bug fix with dimensions @TODO: GPU workload

data/gan.py

@@ -59,20 +59,27 @@ class GNet :
                 self.logs = {}
 
                 self.NUM_GPUS = 1 if 'num_gpu' not in args else args['num_gpu']
-       
+                if self.NUM_GPUS > 1 :
+                    os.environ['CUDA_VISIBLE_DEVICES'] = "4"
 
                 self.X_SPACE_SIZE = args['real'].shape[1] if 'real' in args else 854
                 self.G_STRUCTURE = [128,128] #[self.X_SPACE_SIZE, self.X_SPACE_SIZE]
                 self.D_STRUCTURE = [self.X_SPACE_SIZE,256,128] #[self.X_SPACE_SIZE, self.X_SPACE_SIZE*2, self.X_SPACE_SIZE] #-- change 854 to number of diagnosis
                 # self.NUM_LABELS         = 8 if 'label' not in args elif len(args['label'].shape) args['label'].shape[1]
+                
                 if 'label' in args and len(args['label'].shape) == 2 :
                         self.NUM_LABELS = args['label'].shape[1]
                 elif 'label' in args and len(args['label']) == 1 :
                         self.NUM_LABELS = args['label'].shape[0]
                 else:
                         self.NUM_LABELS = 8
-        self.Z_DIM = 128 #self.X_SPACE_SIZE     
+                # self.Z_DIM = 128 #self.X_SPACE_SIZE     
-        self.BATCHSIZE_PER_GPU = args['real'].shape[0] if 'real' in args else 256
+                self.Z_DIM = 128  #-- used as rows down stream
+                self.G_STRUCTURE = [self.Z_DIM,self.Z_DIM]
+                if 'real' in args : 
+                                self.D_STRUCTURE = [args['real'].shape[1],256,self.Z_DIM]
+
+                self.BATCHSIZE_PER_GPU = int(args['real'].shape[0]* 1) if 'real' in args else 256
                 self.TOTAL_BATCHSIZE = self.BATCHSIZE_PER_GPU * self.NUM_GPUS
                 self.STEPS_PER_EPOCH = 256 #int(np.load('ICD9/train.npy').shape[0] / 2000)       
                 self.MAX_EPOCHS = 10 if 'max_epochs' not in args else int(args['max_epochs'])
@@ -533,6 +540,8 @@ class Predict(GNet):
                                 # The code below will insure we have some acceptable cardinal relationships between id and synthetic values
                                 #
                                 df =  ( pd.DataFrame(np.round(f).astype(np.int32)))
+                                print (df.head())
+                                print ()
                                 p = 0 not in df.sum(axis=1).values
                                 
                                 if      p:

setup.py

@@ -4,7 +4,7 @@ import sys
 
 def read(fname):
     return open(os.path.join(os.path.dirname(__file__), fname)).read() 
-args = {"name":"data-maker","version":"1.0.8","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
+args = {"name":"data-maker","version":"1.0.9","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==1.15','pandas','pandas-gbq','pymongo']
 args['url'] = 'https://hiplab.mc.vanderbilt.edu/aou/data-maker.git'