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827 lines
46 KiB
Python

"""
5 years ago
from data.params import SYS_ARGS
3 years ago
self.BATCHSIZE_PER_GPU = PROPOSED_BATCH_PER_GPU
#
if os.path.exists(_name) :
attr = json.loads((open(_name)).read())
for key in attr :
value = attr[key]
# f.close()
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 []}] :
f = open(_info['name'],'w')
f.write(json.dumps(_info['data']))
f.close()
n_labels number of labels default None
"""
inputs = args['inputs']
name = args['name']
labels = None if 'labels' not in args else args['labels']
n_labels= None if 'n_labels' not in args else args['n_labels']
shift = [0] if self.__class__.__name__.lower() == 'generator' else [1] #-- not sure what this is doing
for g, _ in grad_and_vars:
tmp_dim = self.Z_DIM if 'dim' not in args else args['dim']
label = args['label']
loss = w_distance + 10 * gradient_penalty + sum(all_regs)
#tf.add_to_collection('dlosses', loss)
tf.compat.v1.add_to_collection('dlosses', loss)
return w_distance, loss
tf.compat.v1.reset_default_graph()
# df = (i * df).sum(axis=1)