fix: updated tensorflow functions

HISTORY.rst

@@ -51,3 +51,14 @@ History
 * feat: added early_stop_patience argument
 
 
+0.4.4 (2017-02-10)
+------------------
+
+* fix: When X_feat had 0 columns, loading its weights from file was failing.
+* feat: When training the global model in ConciseCV, use the average number of epochs yielding the best validation-set accuracy.
+
+0.4.5 SNAPSHOT 
+------------------
+
+* fix: Update tensorflow function (tf.op_scope -> tf.name_scope, initialize_all_variables -> tf.global_variables_initializer)
+

concise/concise.py

@@ -394,7 +394,7 @@ def model(data, tf_X_feat, var):
             #
             # http://www.subsubroutine.com/sub-subroutine/2016/11/12/painting-like-van-gogh-with-convolutional-neural-networks
 
-            init = tf.initialize_all_variables()
+            init = tf.global_variables_initializer() #tf.initialize_all_variables()
 
             other_var = {
                 "tf_X_feat": tf_X_feat,

concise/tf_helper.py

@@ -14,7 +14,7 @@ def l1_loss(tensor, weight=1.0, scope=None):
     Returns:
       the L1 loss op.
     """
-    with tf.op_scope([tensor], scope, 'L1Loss'):
+    with tf.name_scope(scope, 'L1Loss', [tensor]):
         weight = tf.convert_to_tensor(weight,
                                       dtype=tensor.dtype.base_dtype,
                                       name='loss_weight')
@@ -37,7 +37,7 @@ def huber_loss(tensor, k=1, scope=None):
       the L1 loss op.
     """
     # assert k >= 0
-    with tf.op_scope([tensor], scope, 'L1Loss'):
+    with tf.name_scope(scope, 'L1Loss', [tensor]):
         loss = tf.reduce_mean(tf.select(tf.abs(tensor) < k,
                                         0.5 * tf.square(tensor),
                                         k * tf.abs(tensor) - 0.5 * k ^ 2)