bug fix with dimensionalities and removing conditions

data/gan.py

@@ -79,7 +79,8 @@ class GNet :
                 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.BATCHSIZE_PER_GPU = int(args['real'].shape[0]* 1) if 'real' in args else 256
+                self.BATCHSIZE_PER_GPU = 3000 if 'batch_size' not in args else int(args['batch_size'])
                 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'])
@@ -410,7 +411,7 @@ class Train (GNet):
                         dataset = tf.data.Dataset.from_tensor_slices(features_placeholder)
                 # labels_placeholder = None
                 dataset = dataset.repeat(10000)
-                dataset = dataset.batch(batch_size=3000)
+                dataset = dataset.batch(batch_size=self.BATCHSIZE_PER_GPU)
                 dataset = dataset.prefetch(1)
                 # iterator = dataset.make_initializable_iterator()
                 iterator = tf.compat.v1.data.make_initializable_iterator(dataset)
@@ -430,7 +431,8 @@ class Train (GNet):
                                                         (real, label) = iterator.get_next()
                                                 else:
                                                         real = iterator.get_next()
-                                                loss, w = self.loss(scope=scope, stage=stage, real=self._REAL, label=self._LABEL)
+                                                        label= None
+                                                loss, w = self.loss(scope=scope, stage=stage, real=real, label=label)
                                                 #tf.get_variable_scope().reuse_variables()
                                                 tf.compat.v1.get_variable_scope().reuse_variables()
                                                 #vars_ = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
@@ -465,6 +467,7 @@ class Train (GNet):
                         logs = []
                         #with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
                         with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
+                                
                                 sess.run(init)
                                 
                                 sess.run(iterator_d.initializer,

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":"1.1.0","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
+args = {"name":"data-maker","version":"1.1.1","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/git/aou/data-maker.git'