Merge branch 'dev' of aou/data-maker into master

data/gan.py

@@ -193,9 +193,11 @@ class Generator (GNet):
         fake    = args['fake']
         label   = args['label']
         y_hat_fake = self.discriminator.network(inputs=fake, label=label)
-        all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
+        #all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
+        all_regs = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.REGULARIZATION_LOSSES)
         loss = -tf.reduce_mean(y_hat_fake) + sum(all_regs)
-        tf.add_to_collection('glosses', loss)
+        #tf.add_to_collection('glosses', loss)
+        tf.compat.v1.add_to_collection('glosses', loss)
         return loss, loss        
     def load_meta(self, column):
         super().load_meta(column)
@@ -281,10 +283,12 @@ class Discriminator(GNet):
         grad        = tf.gradients(y_hat, [x_hat])[0]
         slopes      = tf.sqrt(tf.reduce_sum(tf.square(grad), 1))
         gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)
-        all_regs    = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
+        #all_regs    = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
+        all_regs    = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.REGULARIZATION_LOSSES)
         w_distance  = -tf.reduce_mean(y_hat_real) + tf.reduce_mean(y_hat_fake)
         loss        = w_distance + 10 * gradient_penalty + sum(all_regs)
-        tf.add_to_collection('dlosses', loss)
+        #tf.add_to_collection('dlosses', loss)
+        tf.compat.v1.add_to_collection('dlosses', loss)
 
         return w_distance, loss        
 class Train (GNet):
@@ -333,10 +337,12 @@ class Train (GNet):
         fake = self.generator.network(inputs=z, label=label)
         if stage == 'D':
             w, loss = self.discriminator.loss(real=real, fake=fake, label=label)
-            losses = tf.get_collection('dlosses', scope)
+            #losses = tf.get_collection('dlosses', scope)
+            losses = tf.compat.v1.get_collection('dlosses', scope)
         else:
             w, loss = self.generator.loss(fake=fake, label=label)
-            losses = tf.get_collection('glosses', scope)
+            #losses = tf.get_collection('glosses', scope)
+            losses = tf.compat.v1.get_collection('glosses', scope)
 
         total_loss = tf.add_n(losses, name='total_loss')
 
@@ -370,8 +376,10 @@ class Train (GNet):
                     with tf.name_scope('%s_%d' % ('TOWER', i)) as scope:
                         (real, label) = iterator.get_next()
                         loss, w = self.loss(scope=scope, stage=stage, real=self._REAL, label=self._LABEL)
-                        tf.get_variable_scope().reuse_variables()
-                        vars_ = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
+                        #tf.get_variable_scope().reuse_variables()
+                        tf.compat.v1.get_variable_scope().reuse_variables()
+                        #vars_ = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
+                        vars_ = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
                         grads = opt.compute_gradients(loss, vars_)
                         tower_grads.append(grads)
                         per_gpu_w.append(w)
@@ -394,9 +402,11 @@ class Train (GNet):
             train_g, _, iterator_g, features_placeholder_g, labels_placeholder_g = self.network(stage='G', opt=opt_g)
             # saver = tf.train.Saver()
             saver   = tf.compat.v1.train.Saver()
-            init    = tf.global_variables_initializer()
+            # init    = tf.global_variables_initializer()
+            init    = tf.compat.v1.global_variables_initializer()
             logs = []
-            with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
+            #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,
                         feed_dict={features_placeholder_d: REAL, labels_placeholder_d: LABEL})

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":"1.0.1","author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
+args = {"name":"data-maker","version":"1.0.2","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['url'] = 'https://hiplab.mc.vanderbilt.edu/aou/data-maker.git'