Update helper.py

seq2seq/contrib/seq2seq/helper.py

@@ -384,10 +384,8 @@ def initialize(self, name=None):
   def sample(self, time, outputs, state, name=None):
     with ops.name_scope(name, "ScheduledOutputTrainingHelperSample",
                         [time, outputs, state]):
-      sampler = bernoulli.Bernoulli(probs=self._sampling_probability)
-      return math_ops.cast(
-          sampler.sample(sample_shape=self.batch_size, seed=self._seed),
-          dtypes.bool)
+      sampler = bernoulli.Bernoulli(probs=self._sampling_probability, dtype=dtypes.int32)
+      return sampler.sample(sample_shape=self.batch_size, seed=self._seed)
 
   def next_inputs(self, time, outputs, state, sample_ids, name=None):
     with ops.name_scope(name, "ScheduledOutputTrainingHelperNextInputs",
@@ -419,7 +417,7 @@ def maybe_concatenate_auxiliary_inputs(outputs_, indices=None):
 
         if self._next_input_layer is None:
           return array_ops.where(
-              sample_ids, maybe_concatenate_auxiliary_inputs(outputs),
+              math_ops.cast(sample_ids, dtypes.bool), maybe_concatenate_auxiliary_inputs(outputs).cell_output,
               base_next_inputs)
 
         where_sampling = math_ops.cast(

seq2seq/test/hooks_test.py

@@ -39,16 +39,16 @@ class TestPrintModelAnalysisHook(tf.test.TestCase):
   def test_begin(self):
     model_dir = tempfile.mkdtemp()
     outfile = tempfile.NamedTemporaryFile()
-    tf.get_variable("weigths", [128, 128])
+    tf.get_variable("weights", [128, 128])
     hook = hooks.PrintModelAnalysisHook(
         params={}, model_dir=model_dir, run_config=tf.contrib.learn.RunConfig())
     hook.begin()
 
     with gfile.GFile(os.path.join(model_dir, "model_analysis.txt")) as file:
-      file_contents = file.read().strip()
+      file_contents = tf.compat.as_text(file.read()).strip()
 
-    self.assertEqual(file_contents.decode(), "_TFProfRoot (--/16.38k params)\n"
-                     "  weigths (128x128, 16.38k/16.38k params)")
+    self.assertEqual(file_contents, "_TFProfRoot (--/16.38k params)\n"
+                     "  weights (128x128, 16.38k/16.38k params)")
     outfile.close()
 
 
@@ -94,7 +94,7 @@ def test_sampling(self):
       outfile = os.path.join(self.sample_dir, "samples_000000.txt")
       with open(outfile, "rb") as readfile:
         self.assertIn("Prediction followed by Target @ Step 0",
-                      readfile.read().decode("utf-8"))
+                      tf.compat.as_text(readfile.read()))
 
       # Should not trigger for step 9
       sess.run(tf.assign(global_step, 9))
@@ -108,7 +108,7 @@ def test_sampling(self):
       outfile = os.path.join(self.sample_dir, "samples_000010.txt")
       with open(outfile, "rb") as readfile:
         self.assertIn("Prediction followed by Target @ Step 10",
-                      readfile.read().decode("utf-8"))
+                      tf.compat.as_text(readfile.read()))
 
 
 class TestMetadataCaptureHook(tf.test.TestCase):
@@ -125,7 +125,7 @@ def tearDown(self):
   def test_capture(self):
     global_step = tf.contrib.framework.get_or_create_global_step()
     # Some test computation
-    some_weights = tf.get_variable("weigths", [2, 128])
+    some_weights = tf.get_variable("weights", [2, 128])
     computation = tf.nn.softmax(some_weights)
 
     hook = hooks.MetadataCaptureHook(