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"""
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from data.params import SYS_ARGS
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self.BATCHSIZE_PER_GPU = PROPOSED_BATCH_PER_GPU
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#
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if os.path.exists(_name) :
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attr = json.loads((open(_name)).read())
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for key in attr :
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value = attr[key]
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# f.close()
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for _info in [{"name":os.sep.join([self.out_dir,'meta-'+suffix+'.json']),"data":_object},{"name":os.sep.join([self.out_dir,'epochs.json']),"data":self.logs['epochs'] if 'epochs' in self.logs else []}] :
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f = open(_info['name'],'w')
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f.write(json.dumps(_info['data']))
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f.close()
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n_labels number of labels default None
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"""
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inputs = args['inputs']
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name = args['name']
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labels = None if 'labels' not in args else args['labels']
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n_labels= None if 'n_labels' not in args else args['n_labels']
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shift = [0] if self.__class__.__name__.lower() == 'generator' else [1] #-- not sure what this is doing
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for g, _ in grad_and_vars:
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tmp_dim = self.Z_DIM if 'dim' not in args else args['dim']
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label = args['label']
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loss = w_distance + 10 * gradient_penalty + sum(all_regs)
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#tf.add_to_collection('dlosses', loss)
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tf.compat.v1.add_to_collection('dlosses', loss)
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return w_distance, loss
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print(format_str % (epoch, -w_sum/(self.STEPS_PER_EPOCH*2), duration))
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# print (dir (w_distance))
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# This would allow the learnt model to be portable to another system
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def _apply(self,**args):
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if labels :
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# continue
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# self.logger.write({"module":"gan-generate","action":"generate","input":info})
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# self.logger.write({"module":"gan-generate","action":"compile.io","input":info})
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