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Merging the release into stable. Handling of GPU and epochs

This commit is contained in:
Steve Nyemba 2020-01-10 11:21:26 -06:00
parent 54abbeb42a f85d5fd870
commit 76e84c3859
3 changed files with 10 additions and 9 deletions
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6
data/gan.py
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@ -245,15 +245,12 @@ class Discriminator(GNet):
:label
"""
x = args['inputs']
print ()
print (x[:3,:])
print()
label = args['label']
with tf.compat.v1.variable_scope('D', reuse=tf.compat.v1.AUTO_REUSE , regularizer=l2_regularizer(0.00001)):
for i, dim in enumerate(self.D_STRUCTURE[1:]):
kernel = self.get.variables(name='W_' + str(i), shape=[self.D_STRUCTURE[i], dim])
bias = self.get.variables(name='b_' + str(i), shape=[dim])
print (["\t",bias,kernel])
# print (["\t",bias,kernel])
x = tf.nn.relu(tf.add(tf.matmul(x, kernel), bias))
x = self.normalize(inputs=x, name='cln' + str(i), shift=1,labels=label, n_labels=self.NUM_LABELS)
i = len(self.D_STRUCTURE)
@ -538,6 +535,7 @@ if __name__ == '__main__' :
# Now we get things done ...
column = SYS_ARGS['column']
column_id = SYS_ARGS['id'] if 'id' in SYS_ARGS else 'person_id'
column_id = column_id.split(',') if ',' in column_id else column_id
df = pd.read_csv(SYS_ARGS['raw-data'])
LABEL = pd.get_dummies(df[column_id]).astype(np.float32).values

7
data/maker/__init__.py
View File

@ -38,7 +38,7 @@ def train (**args) :
else:
logger = None
trainer = gan.Train(context=context,max_epochs=max_epochs,real=real,label=labels,column=column,column_id=column_id,logger = logger,logs=logs)
trainer = gan.Train(context=context,max_epochs=max_epochs,num_gpu=num_gpu,real=real,label=labels,column=column,column_id=column_id,logger = logger,logs=logs)
return trainer.apply()
def generate(**args):
@ -57,6 +57,9 @@ def generate(**args):
column_id = args['id']
logs = args['logs']
context = args['context']
num_gpu = 1 if 'num_gpu' not in args else args['num_gpu']
max_epochs = 10 if 'max_epochs' not in args else args['max_epochs']
#
#@TODO:
# If the identifier is not present, we should fine a way to determine or make one
@ -67,7 +70,7 @@ def generate(**args):
values.sort()
labels = pd.get_dummies(df[column_id]).astype(np.float32).values
handler = gan.Predict (context=context,label=labels,values=values,column=column)
handler = gan.Predict (context=context,label=labels,max_epochs=max_epochs,num_gpu=num_gpu,values=values,column=column,logs=logs)
handler.load_meta(column)
r = handler.apply()
_df = df.copy()

4
setup.py
View File

@ -4,9 +4,9 @@ import sys
def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read()
args = {"name":"data-maker","version":"1.0.2","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
args = {"name":"data-maker","version":"1.0.5","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
"packages":find_packages(),"keywords":["healthcare","data","transport","protocol"]}
args["install_requires"] = ['data-transport@git+https://dev.the-phi.com/git/steve/data-transport.git','tensorflow==1.14.0','numpy==1.16.3','pandas','pandas-gbq','pymongo']
args["install_requires"] = ['data-transport@git+https://dev.the-phi.com/git/steve/data-transport.git','tensorflow==1.15','pandas','pandas-gbq','pymongo']
args['url'] = 'https://hiplab.mc.vanderbilt.edu/aou/data-maker.git'
if sys.version_info[0] == 2 :