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)
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