[FIXED] Multioutput custom keras ResNet50 model; ValueError: Graph disconnected

Issue

In my code I am getting this error. How I can solve it?

ValueError: Graph disconnected: cannot obtain value for tensor KerasTensor(type_spec=TensorSpec(shape=(None, None, None, 3), dtype=tf.float32, name='input_2'), name='input_2', description="created by layer 'input_2'") at layer "conv1_pad". The following previous layers were accessed without issue: []

My model is

def multiple_outputs(generator):
    for batch_x,batch_y in generator:
        yield (batch_x, np.hsplit(batch_y,[26,28])) #here splitting input data into 6 groups
   
image_input = Input(shape=(input_size))
base_model =ResNet50(weights='imagenet',include_top=False)
m = base_model.output
x = GlobalAveragePooling2D(name='avg_pool')(m)
x = Dropout(0.2)(x)
type_out = Dense(26, activation='sigmoid', name='type_output')(x)
top_out = Dense(3, activation='softmax', name='top_output')(x)

model = Model(inputs=image_input,outputs= [type_out, top_out])

following I have mentioned model.fit section

history = model.fit(x=multiple_outputs(train_generator),
                steps_per_epoch=STEP_SIZE_TRAIN,
                validation_data=multiple_outputs(valid_generator),
                validation_steps=STEP_SIZE_VALID,
                callbacks=callbacks,
                max_queue_size=10,
                workers=1,
                use_multiprocessing=False,
                epochs=1)

Please, can someone help me in solving this issue?

Solution

Answer is, here I have done a mistake in model initialization. Input_tensor should be added in model initialization.

base_model = ResNet50(weights='imagenet', include_top=False, input_tensor=image_input)

Answered By - EverythingNeedToBeKnown