@ -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 ,
