ValueError: ('Error when checking model input: expected no data, but got:', array)

Question

I'm trying to build an inception and resnet model with my own image data. The dataset is 8000 images in total and has 6 labels. Everything goes fine while building the model. But the mentioned error occurs in the model.fit(). I'm really not sure what the problem after spending 14 hours.

I tried the following

1. Changing the image dimension ordering

2. Making changes to keras.json

3. changing the input_tensor shape in the model

Image of the error :

inception_model = InceptionV3(input_tensor = inception_model.input, include_top = True, weights = 'imagenet')
inception_last_layer = inception_model.get_layer('predictions').output
inception_out = Dense(num_classes, activation='softmax', name='output')(inception_last_layer)
custom_inception = Model(inception_model.input, inception_out)

for layer in custom_inception.layers[:-3]:
        layer.trainable = False

custom_inception.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy', 'mse', 'mae', 'mape'])
train_inception = custom_inception.fit(X_train, y_train, batch_size=8, epochs=2)

EDIT: I'm currently using keras 2.2.0 which I downgraded from latest version after going through some keras issues in github. It did solve some initial hiccups. I'm currently using inception and resnet from their respective python files which I made some changes include_top=include_top to require_flatten=include_top from this

EDIT2: Here are the inputs shapes

(1690, 220, 220, 1)  is the X_train shape
(1690, 6)  is the y_train 
(423, 220, 220, 1)  is the X_test shape
(423, 6)  is the y_test 

Answer 1

Solved this problem by the following steps:

input_tensor=Input((300,300,3))

in place of

input_tensor = inception_model.input

2. Upgrading tensorflow and keras to 1.13.1 and 2.2.4
3. Defining the model with input shape(300,300,3) and stacking my (300,300,1) input thrice on channel axis inorder to match (300,300,3)