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supposed to solve https://github.com/qurator-spk/sbb_binarization/issues/41
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build_model_load_pretrained_weights_and_save.py
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build_model_load_pretrained_weights_and_save.py
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import os
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import sys
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import tensorflow as tf
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import keras , warnings
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from keras.optimizers import *
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from sacred import Experiment
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from models import *
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from utils import *
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from metrics import *
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def configuration():
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gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
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session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
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if __name__=='__main__':
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n_classes = 2
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input_height = 224
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input_width = 448
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weight_decay = 1e-6
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pretraining = False
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dir_of_weights = 'model_bin_sbb_ens.h5'
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#configuration()
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model = resnet50_unet(n_classes, input_height, input_width,weight_decay,pretraining)
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model.load_weights(dir_of_weights)
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model.save('./name_in_another_python_version.h5')
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