1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
|
# Copyright 2018 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.
# ==============================================================================
"""Stateless random ops which take seed as a tensor input."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.ops import gen_stateless_random_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import math_ops
ops.NotDifferentiable("StatelessMultinomial")
ops.NotDifferentiable("StatelessRandomNormal")
ops.NotDifferentiable("StatelessRandomUniform")
ops.NotDifferentiable("StatelessRandomUniformInt")
ops.NotDifferentiable("StatelessTruncatedNormal")
def stateless_random_uniform(shape,
seed,
minval=0,
maxval=None,
dtype=dtypes.float32,
name=None):
"""Outputs deterministic pseudorandom values from a uniform distribution.
This is a stateless version of `tf.random_uniform`: if run twice with the
same seeds, it will produce the same pseudorandom numbers. The output is
consistent across multiple runs on the same hardware (and between CPU
and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
The generated values follow a uniform distribution in the range
`[minval, maxval)`. The lower bound `minval` is included in the range, while
the upper bound `maxval` is excluded.
For floats, the default range is `[0, 1)`. For ints, at least `maxval` must
be specified explicitly.
In the integer case, the random integers are slightly biased unless
`maxval - minval` is an exact power of two. The bias is small for values of
`maxval - minval` significantly smaller than the range of the output (either
`2**32` or `2**64`).
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] integer Tensor of seeds to the random number generator.
minval: A 0-D Tensor or Python value of type `dtype`. The lower bound on the
range of random values to generate. Defaults to 0.
maxval: A 0-D Tensor or Python value of type `dtype`. The upper bound on the
range of random values to generate. Defaults to 1 if `dtype` is floating
point.
dtype: The type of the output: `float16`, `float32`, `float64`, `int32`, or
`int64`.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random uniform values.
Raises:
ValueError: If `dtype` is integral and `maxval` is not specified.
"""
dtype = dtypes.as_dtype(dtype)
if dtype not in (dtypes.float16, dtypes.bfloat16, dtypes.float32,
dtypes.float64, dtypes.int32, dtypes.int64):
raise ValueError("Invalid dtype %r" % dtype)
if maxval is None:
if dtype.is_integer:
raise ValueError("Must specify maxval for integer dtype %r" % dtype)
maxval = 1
with ops.name_scope(name, "stateless_random_uniform",
[shape, seed, minval, maxval]) as name:
shape = random_ops._ShapeTensor(shape) # pylint: disable=protected-access
minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
if dtype.is_integer:
return gen_stateless_random_ops.stateless_random_uniform_int(
shape, seed=seed, minval=minval, maxval=maxval, name=name)
else:
rnd = gen_stateless_random_ops.stateless_random_uniform(
shape, seed=seed, dtype=dtype)
return math_ops.add(rnd * (maxval - minval), minval, name=name)
def stateless_random_normal(shape,
seed,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
name=None):
"""Outputs deterministic pseudorandom values from a normal distribution.
This is a stateless version of `tf.random_normal`: if run twice with the
same seeds, it will produce the same pseudorandom numbers. The output is
consistent across multiple runs on the same hardware (and between CPU
and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] integer Tensor of seeds to the random number generator.
mean: A 0-D Tensor or Python value of type `dtype`. The mean of the normal
distribution.
stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
of the normal distribution.
dtype: The type of the output.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random normal values.
"""
with ops.name_scope(name, "stateless_random_normal",
[shape, seed, mean, stddev]) as name:
shape = random_ops._ShapeTensor(shape) # pylint: disable=protected-access
mean = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
rnd = gen_stateless_random_ops.stateless_random_normal(shape, seed, dtype)
return math_ops.add(rnd * stddev, mean, name=name)
def stateless_truncated_normal(shape,
seed,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
name=None):
"""Outputs deterministic pseudorandom values, truncated normally distributed.
This is a stateless version of `tf.truncated_normal`: if run twice with the
same seeds, it will produce the same pseudorandom numbers. The output is
consistent across multiple runs on the same hardware (and between CPU
and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
The generated values follow a normal distribution with specified mean and
standard deviation, except that values whose magnitude is more than 2 standard
deviations from the mean are dropped and re-picked.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
seed: A shape [2] integer Tensor of seeds to the random number generator.
mean: A 0-D Tensor or Python value of type `dtype`. The mean of the
truncated normal distribution.
stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
of the normal distribution, before truncation.
dtype: The type of the output.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random truncated normal values.
"""
with ops.name_scope(name, "stateless_truncated_normal",
[shape, seed, mean, stddev]) as name:
shape = random_ops._ShapeTensor(shape) # pylint: disable=protected-access
mean = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
rnd = gen_stateless_random_ops.stateless_truncated_normal(
shape, seed, dtype)
return math_ops.add(rnd * stddev, mean, name=name)
def stateless_multinomial(logits,
num_samples,
seed,
output_dtype=dtypes.int64,
name=None):
"""Draws deterministic pseudorandom samples from a multinomial distribution.
This is a stateless version of `tf.multinomial`: if run twice with the
same seeds, it will produce the same pseudorandom numbers. The output is
consistent across multiple runs on the same hardware (and between CPU
and GPU), but may change between versions of TensorFlow or on non-CPU/GPU
hardware.
Example:
```python
# samples has shape [1, 5], where each value is either 0 or 1 with equal
# probability.
samples = tf.contrib.stateless.stateless_multinomial(
tf.log([[10., 10.]]), 5, seed=[7, 17])
```
Args:
logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice
`[i, :]` represents the unnormalized log-probabilities for all classes.
num_samples: 0-D. Number of independent samples to draw for each row slice.
seed: A shape [2] integer Tensor of seeds to the random number generator.
name: Optional name for the operation.
output_dtype: integer type to use for the output. Defaults to int64.
Returns:
The drawn samples of shape `[batch_size, num_samples]`.
"""
with ops.name_scope(name, "stateless_multinomial", [logits, seed]):
logits = ops.convert_to_tensor(logits, name="logits")
return gen_stateless_random_ops.stateless_multinomial(
logits, num_samples, seed, output_dtype=output_dtype)
|
