https://github.com/ShehabMMohamed/TinyImageNet-KaggleCompetition
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from keras.applications import Xception
from keras.layers import Input, Dense
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img_height = 299
img_width = 299
input_tensor = Input(shape=(img_width, img_height, 3))
pre_trained_model = Xception(weights='imagenet', input_tensor=input_tensor, include_top=False, pooling='avg')
# Downloads 83.7Mb of model...,
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pre_trained_model.layers[20].name
# 'block3_sepconv2' ( next are 'block3_sepconv2_bn' and 'conv2d_2' )
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pre_trained_model.summary()
#Total params: 20,861,480
#Trainable params: 20,806,952
#Non-trainable params: 54,528
# Input ... block3_sepconv2 ... block14_sepconv2 ... global_average_pooling2d_1
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### That's a lot of fine tuning...
https://github.com/keras-team/keras-applications/blob/master/keras_applications/xception.py#L188
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