AttributeError:'Tensor' object has no attribute '_keras_history'

Question

Aux_input = Input(shape=(wrd_temp.shape[1],1), dtype='float32')#shape (,200)
Main_input = Input(shape=(wrdvec.shape[1],),dtype='float32')#shape(,367)

X = Bidirectional(LSTM(20,return_sequences=True))(Aux_input)
X = Dropout(0.2)(X)
X = Bidirectional(LSTM(28,return_sequences=True))(X)
X = Dropout(0.2)(X)
X = Bidirectional(LSTM(28,return_sequences=False))(X)
Aux_Output = Dense(Opt_train.shape[1], activation= 'softmax' )(X)#total 22 classes

x = keras.layers.concatenate([Main_input,Aux_Output],axis=1)

x = tf.reshape(x,[1,389,1])#here 389 is the shape of the new input i.e.(
Main_input+Aux_Output)

x = Bidirectional(LSTM(20,return_sequences=True))(x)
x = Dropout(0.2)(x)
x = Bidirectional(LSTM(28,return_sequences=True))(x)
x = Dropout(0.2)(x)
x = Bidirectional(LSTM(28,return_sequences=False))(x)
Main_Output = Dense(Opt_train.shape[1], activation= 'softmax' )(x)

model = Model(inputs=[Aux_input,Main_input], outputs=    [Aux_Output,Main_Output])

Error occurs in line declaring the model ie model = Model(), here the attribute error has occurred, Also if there is any other mistake in my implementation please do take a not and notify me in the comment section.

Answer 1

The problem lied in the fact that using every tf operation should be encapsulated by either:

1. Using keras.backend functions,
2. Lambda layers,
3. Designated keras functions with the same behavior.

When you are using tf operation - you are getting tf tensor object which doesn't have history field. When you use keras functions you will get keras.tensor s.