Lint some files.

PiperOrigin-RevId: 188698275

2 files changed, 22 insertions, 17 deletions

diff --git a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
index 69f7b8e107..f21915ffbc 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/rnn_cell_test.py
@@ -878,7 +878,6 @@ class RNNCellTest(test.TestCase):
       shape = [2, 1]
       filter_size = [3]
       num_features = 1
-      batch_size = 2
       expected_state_c = np.array(
           [[[1.4375670191], [1.4375670191]], [[2.7542609292], [2.7542609292]]],
           dtype=np.float32)
@@ -912,7 +911,6 @@ class RNNCellTest(test.TestCase):
       shape = [2, 2, 1]
       filter_size = [3, 3]
       num_features = 1
-      batch_size = 2
       expected_state_c = np.array(
           [[[[1.4375670191], [1.4375670191]], [[1.4375670191], [1.4375670191]]],
            [[[2.7542609292], [2.7542609292]], [[2.7542609292], [2.7542609292]]
@@ -954,7 +952,6 @@ class RNNCellTest(test.TestCase):
       shape = [2, 2, 2, 1]
       filter_size = [3, 3, 3]
       num_features = 1
-      batch_size = 2
       expected_state_c = np.array(
           [[[[[1.4375670191], [1.4375670191]], [[1.4375670191], [1.4375670191]]
             ], [[[1.4375670191], [1.4375670191]], [[1.4375670191],
@@ -1584,7 +1581,7 @@ class WeightNormLSTMCellTest(test.TestCase):
   """Compared cell output with pre-calculated values."""
 
   def _cell_output(self, cell):
-    """Calculate cell output"""
+    """Calculates cell output."""
 
     with self.test_session() as sess:
       init = init_ops.constant_initializer(0.5)
@@ -1611,7 +1608,7 @@ class WeightNormLSTMCellTest(test.TestCase):
     return actual_state_c, actual_state_h
 
   def testBasicCell(self):
-    """Tests cell w/o peepholes and w/o normalisation"""
+    """Tests cell w/o peepholes and w/o normalisation."""
 
     def cell():
       return contrib_rnn_cell.WeightNormLSTMCell(
@@ -1626,7 +1623,7 @@ class WeightNormLSTMCellTest(test.TestCase):
     self.assertAllClose(expected_h, actual_h, 1e-5)
 
   def testNonbasicCell(self):
-    """Tests cell with peepholes and w/o normalisation"""
+    """Tests cell with peepholes and w/o normalisation."""
 
     def cell():
       return contrib_rnn_cell.WeightNormLSTMCell(
@@ -1640,9 +1637,8 @@ class WeightNormLSTMCellTest(test.TestCase):
     self.assertAllClose(expected_c, actual_c, 1e-5)
     self.assertAllClose(expected_h, actual_h, 1e-5)
 
-
   def testBasicCellWithNorm(self):
-    """Tests cell w/o peepholes and with normalisation"""
+    """Tests cell w/o peepholes and with normalisation."""
 
     def cell():
       return contrib_rnn_cell.WeightNormLSTMCell(
@@ -1657,7 +1653,7 @@ class WeightNormLSTMCellTest(test.TestCase):
     self.assertAllClose(expected_h, actual_h, 1e-5)
 
   def testNonBasicCellWithNorm(self):
-    """Tests cell with peepholes and with normalisation"""
+    """Tests cell with peepholes and with normalisation."""
 
     def cell():
       return contrib_rnn_cell.WeightNormLSTMCell(
diff --git a/tensorflow/contrib/rnn/python/ops/rnn_cell.py b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
index 3028edad1b..73f2607d84 100644
--- a/tensorflow/contrib/rnn/python/ops/rnn_cell.py
+++ b/tensorflow/contrib/rnn/python/ops/rnn_cell.py
@@ -2058,16 +2058,19 @@ class ConvLSTMCell(rnn_cell_impl.RNNCell):
                initializers=None,
                name="conv_lstm_cell"):
     """Construct ConvLSTMCell.
+
     Args:
       conv_ndims: Convolution dimensionality (1, 2 or 3).
       input_shape: Shape of the input as int tuple, excluding the batch size.
       output_channels: int, number of output channels of the conv LSTM.
       kernel_shape: Shape of kernel as in tuple (of size 1,2 or 3).
-      use_bias: Use bias in convolutions.
+      use_bias: (bool) Use bias in convolutions.
       skip_connection: If set to `True`, concatenate the input to the
-      output of the conv LSTM. Default: `False`.
+        output of the conv LSTM. Default: `False`.
       forget_bias: Forget bias.
+      initializers: Unused.
       name: Name of the module.
+
     Raises:
       ValueError: If `skip_connection` is `True` and stride is different from 1
         or if `input_shape` is incompatible with `conv_ndims`.
@@ -2156,15 +2159,19 @@ class Conv3DLSTMCell(ConvLSTMCell):
 
 
 def _conv(args, filter_size, num_features, bias, bias_start=0.0):
-  """convolution:
+  """Convolution.
+
   Args:
     args: a Tensor or a list of Tensors of dimension 3D, 4D or 5D,
     batch x n, Tensors.
     filter_size: int tuple of filter height and width.
     num_features: int, number of features.
+    bias: Whether to use biases in the convolution layer.
     bias_start: starting value to initialize the bias; 0 by default.
+
   Returns:
     A 3D, 4D, or 5D Tensor with shape [batch ... num_features]
+
   Raises:
     ValueError: if some of the arguments has unspecified or wrong shape.
   """
@@ -2304,7 +2311,7 @@ class GLSTMCell(rnn_cell_impl.RNNCell):
     return self._output_size
 
   def _get_input_for_group(self, inputs, group_id, group_size):
-    """Slices inputs into groups to prepare for processing by cell's groups
+    """Slices inputs into groups to prepare for processing by cell's groups.
 
     Args:
       inputs: cell input or it's previous state,
@@ -2705,7 +2712,7 @@ class LayerNormLSTMCell(rnn_cell_impl.RNNCell):
 
 
 class SRUCell(rnn_cell_impl.LayerRNNCell):
-  """SRU, Simple Recurrent Unit
+  """SRU, Simple Recurrent Unit.
 
      Implementation based on
      Training RNNs as Fast as CNNs (cf. https://arxiv.org/abs/1709.02755).
@@ -2753,12 +2760,13 @@ class SRUCell(rnn_cell_impl.LayerRNNCell):
 
     input_depth = inputs_shape[1].value
 
+    # pylint: disable=protected-access
     self._kernel = self.add_variable(
         rnn_cell_impl._WEIGHTS_VARIABLE_NAME,
         shape=[input_depth, 4 * self._num_units])
-
+    # pylint: enable=protected-access
     self._bias = self.add_variable(
-        rnn_cell_impl._BIAS_VARIABLE_NAME,
+        rnn_cell_impl._BIAS_VARIABLE_NAME,  # pylint: disable=protected-access
         shape=[2 * self._num_units],
         initializer=init_ops.constant_initializer(0.0, dtype=self.dtype))
 
@@ -2767,7 +2775,7 @@ class SRUCell(rnn_cell_impl.LayerRNNCell):
   def call(self, inputs, state):
     """Simple recurrent unit (SRU) with num_units cells."""
 
-    U = math_ops.matmul(inputs, self._kernel)
+    U = math_ops.matmul(inputs, self._kernel)  # pylint: disable=invalid-name
     x_bar, f_intermediate, r_intermediate, x_tx = array_ops.split(
         value=U, num_or_size_splits=4, axis=1)
 
@@ -2897,6 +2905,7 @@ class WeightNormLSTMCell(rnn_cell_impl.RNNCell):
     Args:
       args: a 2D Tensor or a list of 2D, batch x n, Tensors.
       output_size: int, second dimension of W[i].
+      norm: bool, whether to normalize the weights.
       bias: boolean, whether to add a bias term or not.
       bias_initializer: starting value to initialize the bias
         (default is all zeros).