revert temp changes

andrejonasson · Feb 8, 2018 · 023c5ea · 023c5ea
1 parent c907459
commit 023c5ea
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Showing 2 changed files with 3 additions and 4 deletions.
diff --git a/question_answering/networks/dcn_model.py b/question_answering/networks/dcn_model.py
@@ -51,7 +51,7 @@ def __init__(self, pretrained_embeddings, hparams):
 
         # Setup RNN Cells
         cell = lambda: cell_factory(hparams['cell'], hparams['state_size'], self.is_training, hparams['input_keep_prob'], hparams['output_keep_prob'], hparams['state_keep_prob'])
-        final_cell = lambda: cell_factory(hparams['cell'], hparams['state_size'], self.is_training, 1.0, hparams['output_keep_prob'], hparams['state_keep_prob'])  # TODO TEMP hparams['final_input_keep_prob']
+        final_cell = lambda: cell_factory(hparams['cell'], hparams['state_size'], self.is_training, hparams['final_input_keep_prob'], hparams['output_keep_prob'], hparams['state_keep_prob'])  # TODO TEMP
 
         # Setup Encoders
         with tf.variable_scope('prediction'):
@@ -61,7 +61,7 @@ def __init__(self, pretrained_embeddings, hparams):
                 self.encode = dcn_encode
             else:
                 self.encode = dcnplus_encode
-            encoding = self.encode(cell, final_cell, q_embeddings, self.question_length, p_embeddings, self.paragraph_length, keep_prob=maybe_dropout(hparams['keep_prob'], self.is_training), final_input_keep_prob=maybe_dropout(hparams['final_input_keep_prob'], self.is_training))
+            encoding = self.encode(cell, final_cell, q_embeddings, self.question_length, p_embeddings, self.paragraph_length, keep_prob=maybe_dropout(hparams['keep_prob'], self.is_training))
             encoding = tf.nn.dropout(encoding, keep_prob=maybe_dropout(hparams['encoding_keep_prob'], self.is_training))
 
         # Decoder, loss and prediction mechanism are different for baseline/mixed and dcn/dcn_plus

diff --git a/question_answering/networks/dcn_plus.py b/question_answering/networks/dcn_plus.py
@@ -89,7 +89,7 @@ def baseline_encode(cell_factory, final_cell_factory, query, query_length, docum
     return encoding  # N x P x 2H
 
 
-def dcn_encode(cell_factory, final_cell_factory, query, query_length, document, document_length, keep_prob=1.0, final_input_keep_prob=1.0):
+def dcn_encode(cell_factory, final_cell_factory, query, query_length, document, document_length, keep_prob=1.0):
     """ DCN Encoder that encodes questions and paragraphs into one representation.  
 
     It first encodes the question and paragraphs using a shared LSTM, then uses a 
@@ -136,7 +136,6 @@ def dcn_encode(cell_factory, final_cell_factory, query, query_length, document,
 
     with tf.variable_scope('final_encoder'):
         document_representation = tf.concat(document_representations, 2)
-        document_representation = tf.nn.dropout(document_representation, final_input_keep_prob) # test if wanted
         final = final_cell_factory()
         outputs, _ = tf.nn.bidirectional_dynamic_rnn(
             cell_fw = final,