如何正确创建多输入神经网络

Question

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 fit method 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?

Answer 1

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.