diff --git a/models/dual_encoder.py b/models/dual_encoder.py
index e0e94a6..cb35f0e 100644
--- a/models/dual_encoder.py
+++ b/models/dual_encoder.py
@@ -51,10 +51,10 @@ def dual_encoder_model(
     # Run the utterance and context through the RNN
     rnn_outputs, rnn_states = tf.nn.dynamic_rnn(
         cell,
-        tf.concat(0, [context_embedded, utterance_embedded]),
-        sequence_length=tf.concat(0, [context_len, utterance_len]),
+        tf.concat([context_embedded, utterance_embedded], 0),
+        sequence_length=tf.concat([context_len, utterance_len], 0),
         dtype=tf.float32)
-    encoding_context, encoding_utterance = tf.split(0, 2, rnn_states.h)
+    encoding_context, encoding_utterance = tf.split(rnn_states.h, 2, 0)
 
   with tf.variable_scope("prediction") as vs:
     M = tf.get_variable("M",
@@ -68,7 +68,7 @@ def dual_encoder_model(
 
     # Dot product between generated response and actual response
     # (c * M) * r
-    logits = tf.batch_matmul(generated_response, encoding_utterance, True)
+    logits = tf.matmul(generated_response, encoding_utterance, True)
     logits = tf.squeeze(logits, [2])
 
     # Apply sigmoid to convert logits to probabilities
@@ -78,7 +78,7 @@ def dual_encoder_model(
       return probs, None
 
     # Calculate the binary cross-entropy loss
-    losses = tf.nn.sigmoid_cross_entropy_with_logits(logits, tf.to_float(targets))
+    losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=tf.to_float(targets))
 
   # Mean loss across the batch of examples
   mean_loss = tf.reduce_mean(losses, name="mean_loss")
diff --git a/udc_hparams.py b/udc_hparams.py
index c5747ae..b7ebb0d 100644
--- a/udc_hparams.py
+++ b/udc_hparams.py
@@ -19,8 +19,8 @@
 
 # Training Parameters
 tf.flags.DEFINE_float("learning_rate", 0.001, "Learning rate")
-tf.flags.DEFINE_integer("batch_size", 128, "Batch size during training")
-tf.flags.DEFINE_integer("eval_batch_size", 16, "Batch size during evaluation")
+tf.flags.DEFINE_integer("batch_size", 64, "Batch size during training")
+tf.flags.DEFINE_integer("eval_batch_size", 8, "Batch size during evaluation")
 tf.flags.DEFINE_string("optimizer", "Adam", "Optimizer Name (Adam, Adagrad, etc)")
 
 FLAGS = tf.flags.FLAGS
diff --git a/udc_model.py b/udc_model.py
index 1ad0739..7ef3d39 100644
--- a/udc_model.py
+++ b/udc_model.py
@@ -76,20 +76,20 @@ def model_fn(features, targets, mode):
       probs, loss = model_impl(
           hparams,
           mode,
-          tf.concat(0, all_contexts),
-          tf.concat(0, all_context_lens),
-          tf.concat(0, all_utterances),
-          tf.concat(0, all_utterance_lens),
-          tf.concat(0, all_targets))
+          tf.concat(all_contexts, 0),
+          tf.concat(all_context_lens, 0),
+          tf.concat(all_utterances, 0),
+          tf.concat(all_utterance_lens, 0),
+          tf.concat(all_targets, 0))
 
-      split_probs = tf.split(0, 10, probs)
-      shaped_probs = tf.concat(1, split_probs)
+      split_probs = tf.split(probs, 10, 0)
+      shaped_probs = tf.concat(split_probs, 1)
 
       # Add summaries
-      tf.histogram_summary("eval_correct_probs_hist", split_probs[0])
-      tf.scalar_summary("eval_correct_probs_average", tf.reduce_mean(split_probs[0]))
-      tf.histogram_summary("eval_incorrect_probs_hist", split_probs[1])
-      tf.scalar_summary("eval_incorrect_probs_average", tf.reduce_mean(split_probs[1]))
+      tf.summary.histogram("eval_correct_probs_hist", split_probs[0])
+      tf.summary.scalar("eval_correct_probs_average", tf.reduce_mean(split_probs[0]))
+      tf.summary.histogram("eval_incorrect_probs_hist", split_probs[1])
+      tf.summary.scalar("eval_incorrect_probs_average", tf.reduce_mean(split_probs[1]))
 
       return shaped_probs, loss, None
 
diff --git a/udc_test.py b/udc_test.py
index a23cd50..1844b97 100644
--- a/udc_test.py
+++ b/udc_test.py
@@ -12,7 +12,7 @@
 tf.flags.DEFINE_string("test_file", "./data/test.tfrecords", "Path of test data in TFRecords format")
 tf.flags.DEFINE_string("model_dir", None, "Directory to load model checkpoints from")
 tf.flags.DEFINE_integer("loglevel", 20, "Tensorflow log level")
-tf.flags.DEFINE_integer("test_batch_size", 16, "Batch size for testing")
+tf.flags.DEFINE_integer("test_batch_size", 8, "Batch size for testing")
 FLAGS = tf.flags.FLAGS
 
 if not FLAGS.model_dir: