TensorFlow生成随机数操作
2018-11-03 13:51 更新
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""生成随机数的操作""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import random_seed
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_random_ops
from tensorflow.python.ops import math_ops
# go/tf-wildcard-import
# pylint: disable=wildcard-import
from tensorflow.python.ops.gen_random_ops import *
# pylint: enable=wildcard-import
def _ShapeTensor(shape):
"""Convert to an int32 or int64 tensor, defaulting to int32 if empty."""
if isinstance(shape, (tuple, list)) and not shape:
dtype = dtypes.int32
else:
dtype = None
return ops.convert_to_tensor(shape, dtype=dtype, name="shape")
# pylint: disable=protected-access
def random_normal(shape,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a normal distribution.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
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.
seed: A Python integer. Used to create a random seed for the distribution.
See
@{tf.set_random_seed}
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random normal values.
"""
with ops.name_scope(name, "random_normal", [shape, mean, stddev]) as name:
shape_tensor = _ShapeTensor(shape)
mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
seed1, seed2 = random_seed.get_seed(seed)
rnd = gen_random_ops._random_standard_normal(
shape_tensor, dtype, seed=seed1, seed2=seed2)
mul = rnd * stddev_tensor
value = math_ops.add(mul, mean_tensor, name=name)
return value
ops.NotDifferentiable("RandomStandardNormal")
def parameterized_truncated_normal(shape,
means=0.0,
stddevs=1.0,
minvals=-2.0,
maxvals=2.0,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a truncated normal distribution.
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.
means: A 0-D Tensor or Python value of type `dtype`. The mean of the
truncated normal distribution.
stddevs: A 0-D Tensor or Python value of type `dtype`. The standard
deviation of the truncated normal distribution.
minvals: A 0-D Tensor or Python value of type `dtype`. The minimum value of
the truncated normal distribution.
maxvals: A 0-D Tensor or Python value of type `dtype`. The maximum value of
the truncated normal distribution.
dtype: The type of the output.
seed: A Python integer. Used to create a random seed for the distribution.
See
@{tf.set_random_seed}
for behavior.
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, "parameterized_truncated_normal",
[shape, means, stddevs, minvals, maxvals]) as name:
shape_tensor = _ShapeTensor(shape)
means_tensor = ops.convert_to_tensor(means, dtype=dtype, name="means")
stddevs_tensor = ops.convert_to_tensor(stddevs, dtype=dtype, name="stddevs")
minvals_tensor = ops.convert_to_tensor(minvals, dtype=dtype, name="minvals")
maxvals_tensor = ops.convert_to_tensor(maxvals, dtype=dtype, name="maxvals")
seed1, seed2 = random_seed.get_seed(seed)
rnd = gen_random_ops._parameterized_truncated_normal(
shape_tensor,
means_tensor,
stddevs_tensor,
minvals_tensor,
maxvals_tensor,
seed=seed1,
seed2=seed2)
return rnd
def truncated_normal(shape,
mean=0.0,
stddev=1.0,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a truncated normal distribution.
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.
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 truncated normal distribution.
dtype: The type of the output.
seed: A Python integer. Used to create a random seed for the distribution.
See
@{tf.set_random_seed}
for behavior.
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, "truncated_normal", [shape, mean, stddev]) as name:
shape_tensor = _ShapeTensor(shape)
mean_tensor = ops.convert_to_tensor(mean, dtype=dtype, name="mean")
stddev_tensor = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev")
seed1, seed2 = random_seed.get_seed(seed)
rnd = gen_random_ops._truncated_normal(
shape_tensor, dtype, seed=seed1, seed2=seed2)
mul = rnd * stddev_tensor
value = math_ops.add(mul, mean_tensor, name=name)
return value
ops.NotDifferentiable("ParameterizedTruncatedNormal")
ops.NotDifferentiable("TruncatedNormal")
def random_uniform(shape,
minval=0,
maxval=None,
dtype=dtypes.float32,
seed=None,
name=None):
"""Outputs random values from a uniform distribution.
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.
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`.
seed: A Python integer. Used to create a random seed for the distribution.
See @{tf.set_random_seed}
for behavior.
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.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, "random_uniform", [shape, minval, maxval]) as name:
shape = _ShapeTensor(shape)
minval = ops.convert_to_tensor(minval, dtype=dtype, name="min")
maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max")
seed1, seed2 = random_seed.get_seed(seed)
if dtype.is_integer:
return gen_random_ops._random_uniform_int(
shape, minval, maxval, seed=seed1, seed2=seed2, name=name)
else:
rnd = gen_random_ops._random_uniform(
shape, dtype, seed=seed1, seed2=seed2)
return math_ops.add(rnd * (maxval - minval), minval, name=name)
ops.NotDifferentiable("RandomUniform")
def random_shuffle(value, seed=None, name=None):
"""Randomly shuffles a tensor along its first dimension.
The tensor is shuffled along dimension 0, such that each `value[j]` is mapped
to one and only one `output[i]`. For example, a mapping that might occur for a
3x2 tensor is:
```python
[[1, 2], [[5, 6],
[3, 4], ==> [1, 2],
[5, 6]] [3, 4]]
```
Args:
value: A Tensor to be shuffled.
seed: A Python integer. Used to create a random seed for the distribution.
See
@{tf.set_random_seed}
for behavior.
name: A name for the operation (optional).
Returns:
A tensor of same shape and type as `value`, shuffled along its first
dimension.
"""
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops._random_shuffle(
value, seed=seed1, seed2=seed2, name=name)
def random_crop(value, size, seed=None, name=None):
"""Randomly crops a tensor to a given size.
Slices a shape `size` portion out of `value` at a uniformly chosen offset.
Requires `value.shape >= size`.
If a dimension should not be cropped, pass the full size of that dimension.
For example, RGB images can be cropped with
`size = [crop_height, crop_width, 3]`.
Args:
value: Input tensor to crop.
size: 1-D tensor with size the rank of `value`.
seed: Python integer. Used to create a random seed. See
@{tf.set_random_seed}
for behavior.
name: A name for this operation (optional).
Returns:
A cropped tensor of the same rank as `value` and shape `size`.
"""
# TODO(shlens): Implement edge case to guarantee output size dimensions.
# If size > value.shape, zero pad the result so that it always has shape
# exactly size.
with ops.name_scope(name, "random_crop", [value, size]) as name:
value = ops.convert_to_tensor(value, name="value")
size = ops.convert_to_tensor(size, dtype=dtypes.int32, name="size")
shape = array_ops.shape(value)
check = control_flow_ops.Assert(
math_ops.reduce_all(shape >= size),
["Need value.shape >= size, got ", shape, size],
summarize=1000)
shape = control_flow_ops.with_dependencies([check], shape)
limit = shape - size + 1
offset = random_uniform(
array_ops.shape(shape),
dtype=size.dtype,
maxval=size.dtype.max,
seed=seed) % limit
return array_ops.slice(value, offset, size, name=name)
def multinomial(logits, num_samples, seed=None, name=None):
"""Draws samples from a multinomial distribution.
Example:
```python
# samples has shape [1, 5], where each value is either 0 or 1 with equal
# probability.
samples = tf.multinomial(tf.log([[10., 10.]]), 5)
```
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 Python integer. Used to create a random seed for the distribution.
See
@{tf.set_random_seed}
for behavior.
name: Optional name for the operation.
Returns:
The drawn samples of shape `[batch_size, num_samples]`.
"""
with ops.name_scope(name, "multinomial", [logits]):
logits = ops.convert_to_tensor(logits, name="logits")
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops.multinomial(
logits, num_samples, seed=seed1, seed2=seed2)
ops.NotDifferentiable("Multinomial")
def random_gamma(shape,
alpha,
beta=None,
dtype=dtypes.float32,
seed=None,
name=None):
"""Draws `shape` samples from each of the given Gamma distribution(s).
`alpha` is the shape parameter describing the distribution(s), and `beta` is
the inverse scale parameter(s).
Example:
samples = tf.random_gamma([10], [0.5, 1.5])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution
samples = tf.random_gamma([7, 5], [0.5, 1.5])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions
samples = tf.random_gamma([30], [[1.],[3.],[5.]], beta=[[3., 4.]])
# samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions.
Note: Because internal calculations are done using `float64` and casting has
`floor` semantics, we must manually map zero outcomes to the smallest
possible positive floating-point value, i.e., `np.finfo(dtype).tiny`. This
means that `np.finfo(dtype).tiny` occurs more frequently than it otherwise
should. This bias can only happen for small values of `alpha`, i.e.,
`alpha << 1` or large values of `beta`, i.e., `beta >> 1`.
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output samples
to be drawn per alpha/beta-parameterized distribution.
alpha: A Tensor or Python value or N-D array of type `dtype`. `alpha`
provides the shape parameter(s) describing the gamma distribution(s) to
sample. Must be broadcastable with `beta`.
beta: A Tensor or Python value or N-D array of type `dtype`. Defaults to 1.
`beta` provides the inverse scale parameter(s) of the gamma
distribution(s) to sample. Must be broadcastable with `alpha`.
dtype: The type of alpha, beta, and the output: `float16`, `float32`, or
`float64`.
seed: A Python integer. Used to create a random seed for the distributions.
See
@{tf.set_random_seed}
for behavior.
name: Optional name for the operation.
Returns:
samples: a `Tensor` of shape `tf.concat(shape, tf.shape(alpha + beta))`
with values of type `dtype`.
"""
with ops.name_scope(name, "random_gamma", [shape, alpha, beta]):
shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32)
alpha = ops.convert_to_tensor(alpha, name="alpha", dtype=dtype)
beta = ops.convert_to_tensor(
beta if beta is not None else 1, name="beta", dtype=dtype)
alpha_broadcast = alpha + array_ops.zeros_like(beta)
seed1, seed2 = random_seed.get_seed(seed)
return math_ops.maximum(
np.finfo(dtype.as_numpy_dtype).tiny,
gen_random_ops._random_gamma(
shape, alpha_broadcast, seed=seed1, seed2=seed2) / beta)
ops.NotDifferentiable("RandomGamma")
def random_poisson(lam, shape, dtype=dtypes.float32, seed=None, name=None):
"""Draws `shape` samples from each of the given Poisson distribution(s).
`lam` is the rate parameter describing the distribution(s).
Example:
samples = tf.random_poisson([0.5, 1.5], [10])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution
samples = tf.random_poisson([12.2, 3.3], [7, 5])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions
Args:
lam: A Tensor or Python value or N-D array of type `dtype`.
`lam` provides the rate parameter(s) describing the poisson
distribution(s) to sample.
shape: A 1-D integer Tensor or Python array. The shape of the output samples
to be drawn per "rate"-parameterized distribution.
dtype: The type of `lam` and the output: `float16`, `float32`, or
`float64`.
seed: A Python integer. Used to create a random seed for the distributions.
See
@{tf.set_random_seed}
for behavior.
name: Optional name for the operation.
Returns:
samples: a `Tensor` of shape `tf.concat(shape, tf.shape(lam))` with
values of type `dtype`.
"""
with ops.name_scope(name, "random_poisson", [lam, shape]):
lam = ops.convert_to_tensor(lam, name="lam", dtype=dtype)
shape = ops.convert_to_tensor(shape, name="shape", dtype=dtypes.int32)
seed1, seed2 = random_seed.get_seed(seed)
return gen_random_ops._random_poisson(shape, lam, seed=seed1, seed2=seed2)