Tweak some NNI params to perform better on the test

michaelhush · e30906a4223f1c977991b063fa4878f31b94a892 · charmasaur committed Dec 9, 2016 · Dec 9, 2016 · e30906a
1 parent 82fa70a
commit e30906a4223f1c977991b063fa4878f31b94a892
Unified Split

Showing with 4 additions and 2 deletions.

+4 −2 mloop/nnlearner.py
diff --git a/mloop/nnlearner.py b/mloop/nnlearner.py
@@ -71,7 +71,7 @@ def _create_neural_net(self):
                 tf.reduce_mean(tf.reduce_sum(tf.square(self.output_var - self.output_placeholder),
                                              reduction_indices=[1]))
                 + self.regularisation_coefficient * sum([tf.nn.l2_loss(W) for W in self.weights]))
-        self.train_step = tf.train.AdamOptimizer().minimize(loss_func)
+        self.train_step = tf.train.AdamOptimizer(1.0).minimize(loss_func)
 
         self.tf_session.run(tf.initialize_all_variables())
 
@@ -104,7 +104,9 @@ def fit_neural_net(self, all_params, all_costs):
                 batch_output = [[all_costs[index]] for index in batch_indices]
                 self.tf_session.run(self.train_step,
                                     feed_dict={self.input_placeholder: batch_input,
-                                               self.output_placeholder: batch_output})
+                                               self.output_placeholder: batch_output,
+                                               self.regularisation_coefficient: 0.01,
+                                               })
 
     def predict_cost(self,params):
         '''