Merging the release into stable. Handling of GPU and epochs

data/gan.py

@@ -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
     

data/maker/__init__.py

@@ -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()

setup.py

@@ -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 :