diff options
8 files changed, 42 insertions, 62 deletions
diff --git a/tensorflow/contrib/bayesflow/python/kernel_tests/stochastic_tensor_test.py b/tensorflow/contrib/bayesflow/python/kernel_tests/stochastic_tensor_test.py index 81e40dbe5e..c7f185aab8 100644 --- a/tensorflow/contrib/bayesflow/python/kernel_tests/stochastic_tensor_test.py +++ b/tensorflow/contrib/bayesflow/python/kernel_tests/stochastic_tensor_test.py @@ -42,12 +42,10 @@ class StochasticTensorTest(test.TestCase): sigma2 = constant_op.constant([0.1, 0.2, 0.3]) prior_default = st.StochasticTensor( - distributions.Normal( - loc=mu, scale=sigma)) + distributions.Normal(loc=mu, scale=sigma)) self.assertTrue(isinstance(prior_default.value_type, st.SampleValue)) prior_0 = st.StochasticTensor( - distributions.Normal( - loc=mu, scale=sigma), + distributions.Normal(loc=mu, scale=sigma), dist_value_type=st.SampleValue()) self.assertTrue(isinstance(prior_0.value_type, st.SampleValue)) @@ -55,8 +53,7 @@ class StochasticTensorTest(test.TestCase): prior = st.StochasticTensor(distributions.Normal(loc=mu, scale=sigma)) self.assertTrue(isinstance(prior.value_type, st.SampleValue)) likelihood = st.StochasticTensor( - distributions.Normal( - loc=prior, scale=sigma2)) + distributions.Normal(loc=prior, scale=sigma2)) self.assertTrue(isinstance(likelihood.value_type, st.SampleValue)) coll = ops.get_collection(st.STOCHASTIC_TENSOR_COLLECTION) @@ -102,8 +99,7 @@ class StochasticTensorTest(test.TestCase): with st.value_type(st.SampleValue()): prior_single = st.StochasticTensor( - distributions.Normal( - loc=mu, scale=sigma)) + distributions.Normal(loc=mu, scale=sigma)) prior_single_value = prior_single.value() self.assertEqual(prior_single_value.get_shape(), (2, 3)) @@ -113,8 +109,7 @@ class StochasticTensorTest(test.TestCase): with st.value_type(st.SampleValue(1)): prior_single = st.StochasticTensor( - distributions.Normal( - loc=mu, scale=sigma)) + distributions.Normal(loc=mu, scale=sigma)) self.assertTrue(isinstance(prior_single.value_type, st.SampleValue)) prior_single_value = prior_single.value() @@ -125,8 +120,7 @@ class StochasticTensorTest(test.TestCase): with st.value_type(st.SampleValue(2)): prior_double = st.StochasticTensor( - distributions.Normal( - loc=mu, scale=sigma)) + distributions.Normal(loc=mu, scale=sigma)) prior_double_value = prior_double.value() self.assertEqual(prior_double_value.get_shape(), (2, 2, 3)) @@ -163,8 +157,7 @@ class StochasticTensorTest(test.TestCase): # With passed-in loss_fn. dt = st.StochasticTensor( - distributions.Normal( - loc=mu, scale=sigma), + distributions.Normal(loc=mu, scale=sigma), dist_value_type=st.MeanValue(stop_gradient=True), loss_fn=sge.get_score_function_with_constant_baseline( baseline=constant_op.constant(8.0))) @@ -199,8 +192,7 @@ class ObservedStochasticTensorTest(test.TestCase): sigma = constant_op.constant([1.1, 1.2, 1.3]) obs = array_ops.zeros((2, 3)) z = st.ObservedStochasticTensor( - distributions.Normal( - loc=mu, scale=sigma), value=obs) + distributions.Normal(loc=mu, scale=sigma), value=obs) [obs_val, z_val] = sess.run([obs, z.value()]) self.assertAllEqual(obs_val, z_val) @@ -212,15 +204,13 @@ class ObservedStochasticTensorTest(test.TestCase): sigma = array_ops.placeholder(dtypes.float32) obs = array_ops.placeholder(dtypes.float32) z = st.ObservedStochasticTensor( - distributions.Normal( - loc=mu, scale=sigma), value=obs) + distributions.Normal(loc=mu, scale=sigma), value=obs) mu2 = array_ops.placeholder(dtypes.float32, shape=[None]) sigma2 = array_ops.placeholder(dtypes.float32, shape=[None]) obs2 = array_ops.placeholder(dtypes.float32, shape=[None, None]) z2 = st.ObservedStochasticTensor( - distributions.Normal( - loc=mu2, scale=sigma2), value=obs2) + distributions.Normal(loc=mu2, scale=sigma2), value=obs2) coll = ops.get_collection(st.STOCHASTIC_TENSOR_COLLECTION) self.assertEqual(coll, [z, z2]) @@ -231,22 +221,18 @@ class ObservedStochasticTensorTest(test.TestCase): self.assertRaises( ValueError, st.ObservedStochasticTensor, - distributions.Normal( - loc=mu, scale=sigma), + distributions.Normal(loc=mu, scale=sigma), value=array_ops.zeros((3,))) self.assertRaises( ValueError, st.ObservedStochasticTensor, - distributions.Normal( - loc=mu, scale=sigma), + distributions.Normal(loc=mu, scale=sigma), value=array_ops.zeros((3, 1))) self.assertRaises( ValueError, st.ObservedStochasticTensor, - distributions.Normal( - loc=mu, scale=sigma), - value=array_ops.zeros( - (1, 2), dtype=dtypes.int32)) + distributions.Normal(loc=mu, scale=sigma), + value=array_ops.zeros((1, 2), dtype=dtypes.int32)) if __name__ == "__main__": diff --git a/tensorflow/contrib/distributions/__init__.py b/tensorflow/contrib/distributions/__init__.py index 470b9edb79..e17197080a 100644 --- a/tensorflow/contrib/distributions/__init__.py +++ b/tensorflow/contrib/distributions/__init__.py @@ -135,8 +135,9 @@ from tensorflow.contrib.distributions.python.ops.wishart import * from tensorflow.python.util.all_util import remove_undocumented -_allowed_symbols = ['ConditionalDistribution', - 'ConditionalTransformedDistribution', - 'FULLY_REPARAMETERIZED', 'NOT_REPARAMETERIZED'] +_allowed_symbols = [ + 'ConditionalDistribution', 'ConditionalTransformedDistribution', + 'FULLY_REPARAMETERIZED', 'NOT_REPARAMETERIZED' +] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py index 71460a1769..5d6e4d9197 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/affine_test.py @@ -488,9 +488,7 @@ class AffineBijectorTest(test.TestCase): shift=mu, scale_identity_multiplier=2., scale_perturb_diag=[2., 1], - scale_perturb_factor=[[2., 0], - [0., 0], - [0, 1]]) + scale_perturb_factor=[[2., 0], [0., 0], [0, 1]]) bijector_ref = affine_lib.Affine(shift=mu, scale_diag=[10., 2, 3]) self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" @@ -526,9 +524,7 @@ class AffineBijectorTest(test.TestCase): shift=mu, scale_diag=[2., 3, 4], scale_perturb_diag=[2., 1], - scale_perturb_factor=[[2., 0], - [0., 0], - [0, 1]]) + scale_perturb_factor=[[2., 0], [0., 0], [0, 1]]) bijector_ref = affine_lib.Affine(shift=mu, scale_diag=[10., 3, 5]) self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" @@ -561,17 +557,11 @@ class AffineBijectorTest(test.TestCase): # Corresponds to scale = [[10, 0, 0], [1, 3, 0], [2, 3, 5]] bijector = affine_lib.Affine( shift=mu, - scale_tril=[[2., 0, 0], - [1, 3, 0], - [2, 3, 4]], + scale_tril=[[2., 0, 0], [1, 3, 0], [2, 3, 4]], scale_perturb_diag=[2., 1], - scale_perturb_factor=[[2., 0], - [0., 0], - [0, 1]]) + scale_perturb_factor=[[2., 0], [0., 0], [0, 1]]) bijector_ref = affine_lib.Affine( - shift=mu, scale_tril=[[10., 0, 0], - [1, 3, 0], - [2, 3, 5]]) + shift=mu, scale_tril=[[10., 0, 0], [1, 3, 0], [2, 3, 5]]) self.assertEqual(1, bijector.event_ndims.eval()) # "is vector" x = [1., 2, 3] # Vector. diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/chain_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/chain_test.py index ecf068bf6b..cb514e625b 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/chain_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/chain_test.py @@ -70,7 +70,8 @@ class ChainBijectorTest(test.TestCase): softmax_centered_lib.SoftmaxCentered( event_ndims=1, validate_args=True), softmax_centered_lib.SoftmaxCentered( - event_ndims=0, validate_args=True)]) + event_ndims=0, validate_args=True) + ]) x = tensor_shape.TensorShape([]) y = tensor_shape.TensorShape([2 + 1]) self.assertAllEqual(y, bijector.forward_event_shape(x)) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sigmoid_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sigmoid_test.py index e16f9dff22..40018de63f 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sigmoid_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/sigmoid_test.py @@ -36,17 +36,19 @@ class SigmoidBijectorTest(test.TestCase): y = special.expit(x) ildj = -np.log(y) - np.log1p(-y) self.assertAllClose( - y, sigmoid.Sigmoid().forward(x).eval(), - atol=0., rtol=1e-2) + y, sigmoid.Sigmoid().forward(x).eval(), atol=0., rtol=1e-2) self.assertAllClose( - x, sigmoid.Sigmoid().inverse(y).eval(), - atol=0., rtol=1e-4) + x, sigmoid.Sigmoid().inverse(y).eval(), atol=0., rtol=1e-4) self.assertAllClose( - ildj, sigmoid.Sigmoid().inverse_log_det_jacobian(y).eval(), - atol=0., rtol=1e-6) + ildj, + sigmoid.Sigmoid().inverse_log_det_jacobian(y).eval(), + atol=0., + rtol=1e-6) self.assertAllClose( - -ildj, sigmoid.Sigmoid().forward_log_det_jacobian(x).eval(), - atol=0., rtol=1e-4) + -ildj, + sigmoid.Sigmoid().forward_log_det_jacobian(x).eval(), + atol=0., + rtol=1e-4) def testScalarCongruency(self): with self.test_session(): diff --git a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py index 7fee2e1f3a..e3f6ddd8c0 100644 --- a/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py +++ b/tensorflow/contrib/distributions/python/ops/relaxed_bernoulli.py @@ -171,11 +171,12 @@ class RelaxedBernoulli(transformed_distribution.TransformedDistribution): self._logits, self._probs = distribution_util.get_logits_and_probs( logits=logits, probs=probs, validate_args=validate_args) super(RelaxedBernoulli, self).__init__( - distribution=logistic.Logistic(self._logits / self._temperature, - 1. / self._temperature, - validate_args=validate_args, - allow_nan_stats=allow_nan_stats, - name=name + "/Logistic"), + distribution=logistic.Logistic( + self._logits / self._temperature, + 1. / self._temperature, + validate_args=validate_args, + allow_nan_stats=allow_nan_stats, + name=name + "/Logistic"), bijector=sigmoid_lib.Sigmoid(validate_args=validate_args), validate_args=validate_args, name=name) diff --git a/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_functions.py b/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_functions.py index dfe08bb863..b891bf2301 100644 --- a/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_functions.py +++ b/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_functions.py @@ -25,5 +25,4 @@ from tensorflow.python.estimator.inputs.queues.feeding_functions import _enqueue from tensorflow.python.estimator.inputs.queues.feeding_functions import _GeneratorFeedFn from tensorflow.python.estimator.inputs.queues.feeding_functions import _OrderedDictNumpyFeedFn from tensorflow.python.estimator.inputs.queues.feeding_functions import _PandasFeedFn -from tensorflow.python.estimator.inputs.queues.feeding_functions import _GeneratorFeedFn # pylint: enable=unused-import diff --git a/tensorflow/python/ops/nn_ops.py b/tensorflow/python/ops/nn_ops.py index 901d6a12f6..66ccedf546 100644 --- a/tensorflow/python/ops/nn_ops.py +++ b/tensorflow/python/ops/nn_ops.py @@ -2023,7 +2023,7 @@ def top_k(input, k=1, sorted=True, name=None): def conv1d(value, filters, stride, padding, use_cudnn_on_gpu=None, data_format=None, name=None): - """Computes a 1-D convolution given 3-D input and filter tensors. + r"""Computes a 1-D convolution given 3-D input and filter tensors. Given an input tensor of shape [batch, in_width, in_channels] |