How to correctly create a multi input neural network

i'm building a NN that has, as input, two car images and classifies if thery are the same make and model. My problem is in the fitmethod of keras, because there is this error
>ValueError: Error when checking target: expected dense_3 to have shape (1,) but got array with shape (2,)

The network architecture is the following:

input1=Input((150,200,3))
model1=InceptionV3(include_top=False, weights='imagenet', input_tensor=input1)
model1.layers.pop()
input2=Input((150,200,3))
model2=InceptionV3(include_top=False, weights='imagenet', input_tensor=input2)
model2.layers.pop()
for layer in model2.layers:
  layer.name = "custom_layer_"+ layer.name
concat = concatenate([model1.layers[-1].output,model2.layers[-1].output])
flat = Flatten()(concat)
dense1=Dense(100, activation='relu')(flat)
do1=Dropout(0.25)(dense1)
dense2=Dense(50, activation='relu')(do1)
do2=Dropout(0.25)(dense2)
dense3=Dense(1, activation='softmax')(do2)
model = Model(inputs=[model1.input,model2.input],outputs=dense3)

My idea is that the error is due to the to_catogorical method that i have called on the array which stores, as 0 or 1, if the two cars have the same make and model or not. Any suggestion?

Since you are doing binary classification with one-hot encoded labels, then you should change this line:

dense3=Dense(1, activation='softmax')(do2)

To:

dense3=Dense(2, activation='softmax')(do2)

Softmax with a single neuron makes no sense, two neurons should be used for binary classification with softmax activation.