['mae'])
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=40)
#rlrp = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=20, min_delta=1E-4, verbose=1)
mc = ModelCheckpoint('best_model_5x5F_dp_gn.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)
history=w_extraction.fit([x_train,w_expand], [x_train,w_expand],
          epochs=1,
          batch_size=64, 
          validation_data=([x_validation,wv_expand], [x_validation,wv_expand]),
          callbacks=[TensorBoard(log_dir='E:concatnatenetwork', histogram_freq=0, write_graph=False),es,mc])

when I implement it, this error is shown:

Traceback (most recent call last):

File "", line 1, in encoded_merged = add_const([encoded,a])

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\engine\\base_layer.py", line 457, in call output = self.call(inputs, **kwargs)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\keras\\layers\\core.py", line 687, in call return self.function(inputs, **arguments)

File "", line 1, in add_const = Kr.layers.Lambda(lambda x: x[0] + x 1 )

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py", line 866, in binary_op_wrapper return func(x, y, name=name)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py", line 301, in add "Add", x=x, y=y, name=name)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py", line 787, in _apply_op_helper op_def=op_def)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py", line 488, in new_func return func(*args, **kwargs)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py", line 3274, in create_op op_def=op_def)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py", line 1792, in init control_input_ops)

File "D:\\software\\Anaconda3\\envs\\py36\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py", line 1631, in _create_c_op raise ValueError(str(e))

ValueError: Dimensions must be equal, but are 28 and 4 for 'lambda_9/add' (op: 'Add') with input shapes: [?,28,28,1], [4,28,1].

Answer 1

In keras every layer should be an instance of keras's Layer class. On the following line

a=rep(wtm[1,1])

You are selecting tensor element without using keras layer. This line is causing the error. You should change the line to the following to deal with the error.

a=rep(Kr.layers.Lambda(lambda x:x[1,1])(wtm))

Edit 1.

Since wtm has shape of [None, 4,4,1](you can try to print the shape), wtm[1,1] will select one element from first dimension and then the first element from selected element. If you want to get [1,1] shape array what you could do is index the first three dimensions. Here you should mind the batch size too, so what you want is to have array with shape [Batchsize, 1]. This could be done as follows.

new_wtm = Kr.layers.Lambda(lambda x:x[:,1,1,:])(wtm)

Now new_wtm.shape would be [None, 1] and calling repeat method will produce array with shape[None, rep, 1].

rep=Kr.layers.Lambda(lambda x:Kr.backend.repeat(x,28))
a=rep(Kr.layers.Lambda(lambda x:x[:,1,1,:])(wtm))
print(a.shape) # [None, 28, 1]

I hope this will resolve the issue

Edit 2:

To get a with shape [None, 28, 28,1], you need to use tile method.

rep=Kr.layers.Lambda(lambda x:Kr.backend.tile(x,[1, 28, 28, 1]))
a_1 = Kr.layers.Lambda(lambda x: x[:, 1, 1, :])(wtm)

a=rep(Kr.layers.Reshape([1,1,1])(a_1))

The test code

from keras.layers import Input, Concatenate, Activation,GaussianNoise,Dropout,BatchNormalization,MaxPool2D,AveragePooling2D
from keras.layers import Conv2D, AtrousConv2D
from keras.models import Model
from keras.datasets import mnist
from keras.callbacks import TensorBoard
from keras import backend as K
from keras import layers
import matplotlib.pyplot as plt
import tensorflow as tf
import keras as Kr
from keras.optimizers import SGD,RMSprop,Adam
from keras.callbacks import ReduceLROnPlateau
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
import numpy as np
import pylab as pl
import matplotlib.cm as cm
import keract
from matplotlib import pyplot
from keras import optimizers
from keras import regularizers
from tensorflow.python.keras.layers import Lambda;
#-----------------building w train---------------------------------------------

#wv_expand=np.zeros((9999,28,28),dtype='float32')
wt_random=np.random.randint(2, size=(49999,4,4))
w_expand=wt_random.astype(np.float32)
wv_random=np.random.randint(2, size=(9999,4,4))
wv_expand=wv_random.astype(np.float32)
#w_expand[:,:4,:4]=wt_random
#wv_expand[:,:4,:4]=wv_random
x,y,z=w_expand.shape
w_expand=w_expand.reshape((x,y,z,1))
x,y,z=wv_expand.shape
wv_expand=wv_expand.reshape((x,y,z,1))

#-----------------building w test---------------------------------------------
w_test = np.random.randint(2,size=(1,4,4))
w_test=w_test.astype(np.float32)
#wt_expand=np.zeros((1,28,28),dtype='float32')
#wt_expand[:,0:4,0:4]=w_test
w_test=w_test.reshape((1,4,4,1))

#wt_expand=wt_expand.reshape((1,28,28,1))

#-----------------------encoder------------------------------------------------
#------------------------------------------------------------------------------
wtm=Input((4,4,1))
image = Input((28, 28, 1))
conv1 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl1e',dilation_rate=(2,2))(image)
conv2 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl2e',dilation_rate=(2,2))(conv1)
conv3 = Conv2D(64, (5, 5), activation='relu', padding='same', name='convl3e',dilation_rate=(2,2))(conv2)
#conv3 = Conv2D(8, (3, 3), activation='relu', padding='same', name='convl3e', kernel_initializer='Orthogonal',bias_initializer='glorot_uniform')(conv2)
BN=BatchNormalization()(conv3)
#DrO1=Dropout(0.25,name='Dro1')(BN)
encoded =  Conv2D(1, (5, 5), activation='relu', padding='same',name='encoded_I',dilation_rate=(2,2))(BN)

#-----------------------adding w---------------------------------------


#-----------------------decoder------------------------------------------------
#------------------------------------------------------------------------------
rep0=Kr.layers.Lambda(lambda x:Kr.backend.tile(x,[1, 28, 28, 1]),name='aux0')
a_0 = Kr.layers.Lambda(lambda x: x[:, 0, 0, :])(wtm)

a0=rep0(Kr.layers.Reshape([1,1,1])(a_0))

rep1=Kr.layers.Lambda(lambda x:Kr.backend.tile(x,[1, 28, 28, 1]),name='aux1')
a_1 = Kr.layers.Lambda(lambda x: x[:, 0, 1, :])(wtm)

a1=rep1(Kr.layers.Reshape([1,1,1])(a_1))

rep2=Kr.layers.Lambda(lambda x:Kr.backend.tile(x,[1, 28, 28, 1]),name='aux2')
a_2 = Kr.layers.Lambda(lambda x: x[:, 0, 2, :])(wtm)

a2=rep2(Kr.layers.Reshape([1,1,1])(a_2))

rep3=Kr.layers.Lambda(lambda x:Kr.backend.tile(x,[1, 28, 28, 1]),name='aux3')
a_3 = Kr.layers.Lambda(lambda x: x[:, 0, 3, :])(wtm)

a3=rep3(Kr.layers.Reshape([1,1,1])(a_3))


add_const1 = Kr.layers.Lambda(lambda x: x[0] + x[1]+x[2]+x[3]+x[4],name='decoder_output')
encoded_merged = add_const1([encoded,a0,a1,a2,a3])

w=Model(inputs=[image,wtm],outputs=encoded_merged)

w.summary()
#----------------------training the model--------------------------------------
#------------------------------------------------------------------------------
#----------------------Data preparesion----------------------------------------

(x_train, _), (x_test, _) = mnist.load_data()
x_validation=x_train[1:10000,:,:]
x_train=x_train[10001:60000,:,:]
#
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_validation = x_validation.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 28, 28, 1))  # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 28, 28, 1))  # adapt this if using `channels_first` image data format
x_validation = np.reshape(x_validation, (len(x_validation), 28, 28, 1))

#---------------------compile and train the model------------------------------
opt=SGD(lr=0.0001,momentum=0.9)
w.compile(optimizer='adam', loss={'decoder_output':'mse'}, metrics=['mae'])
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=40)
mc = ModelCheckpoint('los4x4_repw.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)
history=w.fit([x_train,w_expand], x_train,
          epochs=1,
          batch_size=32, 
          validation_data=([x_validation,wv_expand], x_validation))
w.summary()

layer_name = 'lambda_96'
intermediate_layer_model = Model(inputs=watermark_extraction.input,outputs=watermark_extraction.get_layer(layer_name).output)
intermediate_output = intermediate_layer_model.predict([x_test[8000:8001],w_test])
fig = plt.figure(figsize=(20, 20))

rows = 8
columns = 8

first = intermediate_output

for i in range(1, columns*rows +1):
    img = intermediate_output[0,:,:,i-1]
    fig.add_subplot(rows, columns, i)
    plt.imshow(img, interpolation='nearest',cmap='gray')
    plt.axis('off')
plt.show()