From cd67e8eb088537874b53b4fa52d02ff50c4a66fa Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 12 Mar 2018 03:13:15 -0700 Subject: [PATCH] Lint some files. PiperOrigin-RevId: 188698275 --- .../rnn/python/kernel_tests/rnn_cell_test.py | 14 +++++------- tensorflow/contrib/rnn/python/ops/rnn_cell.py | 25 +++++++++++++++------- .../seq2seq/python/ops/beam_search_decoder.py | 1 - 3 files changed, 22 insertions(+), 18 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 69f7b8e..f21915f 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 3028eda..73f2607 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). diff --git a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py index 22dc7f2..6e57ccd 100644 --- a/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py +++ b/tensorflow/contrib/seq2seq/python/ops/beam_search_decoder.py @@ -569,7 +569,6 @@ def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size, time = ops.convert_to_tensor(time, name="time") # During the first time step we only consider the initial beam - scores_shape = array_ops.shape(scores) scores_flat = array_ops.reshape(scores, [batch_size, -1]) # Pick the next beams according to the specified successors function -- 2.7.4