Copybara import of the project:

--
2b68d08 by Otilia Stretcu <otiliastr@gmail.com>:

Small fixes.

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3265019 by Otilia Stretcu <otiliastr@gmail.com>:

Changing model name.

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dca1457 by Otilia Stretcu <otiliastr@gmail.com>:

Refactoring.

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8a90308 by Otilia Stretcu <otiliastr@gmail.com>:

Refactoring.

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eef1760 by Otilia Stretcu <otiliastr@gmail.com>:

Removed unused function.

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7954700 by Otilia Stretcu <otiliastr@gmail.com>:

Rename run script.

PiperOrigin-RevId: 272069428

Author: Neural-Link Team

…earch/gam/experiments/run_train_mnist.py …esearch/gam/experiments/run_train_gam.pyneural_structured_learning/research/gam/experiments/run_train_mnist.py renamed to neural_structured_learning/research/gam/experiments/run_train_gam.py neural_structured_learning/research/gam/experiments/run_train_mnist.py renamed to neural_structured_learning/research/gam/experiments/run_train_gam.py

@@ -176,14 +176,14 @@
     'String representing the number of units of the hidden layers of the '
     'aggregation network of the agreement model.')
 flags.DEFINE_float(
-    'weight_decay_cls', 0,
+    'weight_decay_cls', None,
     'Weight of the L2 penalty on the classification model weights.')
 flags.DEFINE_string(
     'weight_decay_schedule_cls', None,
     'Schedule for decaying the weight decay in the classification model. '
     'Choose bewteen None or linear.')
 flags.DEFINE_float(
-    'weight_decay_agr', 0,
+    'weight_decay_agr', None,
     'Weight of the L2 penalty on the agreement model weights.')
 flags.DEFINE_string(
     'weight_decay_schedule_agr', None,
@@ -429,17 +429,21 @@ def main(argv):
   model_name += ('_' + FLAGS.hidden_agr) if FLAGS.model_agr == 'mlp' else ''
   model_name += '-aggr_' + FLAGS.aggregation_agr_inputs
   model_name += ('_' + FLAGS.hidden_aggreg) if FLAGS.hidden_aggreg else ''
-  model_name += ('-add_%d-conf_%.2f-iter_cls_%d-iter_agr_%d-batch_cls_%d' %
+  model_name += ('-add_%d-conf_%.2f-iterCls_%d-iterAgr_%d-batchCls_%d' %
                  (FLAGS.num_samples_to_label, FLAGS.min_confidence_new_label,
                   FLAGS.max_num_iter_cls, FLAGS.max_num_iter_agr,
                   FLAGS.batch_size_cls))
+  model_name += (('-wdecayCls_%.4f' % FLAGS.weight_decay_cls)
+                 if FLAGS.weight_decay_cls else '')
+  model_name += (('-wdecayAgr_%.4f' % FLAGS.weight_decay_agr)
+                 if FLAGS.weight_decay_agr else '')
   model_name += '-LL_%s_LU_%s_UU_%s' % (str(
       FLAGS.reg_weight_ll), str(FLAGS.reg_weight_lu), str(FLAGS.reg_weight_uu))
   model_name += '-perfAgr' if FLAGS.use_perfect_agreement else ''
   model_name += '-perfCls' if FLAGS.use_perfect_classifier else ''
   model_name += '-keepProp' if FLAGS.keep_label_proportions else ''
   model_name += '-PenNegAgr' if FLAGS.penalize_neg_agr else ''
-  model_name += '-transduct' if not FLAGS.inductive else ''
+  model_name += '-transd' if not FLAGS.inductive else ''
   model_name += '-L2' if FLAGS.use_l2_cls else '-CE'
   model_name += '-seed_' + str(FLAGS.seed)
   model_name += FLAGS.experiment_suffix

neural_structured_learning/research/gam/models/cnn.py

@@ -289,7 +289,7 @@ def get_loss(self,
         weight decay is applied.
       **kwargs: Keyword arguments, potentially containing the weight of the
         regularization term, passed under the name `weight_decay`. If this is
-        not provided, it defaults to 0.0.
+        not provided, it defaults to 0.004.
 
     Returns:
       loss: The cummulated loss value.

neural_structured_learning/research/gam/models/mlp.py

@@ -227,7 +227,7 @@ def get_loss(self,
       # Weight decay loss.
       if weight_decay is not None:
         for var in reg_params.values():
-          loss += weight_decay * tf.nn.l2_loss(var)
+          loss = loss + weight_decay * tf.nn.l2_loss(var)
     return loss
 
   def normalize_predictions(self, predictions):