diff options
Diffstat (limited to 'tensorflow/python/ops')
| -rw-r--r-- | tensorflow/python/ops/array_ops.py | 2 | ||||
| -rw-r--r-- | tensorflow/python/ops/bitwise_ops.py | 1 | ||||
| -rw-r--r-- | tensorflow/python/ops/bitwise_ops_test.py | 23 | ||||
| -rw-r--r-- | tensorflow/python/ops/control_flow_ops.py | 10 | ||||
| -rw-r--r-- | tensorflow/python/ops/init_ops.py | 1 | ||||
| -rw-r--r-- | tensorflow/python/ops/nn_test.py | 18 | ||||
| -rw-r--r-- | tensorflow/python/ops/rnn_cell_impl.py | 18 |
7 files changed, 63 insertions, 10 deletions
diff --git a/tensorflow/python/ops/array_ops.py b/tensorflow/python/ops/array_ops.py index bb86640eab..f64c89ac5d 100644 --- a/tensorflow/python/ops/array_ops.py +++ b/tensorflow/python/ops/array_ops.py @@ -330,7 +330,7 @@ def rank(input, name=None): # pylint: disable=redefined-builtin """Returns the rank of a tensor. - This operation returns an integer representing the rank of `input`. + Returns a 0-D `int32` `Tensor` representing the rank of `input`. For example: diff --git a/tensorflow/python/ops/bitwise_ops.py b/tensorflow/python/ops/bitwise_ops.py index cbabc3ed9b..44daf13537 100644 --- a/tensorflow/python/ops/bitwise_ops.py +++ b/tensorflow/python/ops/bitwise_ops.py @@ -36,5 +36,6 @@ ops.NotDifferentiable("BitwiseAnd") ops.NotDifferentiable("BitwiseOr") ops.NotDifferentiable("BitwiseXor") ops.NotDifferentiable("Invert") +ops.NotDifferentiable("PopulationCount") remove_undocumented(__name__) diff --git a/tensorflow/python/ops/bitwise_ops_test.py b/tensorflow/python/ops/bitwise_ops_test.py index 904cf99a5a..1d08c8f82d 100644 --- a/tensorflow/python/ops/bitwise_ops_test.py +++ b/tensorflow/python/ops/bitwise_ops_test.py @@ -18,10 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import numpy as np +import six + from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util from tensorflow.python.ops import bitwise_ops +from tensorflow.python.ops import gen_bitwise_ops from tensorflow.python.platform import googletest @@ -46,6 +50,25 @@ class BitwiseOpTest(test_util.TensorFlowTestCase): self.assertAllEqual(or_result, [5, 5, 7, 15]) self.assertAllEqual(xor_result, [5, 5, 4, 5]) + def testPopulationCountOp(self): + dtype_list = [dtypes.int8, dtypes.int16, + dtypes.int32, dtypes.int64, + dtypes.uint8, dtypes.uint16] + raw_inputs = [0, 1, -1, 3, -3, 5, -5, 14, -14, + 127, 128, 255, 256, 65535, 65536, + 2**31 - 1, 2**31, 2**32 - 1, 2**32, -2**32 + 1, -2**32, + -2**63 + 1, 2**63 - 1] + def count_bits(x): + return sum([bin(z).count("1") for z in six.iterbytes(x.tobytes())]) + for dtype in dtype_list: + with self.test_session(use_gpu=True) as sess: + print("PopulationCount test: ", dtype) + inputs = np.array(raw_inputs, dtype=dtype.as_numpy_dtype) + truth = [count_bits(x) for x in inputs] + input_tensor = constant_op.constant(inputs, dtype=dtype) + popcnt_result = sess.run(gen_bitwise_ops.population_count(input_tensor)) + self.assertAllEqual(truth, popcnt_result) + def testInvertOp(self): dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8, dtypes.uint16] diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py index 44d6c7e275..4ba812eaf5 100644 --- a/tensorflow/python/ops/control_flow_ops.py +++ b/tensorflow/python/ops/control_flow_ops.py @@ -61,6 +61,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import gen_control_flow_ops @@ -983,9 +984,16 @@ class GradLoopState(object): # the right control flow context. real_value = self._grad_context.AddValue(cur_value) break + elif constant_op.is_constant(cur_value): + # If the value to be forwarded is a constant, clone the constant in + # the gradient loop rather than using a stack. + # TODO(phawkins): consider hoisting the constant out of the loop + # instead. + real_value = constant_op.constant( + tensor_util.constant_value(cur_value), dtype=cur_value.dtype) + break else: # Record the history of this value in forward_ctxt. - # TODO(yuanbyu): Avoid recording constants. self._grad_context.Exit() history_value = cur_grad_state.AddForwardAccumulator(cur_value) self._grad_context.Enter() diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py index 1e2f999995..42b4f952bb 100644 --- a/tensorflow/python/ops/init_ops.py +++ b/tensorflow/python/ops/init_ops.py @@ -41,7 +41,6 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import linalg_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops -from tensorflow.python.ops import math_ops class Initializer(object): diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py index 87f6f92a8a..cc8c623947 100644 --- a/tensorflow/python/ops/nn_test.py +++ b/tensorflow/python/ops/nn_test.py @@ -830,7 +830,8 @@ class ReluTest(test_lib.TestCase): class MomentsTest(test_lib.TestCase): - def doOutputTest(self, input_shape, moments_axes, tol=1e-4): + def doOutputTest(self, input_shape, moments_axes, tol=1e-4, + check_gradients=False): for mu in [0.0, 1.0, 1e3]: for sigma in [1.0, 0.1]: for keep_dims in [True, False]: @@ -846,6 +847,15 @@ class MomentsTest(test_lib.TestCase): mean, variance = nn_impl.moments( inputs, moments_axes, keep_dims=keep_dims) + if check_gradients: + err = gradient_checker.compute_gradient_error( + inputs, input_shape, mean, mean.shape.as_list()) + self.assertLess(err, 1e-3) + err = gradient_checker.compute_gradient_error( + inputs, input_shape, variance, variance.shape.as_list()) + self.assertLess(err, 1e-3) + + # Evaluate. [mean, variance] = sess.run([mean, variance]) # Make sure that there are no NaNs self.assertFalse(np.isnan(mean).any()) @@ -853,6 +863,12 @@ class MomentsTest(test_lib.TestCase): self.assertAllClose(mean, expected_mean, rtol=tol, atol=tol) self.assertAllClose(variance, expected_var, rtol=tol, atol=tol) + def testOutputAndGradient2DInput0(self): + self.doOutputTest((10, 10), (0,), check_gradients=True) + + def testOutputAndGradient2DInput01(self): + self.doOutputTest((10, 10), (0, 1), check_gradients=True) + def testOutput2DInput0(self): self.doOutputTest((10, 300), (0,)) diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py index f7854e86c0..304b6ae665 100644 --- a/tensorflow/python/ops/rnn_cell_impl.py +++ b/tensorflow/python/ops/rnn_cell_impl.py @@ -786,13 +786,18 @@ class DropoutWrapper(RNNCell): class ResidualWrapper(RNNCell): """RNNCell wrapper that ensures cell inputs are added to the outputs.""" - def __init__(self, cell): + def __init__(self, cell, residual_fn=None): """Constructs a `ResidualWrapper` for `cell`. Args: cell: An instance of `RNNCell`. + residual_fn: (Optional) The function to map raw cell inputs and raw cell + outputs to the actual cell outputs of the residual network. + Defaults to calling nest.map_structure on (lambda i, o: i + o), inputs + and outputs. """ self._cell = cell + self._residual_fn = residual_fn @property def state_size(self): @@ -807,7 +812,7 @@ class ResidualWrapper(RNNCell): return self._cell.zero_state(batch_size, dtype) def __call__(self, inputs, state, scope=None): - """Run the cell and add its inputs to its outputs. + """Run the cell and then apply the residual_fn on its inputs to its outputs. Args: inputs: cell inputs. @@ -822,13 +827,14 @@ class ResidualWrapper(RNNCell): ValueError: If cell inputs and outputs have different structure (value). """ outputs, new_state = self._cell(inputs, state, scope=scope) - nest.assert_same_structure(inputs, outputs) # Ensure shapes match def assert_shape_match(inp, out): inp.get_shape().assert_is_compatible_with(out.get_shape()) - nest.map_structure(assert_shape_match, inputs, outputs) - res_outputs = nest.map_structure( - lambda inp, out: inp + out, inputs, outputs) + def default_residual_fn(inputs, outputs): + nest.assert_same_structure(inputs, outputs) + nest.map_structure(assert_shape_match, inputs, outputs) + return nest.map_structure(lambda inp, out: inp + out, inputs, outputs) + res_outputs = (self._residual_fn or default_residual_fn)(inputs, outputs) return (res_outputs, new_state) |