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I am trying to finetune a model in Keras:

 inception_model = InceptionV3(weights=None, include_top=False, input_shape=(150, 
150, 1))

 x = inception_model.output
 x = GlobalAveragePooling2D()(x)
 x = Dense(256, activation='relu', name='fc1')(x)
 x = Dropout(0.5)(x)
 predictions = Dense(10, activation='softmax', name='predictions')(x)
 classifier = Model(inception_model.input, predictions)


 ####training training training ... save weights


 classifier.load_weights("saved_weights.h5")

 classifier.layers.pop()
 classifier.layers.pop()
 classifier.layers.pop()
 classifier.layers.pop()
 ###enough poping to reach standard InceptionV3 

 x = classifier.output
 x = GlobalAveragePooling2D()(x)
 x = Dense(256, activation='relu', name='fc1')(x)
 x = Dropout(0.5)(x)
 predictions = Dense(10, activation='softmax', name='predictions')(x)
 classifier = Model(classifier.input, predictions)

But I get the error:

ValueError: Input 0 is incompatible with layer global_average_pooling2d_3: expected ndim=4, found ndim=2

You shouldn't use pop() method on models created using functional API (i.e. keras.models.Model). Only Sequential models (i.e. keras.models.Sequential) have a built-in pop() method (usage: model.pop()). Instead, use index or the names of the layers to access a specific layer:

classifier.load_weights("saved_weights.h5")
x = classifier.layers[-5].output # use index of the layer directly
x = GlobalAveragePooling2D()(x)