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import numpy as np
from keras_squeezenet import SqueezeNet
from keras.applications.imagenet_utils import preprocess_input, decode_predictions
from keras.preprocessing import image
model = SqueezeNet()
model.save_weights('model_weights.h5')
model.save('model.h5')
Why model is so big?
du -sh *.h5
4.9M model.h5
4.9M model_weights.h5
It's strange, but after running scripts second time I get, what is the reason?
Here is the code to reproduce result:
Why model is so big?
It's strange, but after running scripts second time I get, what is the reason?
In my older Caffe experiments SqeezeNet v1.1 have size 2.9 Mb:
https://github.com/mrgloom/kaggle-dogs-vs-cats-solution
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