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Export Hashing layer. Add separator for CategoryCrossing and tf.sparse.cross. Add benchmarks for hashing.

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PiperOrigin-RevId: 312579726
Change-Id: I0dc5bac26413ec114c57bd59e6810d6c641f600d
This commit is contained in:
Zhenyu Tan 2020-05-20 16:40:34 -07:00 committed by TensorFlower Gardener
parent ae14cc6b1b
commit 26b2581519
20 changed files with 938 additions and 91 deletions
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3
tensorflow/python/keras/layers/__init__.py
View File

@ -57,7 +57,8 @@ else:
from tensorflow.python.keras.layers.preprocessing.text_vectorization_v1 import TextVectorization
from tensorflow.python.keras.layers.preprocessing.text_vectorization import TextVectorization as TextVectorizationV2
TextVectorizationV1 = TextVectorization
from tensorflow.python.keras.layers.preprocessing.categorical_crossing import CategoryCrossing
from tensorflow.python.keras.layers.preprocessing.category_crossing import CategoryCrossing
from tensorflow.python.keras.layers.preprocessing.hashing import Hashing
# Advanced activations.
from tensorflow.python.keras.layers.advanced_activations import LeakyReLU

20
tensorflow/python/keras/layers/preprocessing/BUILD
View File

@ -25,7 +25,7 @@ py_library(
],
srcs_version = "PY2AND3",
deps = [
":categorical_crossing",
":category_crossing",
":discretization",
":hashing",
":image_preprocessing",
@ -52,9 +52,9 @@ py_library(
)
py_library(
name = "categorical_crossing",
name = "category_crossing",
srcs = [
"categorical_crossing.py",
"category_crossing.py",
],
srcs_version = "PY2AND3",
deps = [
@ -291,16 +291,16 @@ py_library(
)
cuda_py_test(
name = "categorical_crossing_test",
name = "category_crossing_test",
size = "medium",
srcs = ["categorical_crossing_test.py"],
srcs = ["category_crossing_test.py"],
python_version = "PY3",
shard_count = 4,
tags = [
"no_windows", # b/149031156
],
deps = [
":categorical_crossing",
":category_crossing",
"//tensorflow/python:client_testlib",
"//third_party/py/numpy",
"@absl_py//absl/testing:parameterized",
@ -343,9 +343,9 @@ distribute_py_test(
)
distribute_py_test(
name = "categorical_crossing_distribution_test",
srcs = ["categorical_crossing_distribution_test.py"],
main = "categorical_crossing_distribution_test.py",
name = "category_crossing_distribution_test",
srcs = ["category_crossing_distribution_test.py"],
main = "category_crossing_distribution_test.py",
python_version = "PY3",
tags = [
"multi_and_single_gpu",
@ -354,7 +354,7 @@ distribute_py_test(
"no_oss", # b/155502591
],
deps = [
":categorical_crossing",
":category_crossing",
"//tensorflow/python/distribute:combinations",
"//tensorflow/python/distribute:strategy_combinations",
"//tensorflow/python/keras",

16
tensorflow/python/keras/layers/preprocessing/benchmarks/BUILD
View File

@ -21,12 +21,22 @@ tf_py_test(
)
tf_py_test(
name = "categorical_crossing_benchmark",
srcs = ["categorical_crossing_benchmark.py"],
name = "category_crossing_benchmark",
srcs = ["category_crossing_benchmark.py"],
python_version = "PY3",
deps = [
"//tensorflow:tensorflow_py",
"//tensorflow/python/keras/layers/preprocessing:categorical_crossing",
"//tensorflow/python/keras/layers/preprocessing:category_crossing",
],
)
tf_py_test(
name = "hashing_benchmark",
srcs = ["hashing_benchmark.py"],
python_version = "PY3",
deps = [
"//tensorflow:tensorflow_py",
"//tensorflow/python/keras/layers/preprocessing:hashing",
],
)

6
tensorflow/python/keras/layers/preprocessing/benchmarks/categorical_crossing_benchmark.py → tensorflow/python/keras/layers/preprocessing/benchmarks/category_crossing_benchmark.py
View File

@ -28,7 +28,7 @@ from tensorflow.python.compat import v2_compat
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import tensor_shape
from tensorflow.python.keras.layers.preprocessing import categorical_crossing
from tensorflow.python.keras.layers.preprocessing import category_crossing
from tensorflow.python.ops import sparse_ops
from tensorflow.python.platform import benchmark
from tensorflow.python.platform import test
@ -74,7 +74,7 @@ class BenchmarkLayer(benchmark.Benchmark):
def bm_layer_implementation(self, batch_size):
input_1 = keras.Input(shape=(1,), dtype=dtypes.int64, name="word")
input_2 = keras.Input(shape=(1,), dtype=dtypes.int64, name="int")
layer = categorical_crossing.CategoryCrossing()
layer = category_crossing.CategoryCrossing()
_ = layer([input_1, input_2])
num_repeats = 5
@ -97,7 +97,7 @@ class BenchmarkLayer(benchmark.Benchmark):
ends.append(time.time())
avg_time = np.mean(np.array(ends) - np.array(starts)) / num_batches
name = "categorical_crossing|batch_%s" % batch_size
name = "category_crossing|batch_%s" % batch_size
baseline = self.run_dataset_implementation(batch_size)
extras = {
"dataset implementation baseline": baseline,

115
tensorflow/python/keras/layers/preprocessing/benchmarks/hashing_benchmark.py Normal file
View File

@ -0,0 +1,115 @@
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Benchmark for Keras hashing preprocessing layer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import random
import string
import time
from absl import flags
import numpy as np
from tensorflow.python import keras
from tensorflow.python.compat import v2_compat
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import tensor_shape
from tensorflow.python.keras.layers.preprocessing import hashing
from tensorflow.python.ops import string_ops
from tensorflow.python.platform import benchmark
from tensorflow.python.platform import test
FLAGS = flags.FLAGS
v2_compat.enable_v2_behavior()
# word_gen creates random sequences of ASCII letters (both lowercase and upper).
# The number of unique strings is ~2,700.
def word_gen():
for _ in itertools.count(1):
yield "".join(random.choice(string.ascii_letters) for i in range(2))
class BenchmarkLayer(benchmark.Benchmark):
"""Benchmark the layer forward pass."""
def run_dataset_implementation(self, batch_size):
num_repeats = 5
starts = []
ends = []
for _ in range(num_repeats):
ds = dataset_ops.Dataset.from_generator(word_gen, dtypes.string,
tensor_shape.TensorShape([]))
ds = ds.shuffle(batch_size * 100)
ds = ds.batch(batch_size)
num_batches = 5
ds = ds.take(num_batches)
ds = ds.prefetch(num_batches)
starts.append(time.time())
# Benchmarked code begins here.
for i in ds:
_ = string_ops.string_to_hash_bucket(i, num_buckets=2)
# Benchmarked code ends here.
ends.append(time.time())
avg_time = np.mean(np.array(ends) - np.array(starts)) / num_batches
return avg_time
def bm_layer_implementation(self, batch_size):
input_1 = keras.Input(shape=(None,), dtype=dtypes.string, name="word")
layer = hashing.Hashing(num_bins=2)
_ = layer(input_1)
num_repeats = 5
starts = []
ends = []
for _ in range(num_repeats):
ds = dataset_ops.Dataset.from_generator(word_gen, dtypes.string,
tensor_shape.TensorShape([]))
ds = ds.shuffle(batch_size * 100)
ds = ds.batch(batch_size)
num_batches = 5
ds = ds.take(num_batches)
ds = ds.prefetch(num_batches)
starts.append(time.time())
# Benchmarked code begins here.
for i in ds:
_ = layer(i)
# Benchmarked code ends here.
ends.append(time.time())
avg_time = np.mean(np.array(ends) - np.array(starts)) / num_batches
name = "hashing|batch_%s" % batch_size
baseline = self.run_dataset_implementation(batch_size)
extras = {
"dataset implementation baseline": baseline,
"delta seconds": (baseline - avg_time),
"delta percent": ((baseline - avg_time) / baseline) * 100
}
self.report_benchmark(
iters=num_repeats, wall_time=avg_time, extras=extras, name=name)
def benchmark_vocab_size_by_batch(self):
for batch in [32, 64, 256]:
self.bm_layer_implementation(batch_size=batch)
if __name__ == "__main__":
test.main()

31
tensorflow/python/keras/layers/preprocessing/categorical_crossing.py → tensorflow/python/keras/layers/preprocessing/category_crossing.py
View File

@ -49,6 +49,17 @@ class CategoryCrossing(Layer):
[b'b_X_e'],
[b'c_X_f']], dtype=object)>
>>> inp_1 = tf.constant([['a'], ['b'], ['c']])
>>> inp_2 = tf.constant([['d'], ['e'], ['f']])
>>> layer = tf.keras.layers.experimental.preprocessing.CategoryCrossing(
... separator='-')
>>> layer([inp_1, inp_2])
<tf.Tensor: shape=(3, 1), dtype=string, numpy=
array([[b'a-d'],
[b'b-e'],
[b'c-f']], dtype=object)>
Arguments:
depth: depth of input crossing. By default None, all inputs are crossed into
one output. It can also be an int or tuple/list of ints. Passing an
@ -59,6 +70,8 @@ class CategoryCrossing(Layer):
equal to N1 or N2. Passing `None` means a single crossed output with all
inputs. For example, with inputs `a`, `b` and `c`, `depth=2` means the
output will be [a;b;c;cross(a, b);cross(bc);cross(ca)].
separator: A string added between each input being joined. Defaults to
'_X_'.
name: Name to give to the layer.
**kwargs: Keyword arguments to construct a layer.
@ -98,13 +111,12 @@ class CategoryCrossing(Layer):
`[[b'1_X_2_X_3'], [b'4_X_5_X_6']]`
"""
def __init__(self,
depth=None,
name=None,
**kwargs):
# TODO(tanzheny): Consider making seperator configurable.
def __init__(self, depth=None, name=None, separator=None, **kwargs):
super(CategoryCrossing, self).__init__(name=name, **kwargs)
self.depth = depth
if separator is None:
separator = '_X_'
self.separator = separator
if isinstance(depth, (tuple, list)):
self._depth_tuple = depth
elif depth is not None:
@ -114,12 +126,16 @@ class CategoryCrossing(Layer):
"""Gets the crossed output from a partial list/tuple of inputs."""
# If ragged_out=True, convert output from sparse to ragged.
if ragged_out:
# TODO(momernick): Support separator with ragged_cross.
if self.separator != '_X_':
raise ValueError('Non-default separator with ragged input is not '
'supported yet, given {}'.format(self.separator))
return ragged_array_ops.cross(partial_inputs)
elif sparse_out:
return sparse_ops.sparse_cross(partial_inputs)
return sparse_ops.sparse_cross(partial_inputs, separator=self.separator)
else:
return sparse_ops.sparse_tensor_to_dense(
sparse_ops.sparse_cross(partial_inputs))
sparse_ops.sparse_cross(partial_inputs, separator=self.separator))
def call(self, inputs):
depth_tuple = self._depth_tuple if self.depth else (len(inputs),)
@ -178,6 +194,7 @@ class CategoryCrossing(Layer):
def get_config(self):
config = {
'depth': self.depth,
'separator': self.separator,
}
base_config = super(CategoryCrossing, self).get_config()
return dict(list(base_config.items()) + list(config.items()))

4
tensorflow/python/keras/layers/preprocessing/categorical_crossing_distribution_test.py → tensorflow/python/keras/layers/preprocessing/category_crossing_distribution_test.py
View File

@ -28,7 +28,7 @@ from tensorflow.python.distribute import tpu_strategy
from tensorflow.python.framework import config
from tensorflow.python.framework import dtypes
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras.layers.preprocessing import categorical_crossing
from tensorflow.python.keras.layers.preprocessing import category_crossing
from tensorflow.python.keras.layers.preprocessing import preprocessing_test_utils
from tensorflow.python.platform import test
@ -72,7 +72,7 @@ class CategoryCrossingDistributionTest(
input_data_2 = keras.Input(shape=(2,), dtype=dtypes.string,
name='input_2')
input_data = [input_data_1, input_data_2]
layer = categorical_crossing.CategoryCrossing()
layer = category_crossing.CategoryCrossing()
int_data = layer(input_data)
model = keras.Model(inputs=input_data, outputs=int_data)
output_dataset = model.predict(inp_dataset)

58
tensorflow/python/keras/layers/preprocessing/categorical_crossing_test.py → tensorflow/python/keras/layers/preprocessing/category_crossing_test.py
View File

@ -29,7 +29,7 @@ from tensorflow.python.framework import test_util as tf_test_util
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras.engine import input_layer
from tensorflow.python.keras.engine import training
from tensorflow.python.keras.layers.preprocessing import categorical_crossing
from tensorflow.python.keras.layers.preprocessing import category_crossing
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops.ragged import ragged_factory_ops
@ -41,7 +41,7 @@ from tensorflow.python.platform import test
class CategoryCrossingTest(keras_parameterized.TestCase):
def test_crossing_sparse_inputs(self):
layer = categorical_crossing.CategoryCrossing()
layer = category_crossing.CategoryCrossing()
inputs_0 = sparse_tensor.SparseTensor(
indices=[[0, 0], [1, 0], [1, 1]],
values=['a', 'b', 'c'],
@ -52,8 +52,32 @@ class CategoryCrossingTest(keras_parameterized.TestCase):
self.assertAllClose(np.asarray([[0, 0], [1, 0], [1, 1]]), output.indices)
self.assertAllEqual([b'a_X_d', b'b_X_e', b'c_X_e'], output.values)
def test_crossing_sparse_inputs_custom_sep(self):
layer = category_crossing.CategoryCrossing(separator='_Y_')
inputs_0 = sparse_tensor.SparseTensor(
indices=[[0, 0], [1, 0], [1, 1]],
values=['a', 'b', 'c'],
dense_shape=[2, 2])
inputs_1 = sparse_tensor.SparseTensor(
indices=[[0, 1], [1, 2]], values=['d', 'e'], dense_shape=[2, 3])
output = layer([inputs_0, inputs_1])
self.assertAllClose(np.asarray([[0, 0], [1, 0], [1, 1]]), output.indices)
self.assertAllEqual([b'a_Y_d', b'b_Y_e', b'c_Y_e'], output.values)
def test_crossing_sparse_inputs_empty_sep(self):
layer = category_crossing.CategoryCrossing(separator='')
inputs_0 = sparse_tensor.SparseTensor(
indices=[[0, 0], [1, 0], [1, 1]],
values=['a', 'b', 'c'],
dense_shape=[2, 2])
inputs_1 = sparse_tensor.SparseTensor(
indices=[[0, 1], [1, 2]], values=['d', 'e'], dense_shape=[2, 3])
output = layer([inputs_0, inputs_1])
self.assertAllClose(np.asarray([[0, 0], [1, 0], [1, 1]]), output.indices)
self.assertAllEqual([b'ad', b'be', b'ce'], output.values)
def test_crossing_sparse_inputs_depth_int(self):
layer = categorical_crossing.CategoryCrossing(depth=1)
layer = category_crossing.CategoryCrossing(depth=1)
inputs_0 = sparse_tensor.SparseTensor(
indices=[[0, 0], [1, 0], [2, 0]],
values=['a', 'b', 'c'],
@ -69,7 +93,7 @@ class CategoryCrossingTest(keras_parameterized.TestCase):
self.assertAllEqual(expected_out, output)
def test_crossing_sparse_inputs_depth_tuple(self):
layer = categorical_crossing.CategoryCrossing(depth=(2, 3))
layer = category_crossing.CategoryCrossing(depth=(2, 3))
inputs_0 = sparse_tensor.SparseTensor(
indices=[[0, 0], [1, 0], [2, 0]],
values=['a', 'b', 'c'],
@ -107,14 +131,14 @@ class CategoryCrossingTest(keras_parameterized.TestCase):
inp_0_t = input_layer.Input(shape=(None,), ragged=True, dtype=dtypes.string)
inp_1_t = input_layer.Input(shape=(None,), ragged=True, dtype=dtypes.string)
non_hashed_layer = categorical_crossing.CategoryCrossing()
non_hashed_layer = category_crossing.CategoryCrossing()
out_t = non_hashed_layer([inp_0_t, inp_1_t])
model = training.Model(inputs=[inp_0_t, inp_1_t], outputs=out_t)
expected_output = [[b'omar_X_a', b'skywalker_X_a'], [b'marlo_X_b']]
self.assertAllEqual(expected_output, model.predict([inputs_0, inputs_1]))
def test_crossing_ragged_inputs_depth_int(self):
layer = categorical_crossing.CategoryCrossing(depth=1)
layer = category_crossing.CategoryCrossing(depth=1)
inputs_0 = ragged_factory_ops.constant([['a'], ['b'], ['c']])
inputs_1 = ragged_factory_ops.constant([['d'], ['e'], ['f']])
output = layer([inputs_0, inputs_1])
@ -122,7 +146,7 @@ class CategoryCrossingTest(keras_parameterized.TestCase):
self.assertIsInstance(output, ragged_tensor.RaggedTensor)
self.assertAllEqual(expected_output, output)
layer = categorical_crossing.CategoryCrossing(depth=2)
layer = category_crossing.CategoryCrossing(depth=2)
inp_0_t = input_layer.Input(shape=(None,), ragged=True, dtype=dtypes.string)
inp_1_t = input_layer.Input(shape=(None,), ragged=True, dtype=dtypes.string)
out_t = layer([inp_0_t, inp_1_t])
@ -132,7 +156,7 @@ class CategoryCrossingTest(keras_parameterized.TestCase):
self.assertAllEqual(expected_output, model.predict([inputs_0, inputs_1]))
def test_crossing_ragged_inputs_depth_tuple(self):
layer = categorical_crossing.CategoryCrossing(depth=[2, 3])
layer = category_crossing.CategoryCrossing(depth=[2, 3])
inputs_0 = ragged_factory_ops.constant([['a'], ['b'], ['c']])
inputs_1 = ragged_factory_ops.constant([['d'], ['e'], ['f']])
inputs_2 = ragged_factory_ops.constant([['g'], ['h'], ['i']])
@ -149,21 +173,21 @@ class CategoryCrossingTest(keras_parameterized.TestCase):
self.assertAllEqual(expected_output, output)
def test_crossing_with_dense_inputs(self):
layer = categorical_crossing.CategoryCrossing()
layer = category_crossing.CategoryCrossing()
inputs_0 = np.asarray([[1, 2]])
inputs_1 = np.asarray([[1, 3]])
output = layer([inputs_0, inputs_1])
self.assertAllEqual([[b'1_X_1', b'1_X_3', b'2_X_1', b'2_X_3']], output)
def test_crossing_dense_inputs_depth_int(self):
layer = categorical_crossing.CategoryCrossing(depth=1)
layer = category_crossing.CategoryCrossing(depth=1)
inputs_0 = constant_op.constant([['a'], ['b'], ['c']])
inputs_1 = constant_op.constant([['d'], ['e'], ['f']])
output = layer([inputs_0, inputs_1])
expected_output = [[b'a', b'd'], [b'b', b'e'], [b'c', b'f']]
self.assertAllEqual(expected_output, output)
layer = categorical_crossing.CategoryCrossing(depth=2)
layer = category_crossing.CategoryCrossing(depth=2)
inp_0_t = input_layer.Input(shape=(1,), dtype=dtypes.string)
inp_1_t = input_layer.Input(shape=(1,), dtype=dtypes.string)
out_t = layer([inp_0_t, inp_1_t])
@ -174,7 +198,7 @@ class CategoryCrossingTest(keras_parameterized.TestCase):
self.assertAllEqual(expected_output, model.predict([inputs_0, inputs_1]))
def test_crossing_dense_inputs_depth_tuple(self):
layer = categorical_crossing.CategoryCrossing(depth=[2, 3])
layer = category_crossing.CategoryCrossing(depth=[2, 3])
inputs_0 = constant_op.constant([['a'], ['b'], ['c']])
inputs_1 = constant_op.constant([['d'], ['e'], ['f']])
inputs_2 = constant_op.constant([['g'], ['h'], ['i']])
@ -200,21 +224,21 @@ class CategoryCrossingTest(keras_parameterized.TestCase):
tensor_spec.TensorSpec(input_shape, dtypes.string)
for input_shape in input_shapes
]
layer = categorical_crossing.CategoryCrossing()
layer = category_crossing.CategoryCrossing()
output_spec = layer.compute_output_signature(input_specs)
self.assertEqual(output_spec.shape.dims[0], input_shapes[0].dims[0])
self.assertEqual(output_spec.dtype, dtypes.string)
@tf_test_util.run_v2_only
def test_config_with_custom_name(self):
layer = categorical_crossing.CategoryCrossing(depth=2, name='hashing')
layer = category_crossing.CategoryCrossing(depth=2, name='hashing')
config = layer.get_config()
layer_1 = categorical_crossing.CategoryCrossing.from_config(config)
layer_1 = category_crossing.CategoryCrossing.from_config(config)
self.assertEqual(layer_1.name, layer.name)
layer = categorical_crossing.CategoryCrossing(name='hashing')
layer = category_crossing.CategoryCrossing(name='hashing')
config = layer.get_config()
layer_1 = categorical_crossing.CategoryCrossing.from_config(config)
layer_1 = category_crossing.CategoryCrossing.from_config(config)
self.assertEqual(layer_1.name, layer.name)

176
tensorflow/python/keras/layers/preprocessing/hashing.py
View File

@ -22,20 +22,28 @@ import functools
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_spec
from tensorflow.python.keras.engine.base_layer import Layer
from tensorflow.python.ops import gen_sparse_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import string_ops
from tensorflow.python.ops.ragged import ragged_functional_ops
from tensorflow.python.ops.ragged import ragged_tensor
from tensorflow.python.util.tf_export import keras_export
# Default key from tf.sparse.cross_hashed
_DEFAULT_SALT_KEY = [0xDECAFCAFFE, 0xDECAFCAFFE]
@keras_export('keras.layers.experimental.preprocessing.Hashing')
class Hashing(Layer):
"""Implements categorical feature hashing, also known as "hashing trick".
This layer transforms categorical inputs to hashed output. It converts a
sequence of int or string to a sequence of int. The stable hash function uses
tensorflow::ops::Fingerprint to produce universal output that is consistent
across platforms.
This layer transforms single or multiple categorical inputs to hashed output.
It converts a sequence of int or string to a sequence of int. The stable hash
function uses tensorflow::ops::Fingerprint to produce universal output that
is consistent across platforms.
This layer uses [FarmHash64](https://github.com/google/farmhash) by default,
which provides a consistent hashed output across different platforms and is
@ -48,50 +56,91 @@ class Hashing(Layer):
the `salt` value serving as additional input to the hash function.
Example (FarmHash64):
```python
layer = Hashing(num_bins=3)
inp = np.asarray([['A'], ['B'], ['C'], ['D'], ['E']])
layer(inputs)
[[1], [0], [1], [1], [2]]
```
>>> layer = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=3)
>>> inp = np.asarray([['A'], ['B'], ['C'], ['D'], ['E']])
>>> layer(inp)
<tf.Tensor: shape=(5, 1), dtype=int64, numpy=
array([[1],
[0],
[1],
[1],
[2]])>
Example (SipHash64):
```python
layer = Hashing(num_bins=3, salt=[133, 137])
inp = np.asarray([['A'], ['B'], ['C'], ['D'], ['E']])
layer(inputs)
[[1], [2], [1], [0], [2]]
```
>>> layer = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=3,
... salt=[133, 137])
>>> inp = np.asarray([['A'], ['B'], ['C'], ['D'], ['E']])
>>> layer(inp)
<tf.Tensor: shape=(5, 1), dtype=int64, numpy=
array([[1],
[2],
[1],
[0],
[2]])>
Example (Siphash64 with a single integer, same as `salt=[133, 133]`
>>> layer = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=3,
... salt=133)
>>> inp = np.asarray([['A'], ['B'], ['C'], ['D'], ['E']])
>>> layer(inp)
<tf.Tensor: shape=(5, 1), dtype=int64, numpy=
array([[0],
[0],
[2],
[1],
[0]])>
Reference: [SipHash with salt](https://www.131002.net/siphash/siphash.pdf)
Arguments:
num_bins: Number of hash bins.
salt: A tuple/list of 2 unsigned integer numbers. If passed, the hash
function used will be SipHash64, with these values used as an additional
input (known as a "salt" in cryptography).
salt: A single unsigned integer or None.
If passed, the hash function used will be SipHash64, with these values
used as an additional input (known as a "salt" in cryptography).
These should be non-zero. Defaults to `None` (in that
case, the FarmHash64 hash function is used).
case, the FarmHash64 hash function is used). It also supports
tuple/list of 2 unsigned integer numbers, see reference paper for details.
name: Name to give to the layer.
**kwargs: Keyword arguments to construct a layer.
Input shape: A string, int32 or int64 tensor of shape
`[batch_size, d1, ..., dm]`
Input shape: A single or list of string, int32 or int64 `Tensor`,
`SparseTensor` or `RaggedTensor` of shape `[batch_size, ...,]`
Output shape: An int64 tensor of shape `[batch_size, d1, ..., dm]`
Output shape: An int64 `Tensor`, `SparseTensor` or `RaggedTensor` of shape
`[batch_size, ...]`. If any input is `RaggedTensor` then output is
`RaggedTensor`, otherwise if any input is `SparseTensor` then output is
`SparseTensor`, otherwise the output is `Tensor`.
"""
def __init__(self, num_bins, salt=None, name=None, **kwargs):
if num_bins is None or num_bins <= 0:
raise ValueError('`num_bins` cannot be `None` or non-positive values.')
if salt is not None:
if not isinstance(salt, (tuple, list)) or len(salt) != 2:
raise ValueError('`salt` must be a tuple or list of 2 unsigned '
'integer numbers, got {}'.format(salt))
super(Hashing, self).__init__(name=name, **kwargs)
self.num_bins = num_bins
self.salt = salt
self.strong_hash = True if salt is not None else False
if salt is not None:
if isinstance(salt, (tuple, list)) and len(salt) == 2:
self.salt = salt
elif isinstance(salt, int):
self.salt = [salt, salt]
else:
raise ValueError('`salt can only be a tuple of size 2 integers, or a '
'single integer, given {}'.format(salt))
else:
self.salt = _DEFAULT_SALT_KEY
def call(self, inputs):
if isinstance(inputs, (tuple, list)):
return self._process_input_list(inputs)
else:
return self._process_single_input(inputs)
def _process_single_input(self, inputs):
# Converts integer inputs to string.
if inputs.dtype.is_integer:
if isinstance(inputs, sparse_tensor.SparseTensor):
@ -116,10 +165,38 @@ class Hashing(Layer):
else:
return str_to_hash_bucket(inputs, self.num_bins, name='hash')
def _process_input_list(self, inputs):
# TODO(momernick): support ragged_cross_hashed with corrected fingerprint
# and siphash.
if any([isinstance(inp, ragged_tensor.RaggedTensor) for inp in inputs]):
raise ValueError('Hashing with ragged input is not supported yet.')
sparse_inputs = [
inp for inp in inputs if isinstance(inp, sparse_tensor.SparseTensor)
]
dense_inputs = [
inp for inp in inputs if not isinstance(inp, sparse_tensor.SparseTensor)
]
all_dense = True if not sparse_inputs else False
indices = [sp_inp.indices for sp_inp in sparse_inputs]
values = [sp_inp.values for sp_inp in sparse_inputs]
shapes = [sp_inp.dense_shape for sp_inp in sparse_inputs]
indices_out, values_out, shapes_out = gen_sparse_ops.sparse_cross_hashed(
indices=indices,
values=values,
shapes=shapes,
dense_inputs=dense_inputs,
num_buckets=self.num_bins,
strong_hash=self.strong_hash,
salt=self.salt)
sparse_out = sparse_tensor.SparseTensor(indices_out, values_out, shapes_out)
if all_dense:
return sparse_ops.sparse_tensor_to_dense(sparse_out)
return sparse_out
def _get_string_to_hash_bucket_fn(self):
"""Returns the string_to_hash_bucket op to use based on `hasher_key`."""
# string_to_hash_bucket_fast uses FarmHash64 as hash function.
if self.salt is None:
if not self.strong_hash:
return string_ops.string_to_hash_bucket_fast
# string_to_hash_bucket_strong uses SipHash64 as hash function.
else:
@ -127,16 +204,43 @@ class Hashing(Layer):
string_ops.string_to_hash_bucket_strong, key=self.salt)
def compute_output_shape(self, input_shape):
return input_shape
if not isinstance(input_shape, (tuple, list)):
return input_shape
input_shapes = input_shape
batch_size = None
for inp_shape in input_shapes:
inp_tensor_shape = tensor_shape.TensorShape(inp_shape).as_list()
if len(inp_tensor_shape) != 2:
raise ValueError('Inputs must be rank 2, get {}'.format(input_shapes))
if batch_size is None:
batch_size = inp_tensor_shape[0]
# The second dimension is dynamic based on inputs.
output_shape = [batch_size, None]
return tensor_shape.TensorShape(output_shape)
def compute_output_signature(self, input_spec):
output_shape = self.compute_output_shape(input_spec.shape.as_list())
output_dtype = dtypes.int64
if isinstance(input_spec, sparse_tensor.SparseTensorSpec):
if not isinstance(input_spec, (tuple, list)):
output_shape = self.compute_output_shape(input_spec.shape)
output_dtype = dtypes.int64
if isinstance(input_spec, sparse_tensor.SparseTensorSpec):
return sparse_tensor.SparseTensorSpec(
shape=output_shape, dtype=output_dtype)
else:
return tensor_spec.TensorSpec(shape=output_shape, dtype=output_dtype)
input_shapes = [x.shape for x in input_spec]
output_shape = self.compute_output_shape(input_shapes)
if any([
isinstance(inp_spec, ragged_tensor.RaggedTensorSpec)
for inp_spec in input_spec
]):
return tensor_spec.TensorSpec(shape=output_shape, dtype=dtypes.int64)
elif any([
isinstance(inp_spec, sparse_tensor.SparseTensorSpec)
for inp_spec in input_spec
]):
return sparse_tensor.SparseTensorSpec(
shape=output_shape, dtype=output_dtype)
else:
return tensor_spec.TensorSpec(shape=output_shape, dtype=output_dtype)
shape=output_shape, dtype=dtypes.int64)
return tensor_spec.TensorSpec(shape=output_shape, dtype=dtypes.int64)
def get_config(self):
config = {'num_bins': self.num_bins, 'salt': self.salt}

84
tensorflow/python/keras/layers/preprocessing/hashing_test.py
View File

@ -51,6 +51,15 @@ class HashingTest(keras_parameterized.TestCase):
# Assert equal for hashed output that should be true on all platforms.
self.assertAllClose([[0], [0], [1], [0], [0]], output)
def test_hash_dense_multi_inputs_farmhash(self):
layer = hashing.Hashing(num_bins=2)
inp_1 = np.asarray([['omar'], ['stringer'], ['marlo'], ['wire'],
['skywalker']])
inp_2 = np.asarray([['A'], ['B'], ['C'], ['D'], ['E']])
output = layer([inp_1, inp_2])
# Assert equal for hashed output that should be true on all platforms.
self.assertAllClose([[0], [0], [1], [1], [0]], output)
def test_hash_dense_int_input_farmhash(self):
layer = hashing.Hashing(num_bins=3)
inp = np.asarray([[0], [1], [2], [3], [4]])
@ -72,6 +81,21 @@ class HashingTest(keras_parameterized.TestCase):
# Note the result is different from (133, 137).
self.assertAllClose([[1], [0], [1], [0], [1]], output_2)
def test_hash_dense_multi_inputs_siphash(self):
layer = hashing.Hashing(num_bins=2, salt=[133, 137])
inp_1 = np.asarray([['omar'], ['stringer'], ['marlo'], ['wire'],
['skywalker']])
inp_2 = np.asarray([['A'], ['B'], ['C'], ['D'], ['E']])
output = layer([inp_1, inp_2])
# Assert equal for hashed output that should be true on all platforms.
# Note the result is different from FarmHash.
self.assertAllClose([[0], [1], [0], [0], [1]], output)
layer_2 = hashing.Hashing(num_bins=2, salt=[211, 137])
output_2 = layer_2([inp_1, inp_2])
# Note the result is different from (133, 137).
self.assertAllClose([[1], [1], [1], [0], [1]], output_2)
def test_hash_dense_int_input_siphash(self):
layer = hashing.Hashing(num_bins=3, salt=[133, 137])
inp = np.asarray([[0], [1], [2], [3], [4]])
@ -90,6 +114,19 @@ class HashingTest(keras_parameterized.TestCase):
self.assertAllClose(indices, output.indices)
self.assertAllClose([0, 0, 1, 0, 0], output.values)
def test_hash_sparse_multi_inputs_farmhash(self):
layer = hashing.Hashing(num_bins=2)
indices = [[0, 0], [1, 0], [2, 0]]
inp_1 = sparse_tensor.SparseTensor(
indices=indices,
values=['omar', 'stringer', 'marlo'],
dense_shape=[3, 1])
inp_2 = sparse_tensor.SparseTensor(
indices=indices, values=['A', 'B', 'C'], dense_shape=[3, 1])
output = layer([inp_1, inp_2])
self.assertAllClose(indices, output.indices)
self.assertAllClose([0, 0, 1], output.values)
def test_hash_sparse_int_input_farmhash(self):
layer = hashing.Hashing(num_bins=3)
indices = [[0, 0], [1, 0], [1, 1], [2, 0], [2, 1]]
@ -116,6 +153,25 @@ class HashingTest(keras_parameterized.TestCase):
# The result should be same with test_hash_dense_input_siphash.
self.assertAllClose([1, 0, 1, 0, 1], output.values)
def test_hash_sparse_multi_inputs_siphash(self):
layer = hashing.Hashing(num_bins=2, salt=[133, 137])
indices = [[0, 0], [1, 0], [2, 0]]
inp_1 = sparse_tensor.SparseTensor(
indices=indices,
values=['omar', 'stringer', 'marlo'],
dense_shape=[3, 1])
inp_2 = sparse_tensor.SparseTensor(
indices=indices, values=['A', 'B', 'C'], dense_shape=[3, 1])
output = layer([inp_1, inp_2])
# The result should be same with test_hash_dense_input_siphash.
self.assertAllClose(indices, output.indices)
self.assertAllClose([0, 1, 0], output.values)
layer_2 = hashing.Hashing(num_bins=2, salt=[211, 137])
output = layer_2([inp_1, inp_2])
# The result should be same with test_hash_dense_input_siphash.
self.assertAllClose([1, 1, 1], output.values)
def test_hash_sparse_int_input_siphash(self):
layer = hashing.Hashing(num_bins=3, salt=[133, 137])
indices = [[0, 0], [1, 0], [1, 1], [2, 0], [2, 1]]
@ -140,6 +196,17 @@ class HashingTest(keras_parameterized.TestCase):
model = training.Model(inputs=inp_t, outputs=out_t)
self.assertAllClose(out_data, model.predict(inp_data))
def test_hash_ragged_string_multi_inputs_farmhash(self):
layer = hashing.Hashing(num_bins=2)
inp_data_1 = ragged_factory_ops.constant(
[['omar', 'stringer', 'marlo', 'wire'], ['marlo', 'skywalker', 'wire']],
dtype=dtypes.string)
inp_data_2 = ragged_factory_ops.constant(
[['omar', 'stringer', 'marlo', 'wire'], ['marlo', 'skywalker', 'wire']],
dtype=dtypes.string)
with self.assertRaisesRegexp(ValueError, 'not supported yet'):
_ = layer([inp_data_1, inp_data_2])
def test_hash_ragged_int_input_farmhash(self):
layer = hashing.Hashing(num_bins=3)
inp_data = ragged_factory_ops.constant([[0, 1, 3, 4], [2, 1, 0]],
@ -178,6 +245,17 @@ class HashingTest(keras_parameterized.TestCase):
model = training.Model(inputs=inp_t, outputs=out_t)
self.assertAllClose(out_data, model.predict(inp_data))
def test_hash_ragged_string_multi_inputs_siphash(self):
layer = hashing.Hashing(num_bins=2, salt=[133, 137])
inp_data_1 = ragged_factory_ops.constant(
[['omar', 'stringer', 'marlo', 'wire'], ['marlo', 'skywalker', 'wire']],
dtype=dtypes.string)
inp_data_2 = ragged_factory_ops.constant(
[['omar', 'stringer', 'marlo', 'wire'], ['marlo', 'skywalker', 'wire']],
dtype=dtypes.string)
with self.assertRaisesRegexp(ValueError, 'not supported yet'):
_ = layer([inp_data_1, inp_data_2])
def test_hash_ragged_int_input_siphash(self):
layer = hashing.Hashing(num_bins=3, salt=[133, 137])
inp_data = ragged_factory_ops.constant([[0, 1, 3, 4], [2, 1, 0]],
@ -197,11 +275,11 @@ class HashingTest(keras_parameterized.TestCase):
_ = hashing.Hashing(num_bins=None)
with self.assertRaisesRegexp(ValueError, 'cannot be `None`'):
_ = hashing.Hashing(num_bins=-1)
with self.assertRaisesRegexp(ValueError, 'must be a tuple'):
with self.assertRaisesRegexp(ValueError, 'can only be a tuple of size 2'):
_ = hashing.Hashing(num_bins=2, salt='string')
with self.assertRaisesRegexp(ValueError, 'must be a tuple'):
with self.assertRaisesRegexp(ValueError, 'can only be a tuple of size 2'):
_ = hashing.Hashing(num_bins=2, salt=[1])
with self.assertRaisesRegexp(ValueError, 'must be a tuple'):
with self.assertRaisesRegexp(ValueError, 'can only be a tuple of size 2'):
_ = hashing.Hashing(num_bins=1, salt=constant_op.constant([133, 137]))
def test_hash_compute_output_signature(self):

4
tensorflow/python/keras/layers/serialization.py
View File

@ -45,6 +45,8 @@ from tensorflow.python.keras.layers import recurrent
from tensorflow.python.keras.layers import recurrent_v2
from tensorflow.python.keras.layers import rnn_cell_wrapper_v2
from tensorflow.python.keras.layers import wrappers
from tensorflow.python.keras.layers.preprocessing import category_crossing
from tensorflow.python.keras.layers.preprocessing import hashing
from tensorflow.python.keras.layers.preprocessing import image_preprocessing
from tensorflow.python.keras.layers.preprocessing import normalization as preprocessing_normalization
from tensorflow.python.keras.layers.preprocessing import normalization_v1 as preprocessing_normalization_v1
@ -60,7 +62,7 @@ ALL_MODULES = (base_layer, input_layer, advanced_activations, convolutional,
embeddings, einsum_dense, local, merge, noise, normalization,
pooling, image_preprocessing, preprocessing_normalization_v1,
preprocessing_text_vectorization_v1,
recurrent, wrappers)
recurrent, wrappers, hashing, category_crossing)
ALL_V2_MODULES = (
rnn_cell_wrapper_v2,
normalization_v2,

56
tensorflow/python/ops/sparse_ops.py
View File

@ -27,6 +27,7 @@ import numbers
import numpy as np
from tensorflow.python.compat import compat as tf_compat
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
@ -569,7 +570,7 @@ def sparse_add_v2(a, b, threshold=0):
@tf_export("sparse.cross")
def sparse_cross(inputs, name=None):
def sparse_cross(inputs, name=None, separator=None):
"""Generates sparse cross from a list of sparse and dense tensors.
For example, if the inputs are
@ -590,14 +591,39 @@ def sparse_cross(inputs, name=None):
[1, 0]: "b_X_e_X_g"
[1, 1]: "c_X_e_X_g"
Customized separator "_Y_":
>>> inp_0 = tf.constant([['a'], ['b']])
>>> inp_1 = tf.constant([['c'], ['d']])
>>> output = tf.sparse.cross([inp_0, inp_1], separator='_Y_')
>>> output.values
<tf.Tensor: shape=(2,), dtype=string, numpy=array([b'a_Y_c', b'b_Y_d'],
dtype=object)>
Args:
inputs: An iterable of `Tensor` or `SparseTensor`.
name: Optional name for the op.
separator: A string added between each string being joined. Defaults to
'_X_'.
Returns:
A `SparseTensor` of type `string`.
"""
return _sparse_cross_internal(inputs=inputs, hashed_output=False, name=name)
if separator is None and not tf_compat.forward_compatible(2020, 6, 14):
return _sparse_cross_internal(inputs=inputs, hashed_output=False, name=name)
if separator is None:
separator = "_X_"
separator = ops.convert_to_tensor(separator, dtypes.string)
indices, values, shapes, dense_inputs = _sparse_cross_internval_v2(inputs)
indices_out, values_out, shape_out = gen_sparse_ops.sparse_cross_v2(
indices=indices,
values=values,
shapes=shapes,
dense_inputs=dense_inputs,
sep=separator,
name=name)
return sparse_tensor.SparseTensor(indices_out, values_out, shape_out)
_sparse_cross = sparse_cross
@ -655,6 +681,32 @@ _sparse_cross_hashed = sparse_cross_hashed
_DEFAULT_HASH_KEY = 0xDECAFCAFFE
def _sparse_cross_internval_v2(inputs):
"""See gen_sparse_ops.sparse_cross_v2."""
if not isinstance(inputs, (tuple, list)):
raise TypeError("Inputs must be a list")
if not all(
isinstance(i, sparse_tensor.SparseTensor) or isinstance(i, ops.Tensor)
for i in inputs):
raise TypeError("All inputs must be Tensor or SparseTensor.")
sparse_inputs = [
i for i in inputs if isinstance(i, sparse_tensor.SparseTensor)
]
dense_inputs = [
i for i in inputs if not isinstance(i, sparse_tensor.SparseTensor)
]
indices = [sp_input.indices for sp_input in sparse_inputs]
values = [sp_input.values for sp_input in sparse_inputs]
shapes = [sp_input.dense_shape for sp_input in sparse_inputs]
for i in range(len(values)):
if values[i].dtype != dtypes.string:
values[i] = math_ops.cast(values[i], dtypes.int64)
for i in range(len(dense_inputs)):
if dense_inputs[i].dtype != dtypes.string:
dense_inputs[i] = math_ops.cast(dense_inputs[i], dtypes.int64)
return indices, values, shapes, dense_inputs
def _sparse_cross_internal(inputs,
hashed_output=False,
num_buckets=0,

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.experimental.preprocessing.-category-crossing.pbtxt
View File

@ -1,6 +1,6 @@
path: "tensorflow.keras.layers.experimental.preprocessing.CategoryCrossing"
tf_class {
is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.categorical_crossing.CategoryCrossing\'>"
is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.category_crossing.CategoryCrossing\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
@ -113,7 +113,7 @@ tf_class {
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'depth\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\'], "
argspec: "args=[\'self\', \'depth\', \'name\', \'separator\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\'], "
}
member_method {
name: "add_loss"

218
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.experimental.preprocessing.-hashing.pbtxt Normal file
View File

@ -0,0 +1,218 @@
path: "tensorflow.keras.layers.experimental.preprocessing.Hashing"
tf_class {
is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.hashing.Hashing\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
is_instance: "<type \'object\'>"
member {
name: "activity_regularizer"
mtype: "<type \'property\'>"
}
member {
name: "dtype"
mtype: "<type \'property\'>"
}
member {
name: "dynamic"
mtype: "<type \'property\'>"
}
member {
name: "inbound_nodes"
mtype: "<type \'property\'>"
}
member {
name: "input"
mtype: "<type \'property\'>"
}
member {
name: "input_mask"
mtype: "<type \'property\'>"
}
member {
name: "input_shape"
mtype: "<type \'property\'>"
}
member {
name: "input_spec"
mtype: "<type \'property\'>"
}
member {
name: "losses"
mtype: "<type \'property\'>"
}
member {
name: "metrics"
mtype: "<type \'property\'>"
}
member {
name: "name"
mtype: "<type \'property\'>"
}
member {
name: "name_scope"
mtype: "<type \'property\'>"
}
member {
name: "non_trainable_variables"
mtype: "<type \'property\'>"
}
member {
name: "non_trainable_weights"
mtype: "<type \'property\'>"
}
member {
name: "outbound_nodes"
mtype: "<type \'property\'>"
}
member {
name: "output"
mtype: "<type \'property\'>"
}
member {
name: "output_mask"
mtype: "<type \'property\'>"
}
member {
name: "output_shape"
mtype: "<type \'property\'>"
}
member {
name: "stateful"
mtype: "<type \'property\'>"
}
member {
name: "submodules"
mtype: "<type \'property\'>"
}
member {
name: "trainable"
mtype: "<type \'property\'>"
}
member {
name: "trainable_variables"
mtype: "<type \'property\'>"
}
member {
name: "trainable_weights"
mtype: "<type \'property\'>"
}
member {
name: "updates"
mtype: "<type \'property\'>"
}
member {
name: "variables"
mtype: "<type \'property\'>"
}
member {
name: "weights"
mtype: "<type \'property\'>"
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'num_bins\', \'salt\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_loss"
argspec: "args=[\'self\', \'losses\'], varargs=None, keywords=kwargs, defaults=None"
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_variable"
argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
}
member_method {
name: "add_weight"
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
}
member_method {
name: "apply"
argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
}
member_method {
name: "build"
argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "call"
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "compute_mask"
argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "compute_output_shape"
argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "compute_output_signature"
argspec: "args=[\'self\', \'input_spec\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "count_params"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "from_config"
argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_config"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_input_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_input_mask_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_input_shape_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_losses_for"
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_output_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_output_mask_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_output_shape_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_updates_for"
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_weights"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "set_weights"
argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "with_name_scope"
argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
}
}

4
tensorflow/tools/api/golden/v1/tensorflow.keras.layers.experimental.preprocessing.pbtxt
View File

@ -8,6 +8,10 @@ tf_module {
name: "CenterCrop"
mtype: "<type \'type\'>"
}
member {
name: "Hashing"
mtype: "<type \'type\'>"
}
member {
name: "Normalization"
mtype: "<type \'type\'>"

2
tensorflow/tools/api/golden/v1/tensorflow.sparse.pbtxt
View File

@ -22,7 +22,7 @@ tf_module {
}
member_method {
name: "cross"
argspec: "args=[\'inputs\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
argspec: "args=[\'inputs\', \'name\', \'separator\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "cross_hashed"

4
tensorflow/tools/api/golden/v2/tensorflow.keras.layers.experimental.preprocessing.-category-crossing.pbtxt
View File

@ -1,6 +1,6 @@
path: "tensorflow.keras.layers.experimental.preprocessing.CategoryCrossing"
tf_class {
is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.categorical_crossing.CategoryCrossing\'>"
is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.category_crossing.CategoryCrossing\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
@ -113,7 +113,7 @@ tf_class {
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'depth\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\'], "
argspec: "args=[\'self\', \'depth\', \'name\', \'separator\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\'], "
}
member_method {
name: "add_loss"

218
tensorflow/tools/api/golden/v2/tensorflow.keras.layers.experimental.preprocessing.-hashing.pbtxt Normal file
View File

@ -0,0 +1,218 @@
path: "tensorflow.keras.layers.experimental.preprocessing.Hashing"
tf_class {
is_instance: "<class \'tensorflow.python.keras.layers.preprocessing.hashing.Hashing\'>"
is_instance: "<class \'tensorflow.python.keras.engine.base_layer.Layer\'>"
is_instance: "<class \'tensorflow.python.module.module.Module\'>"
is_instance: "<class \'tensorflow.python.training.tracking.tracking.AutoTrackable\'>"
is_instance: "<class \'tensorflow.python.training.tracking.base.Trackable\'>"
is_instance: "<class \'tensorflow.python.keras.utils.version_utils.LayerVersionSelector\'>"
is_instance: "<type \'object\'>"
member {
name: "activity_regularizer"
mtype: "<type \'property\'>"
}
member {
name: "dtype"
mtype: "<type \'property\'>"
}
member {
name: "dynamic"
mtype: "<type \'property\'>"
}
member {
name: "inbound_nodes"
mtype: "<type \'property\'>"
}
member {
name: "input"
mtype: "<type \'property\'>"
}
member {
name: "input_mask"
mtype: "<type \'property\'>"
}
member {
name: "input_shape"
mtype: "<type \'property\'>"
}
member {
name: "input_spec"
mtype: "<type \'property\'>"
}
member {
name: "losses"
mtype: "<type \'property\'>"
}
member {
name: "metrics"
mtype: "<type \'property\'>"
}
member {
name: "name"
mtype: "<type \'property\'>"
}
member {
name: "name_scope"
mtype: "<type \'property\'>"
}
member {
name: "non_trainable_variables"
mtype: "<type \'property\'>"
}
member {
name: "non_trainable_weights"
mtype: "<type \'property\'>"
}
member {
name: "outbound_nodes"
mtype: "<type \'property\'>"
}
member {
name: "output"
mtype: "<type \'property\'>"
}
member {
name: "output_mask"
mtype: "<type \'property\'>"
}
member {
name: "output_shape"
mtype: "<type \'property\'>"
}
member {
name: "stateful"
mtype: "<type \'property\'>"
}
member {
name: "submodules"
mtype: "<type \'property\'>"
}
member {
name: "trainable"
mtype: "<type \'property\'>"
}
member {
name: "trainable_variables"
mtype: "<type \'property\'>"
}
member {
name: "trainable_weights"
mtype: "<type \'property\'>"
}
member {
name: "updates"
mtype: "<type \'property\'>"
}
member {
name: "variables"
mtype: "<type \'property\'>"
}
member {
name: "weights"
mtype: "<type \'property\'>"
}
member_method {
name: "__init__"
argspec: "args=[\'self\', \'num_bins\', \'salt\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\'], "
}
member_method {
name: "add_loss"
argspec: "args=[\'self\', \'losses\'], varargs=None, keywords=kwargs, defaults=None"
}
member_method {
name: "add_metric"
argspec: "args=[\'self\', \'value\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
}
member_method {
name: "add_update"
argspec: "args=[\'self\', \'updates\', \'inputs\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "add_variable"
argspec: "args=[\'self\'], varargs=args, keywords=kwargs, defaults=None"
}
member_method {
name: "add_weight"
argspec: "args=[\'self\', \'name\', \'shape\', \'dtype\', \'initializer\', \'regularizer\', \'trainable\', \'constraint\', \'partitioner\', \'use_resource\', \'synchronization\', \'aggregation\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'None\', \'VariableSynchronization.AUTO\', \'VariableAggregation.NONE\'], "
}
member_method {
name: "apply"
argspec: "args=[\'self\', \'inputs\'], varargs=args, keywords=kwargs, defaults=None"
}
member_method {
name: "build"
argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "call"
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "compute_mask"
argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
}
member_method {
name: "compute_output_shape"
argspec: "args=[\'self\', \'input_shape\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "compute_output_signature"
argspec: "args=[\'self\', \'input_spec\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "count_params"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "from_config"
argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_config"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_input_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_input_mask_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_input_shape_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_losses_for"
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_output_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_output_mask_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_output_shape_at"
argspec: "args=[\'self\', \'node_index\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_updates_for"
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "get_weights"
argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "set_weights"
argspec: "args=[\'self\', \'weights\'], varargs=None, keywords=None, defaults=None"
}
member_method {
name: "with_name_scope"
argspec: "args=[\'cls\', \'method\'], varargs=None, keywords=None, defaults=None"
}
}

4
tensorflow/tools/api/golden/v2/tensorflow.keras.layers.experimental.preprocessing.pbtxt
View File

@ -8,6 +8,10 @@ tf_module {
name: "CenterCrop"
mtype: "<type \'type\'>"
}
member {
name: "Hashing"
mtype: "<type \'type\'>"
}
member {
name: "Normalization"
mtype: "<type \'type\'>"

2
tensorflow/tools/api/golden/v2/tensorflow.sparse.pbtxt
View File

@ -18,7 +18,7 @@ tf_module {
}
member_method {
name: "cross"
argspec: "args=[\'inputs\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], "
argspec: "args=[\'inputs\', \'name\', \'separator\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], "
}
member_method {
name: "cross_hashed"