Pre-training Keras Xception and InceptionV3 models

I'm trying to do a simple binary classification problem using Keras and its pre-built ImageNet CNN architecture.

For VGG16, I took the following approach,

vgg16_model = keras.application.vgg16.VGG16()

'''Rebuild the vgg16 using an empty sequential model'''
model = Sequential()
for layer in vgg16_model.layers:
    model.add(layer)

'''Since the problem is binary, I got rid of the output layer and added a more appropriate output layer.'''
model.pop()

'''Freeze other pre-trained weights'''
for layer in model.layers:
    layer.trainable = False

'''Add the modified final layer'''
model.add(Dense(2, activation = 'softmax'))

And this worked marvelously with higher accuracy than my custom built CNN. But it took a while to train and I wanted to take a similar approach using Xception and InceptionV3 since they were lighter models with higher accuracy.

xception_model = keras.applicaitons.xception.Xception()
model = Sequential()
for layer in xception_model.layers:
    model_xception.add(layer)

When I run the above code, I get the following error:

ValueError: Input 0 is incompatible with layer conv2d_193: expected axis -1 of input shape to have value 64 but got shape (None, None, None, 128)

Basically, I would like to do the same thing as I did with VGG16 model; keep the other pretrained weights as they are and simply modify the output layer to a binary classification output instead of an output layer with 1000 outcomes. I can see that unlike VGG16, which has relatively straightforward convolution layer structure, Xception and InceptionV3 have some funky nodes that I'm not 100% familiar with and I'm assuming those are causing issues. If anyone can help out sort the problem, it'd be much appreciated!

Thanks!

Your code fails because InceptionV3 and Xception are not Sequential models (i.e., they contain "branches"). So you can't just add the layers into a Sequential container.

Now since the top layers of both InceptionV3 and Xception consist of a GlobalAveragePooling2D layer and the final Dense(1000) layer,

if include_top:
    x = GlobalAveragePooling2D(name='avg_pool')(x)
    x = Dense(classes, activation='softmax', name='predictions')(x)

if you want to remove the final dense layer, you can just set include_top=False plus pooling='avg' when creating these models.

base_model = InceptionV3(include_top=False, pooling='avg')
for layer in base_model.layers:
    layer.trainable = False
output = Dense(2, activation='softmax')(base_model.output)
model = Model(base_model.input, output)