EVOLUTION-MANAGER

Edit File: stateless_random_ops.py

# 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 import numpy as np from tensorflow.python.compat import compat from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import gen_stateless_random_ops from tensorflow.python.ops import gen_stateless_random_ops_v2 from tensorflow.python.ops import math_ops from tensorflow.python.util import deprecation from tensorflow.python.util import dispatch from tensorflow.python.util.tf_export import tf_export ops.NotDifferentiable("StatelessMultinomial") ops.NotDifferentiable("StatelessRandomBinomial") ops.NotDifferentiable("StatelessRandomNormal") ops.NotDifferentiable("StatelessRandomPoisson") ops.NotDifferentiable("StatelessRandomUniform") ops.NotDifferentiable("StatelessRandomUniformInt") ops.NotDifferentiable("StatelessRandomUniformFullInt") ops.NotDifferentiable("StatelessTruncatedNormal") ops.NotDifferentiable("StatelessRandomNormalV2") ops.NotDifferentiable("StatelessRandomUniformV2") ops.NotDifferentiable("StatelessRandomUniformIntV2") ops.NotDifferentiable("StatelessRandomUniformFullIntV2") ops.NotDifferentiable("StatelessTruncatedNormalV2") @tf_export("random.experimental.stateless_split") @dispatch.add_dispatch_support def split(seed, num=2): """Splits an RNG seed into `num` new seeds by adding a leading axis. Example: >>> seed = [1, 2] >>> new_seeds = tf.random.experimental.stateless_split(seed, num=3) >>> print(new_seeds) tf.Tensor( [[1105988140 1738052849] [-335576002 370444179] [ 10670227 -246211131]], shape=(3, 2), dtype=int32) >>> tf.random.stateless_normal(shape=[3], seed=new_seeds[0, :]) <tf.Tensor: shape=(3,), dtype=float32, numpy=array([-0.59835213, -0.9578608 , 0.9002807 ], dtype=float32)> Args: seed: an RNG seed (a tensor with shape [2] and dtype `int32` or `int64`). (When using XLA, only `int32` is allowed.) num: optional, a positive integer or scalar tensor indicating the number of seeds to produce (default 2). Returns: A tensor with shape [num, 2] representing `num` new seeds. It will have the same dtype as `seed` (if `seed` doesn't have an explict dtype, the dtype will be determined by `tf.convert_to_tensor`). """ seed = ops.convert_to_tensor(seed) return stateless_random_uniform(shape=[num, 2], seed=seed, dtype=seed.dtype, minval=None, maxval=None) @tf_export("random.experimental.stateless_fold_in") @dispatch.add_dispatch_support def fold_in(seed, data): """Folds in data to an RNG seed to form a new RNG seed. For example, in a distributed-training setting, suppose we have a master seed and a replica ID. We want to fold the replica ID into the master seed to form a "replica seed" to be used by that replica later on, so that different replicas will generate different random numbers but the reproducibility of the whole system can still be controlled by the master seed: >>> master_seed = [1, 2] >>> replica_id = 3 >>> replica_seed = tf.random.experimental.stateless_fold_in( ... master_seed, replica_id) >>> print(replica_seed) tf.Tensor([1105988140 3], shape=(2,), dtype=int32) >>> tf.random.stateless_normal(shape=[3], seed=replica_seed) <tf.Tensor: shape=(3,), dtype=float32, numpy=array([0.03197195, 0.8979765 , 0.13253039], dtype=float32)> Args: seed: an RNG seed (a tensor with shape [2] and dtype `int32` or `int64`). (When using XLA, only `int32` is allowed.) data: an `int32` or `int64` scalar representing data to be folded in to the seed. Returns: A new RNG seed that is a deterministic function of the inputs and is statistically safe for producing a stream of new pseudo-random values. It will have the same dtype as `data` (if `data` doesn't have an explict dtype, the dtype will be determined by `tf.convert_to_tensor`). """ data = ops.convert_to_tensor(data) seed1 = stateless_random_uniform(shape=[], seed=seed, dtype=data.dtype, minval=None, maxval=None) return array_ops.stack([seed1, data]) _get_key_counter_alg = (gen_stateless_random_ops_v2 .stateless_random_get_key_counter_alg) @tf_export("random.stateless_uniform") @dispatch.add_dispatch_support 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 and shapes, 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`). For full-range (i.e. inclusive of both max and min) random integers, pass `minval=None` and `maxval=None` with an integer `dtype`. For an integer dtype either both `minval` and `maxval` must be `None` or neither may be `None`. For example: ```python ints = tf.random.stateless_uniform( [10], seed=(2, 3), minval=None, maxval=None, dtype=tf.int32) ``` Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. seed: A shape [2] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.) minval: A Tensor or Python value of type `dtype`, broadcastable with `shape` (for integer types, broadcasting is not supported, so it needs to be a scalar). The lower bound on the range of random values to generate. Pass `None` for full-range integers. Defaults to 0. maxval: A Tensor or Python value of type `dtype`, broadcastable with `shape` (for integer types, broadcasting is not supported, so it needs to be a scalar). The upper bound on the range of random values to generate. Defaults to 1 if `dtype` is floating point. Pass `None` for full-range integers. dtype: The type of the output: `float16`, `float32`, `float64`, `int32`, or `int64`. For unbounded uniform ints (`minval`, `maxval` both `None`), `uint32` and `uint64` may be used. 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 only one of `minval` or `maxval` is specified. """ dtype = dtypes.as_dtype(dtype) if dtype not in (dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64, dtypes.int32, dtypes.int64, dtypes.uint32, dtypes.uint64): raise ValueError("Invalid dtype %r" % dtype) if dtype.is_integer: if (minval is None) != (maxval is None): raise ValueError("For integer dtype {}, minval and maxval must be both " "`None` or both non-`None`.".format(dtype)) if minval is not None and dtype in (dtypes.uint32, dtypes.uint64): raise ValueError("Invalid dtype for bounded uniform integers: %r" % dtype) elif maxval is None: maxval = 1 with ops.name_scope(name, "stateless_random_uniform", [shape, seed, minval, maxval]) as name: shape = tensor_util.shape_tensor(shape) if dtype.is_integer and minval is None: if compat.forward_compatible(2020, 10, 25): key, counter, alg = _get_key_counter_alg(seed) result = (gen_stateless_random_ops_v2 .stateless_random_uniform_full_int_v2( shape, key=key, counter=counter, dtype=dtype, alg=alg, name=name)) else: result = gen_stateless_random_ops.stateless_random_uniform_full_int( shape, seed=seed, dtype=dtype, name=name) else: minval = ops.convert_to_tensor(minval, dtype=dtype, name="min") maxval = ops.convert_to_tensor(maxval, dtype=dtype, name="max") if dtype.is_integer: if compat.forward_compatible(2020, 10, 25): key, counter, alg = _get_key_counter_alg(seed) result = gen_stateless_random_ops_v2.stateless_random_uniform_int_v2( shape, key=key, counter=counter, minval=minval, maxval=maxval, alg=alg, name=name) else: result = gen_stateless_random_ops.stateless_random_uniform_int( shape, seed=seed, minval=minval, maxval=maxval, name=name) else: if compat.forward_compatible(2020, 10, 25): key, counter, alg = _get_key_counter_alg(seed) rnd = gen_stateless_random_ops_v2.stateless_random_uniform_v2( shape, key=key, counter=counter, dtype=dtype, alg=alg) else: rnd = gen_stateless_random_ops.stateless_random_uniform( shape, seed=seed, dtype=dtype) result = math_ops.add(rnd * (maxval - minval), minval, name=name) tensor_util.maybe_set_static_shape(result, shape) return result @tf_export("random.stateless_binomial") @dispatch.add_dispatch_support def stateless_random_binomial(shape, seed, counts, probs, output_dtype=dtypes.int32, name=None): """Outputs deterministic pseudorandom values from a binomial distribution. The generated values follow a binomial distribution with specified count and probability of success parameters. This is a stateless version of `tf.random.Generator.binomial`: if run twice with the same seeds and shapes, 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 counts = [10., 20.] # Probability of success. probs = [0.8] binomial_samples = tf.random.stateless_binomial( shape=[2], seed=[123, 456], counts=counts, probs=probs) counts = ... # Shape [3, 1, 2] probs = ... # Shape [1, 4, 2] shape = [3, 4, 3, 4, 2] # Sample shape will be [3, 4, 3, 4, 2] binomial_samples = tf.random.stateless_binomial( shape=shape, seed=[123, 456], counts=counts, probs=probs) ``` Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. seed: A shape [2] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.) counts: Tensor. The counts of the binomial distribution. Must be broadcastable with `probs`, and broadcastable with the rightmost dimensions of `shape`. probs: Tensor. The probability of success for the binomial distribution. Must be broadcastable with `counts` and broadcastable with the rightmost dimensions of `shape`. output_dtype: The type of the output. Default: tf.int32 name: A name for the operation (optional). Returns: samples: A Tensor of the specified shape filled with random binomial values. For each i, each samples[..., i] is an independent draw from the binomial distribution on counts[i] trials with probability of success probs[i]. """ with ops.name_scope(name, "stateless_random_binomial", [shape, seed, counts, probs]) as name: shape = tensor_util.shape_tensor(shape) probs = ops.convert_to_tensor( probs, dtype_hint=dtypes.float32, name="probs") counts = ops.convert_to_tensor( counts, dtype_hint=probs.dtype, name="counts") result = gen_stateless_random_ops.stateless_random_binomial( shape=shape, seed=seed, counts=counts, probs=probs, dtype=output_dtype) tensor_util.maybe_set_static_shape(result, shape) return result @tf_export("random.stateless_gamma") @dispatch.add_dispatch_support def stateless_random_gamma(shape, seed, alpha, beta=None, dtype=dtypes.float32, name=None): """Outputs deterministic pseudorandom values from a gamma distribution. The generated values follow a gamma distribution with specified concentration (`alpha`) and inverse scale (`beta`) parameters. This is a stateless version of `tf.random.gamma`: if run twice with the same seeds and shapes, 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. A slight difference exists in the interpretation of the `shape` parameter between `stateless_gamma` and `gamma`: in `gamma`, the `shape` is always prepended to the shape of the broadcast of `alpha` with `beta`; whereas in `stateless_gamma` the `shape` parameter must always encompass the shapes of each of `alpha` and `beta` (which must broadcast together to match the trailing dimensions of `shape`). 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`. The samples are differentiable w.r.t. alpha and beta. The derivatives are computed using the approach described in (Figurnov et al., 2018). Example: ```python samples = tf.random.stateless_gamma([10, 2], seed=[12, 34], alpha=[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.stateless_gamma([7, 5, 2], seed=[12, 34], alpha=[.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 alpha = tf.constant([[1.], [3.], [5.]]) beta = tf.constant([[3., 4.]]) samples = tf.random.stateless_gamma( [30, 3, 2], seed=[12, 34], alpha=alpha, beta=beta) # samples has shape [30, 3, 2], with 30 samples each of 3x2 distributions. with tf.GradientTape() as tape: tape.watch([alpha, beta]) loss = tf.reduce_mean(tf.square(tf.random.stateless_gamma( [30, 3, 2], seed=[12, 34], alpha=alpha, beta=beta))) dloss_dalpha, dloss_dbeta = tape.gradient(loss, [alpha, beta]) # unbiased stochastic derivatives of the loss function alpha.shape == dloss_dalpha.shape # True beta.shape == dloss_dbeta.shape # True ``` Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. seed: A shape [2] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.) alpha: Tensor. The concentration parameter of the gamma distribution. Must be broadcastable with `beta`, and broadcastable with the rightmost dimensions of `shape`. beta: Tensor. The inverse scale parameter of the gamma distribution. Must be broadcastable with `alpha` and broadcastable with the rightmost dimensions of `shape`. dtype: Floating point dtype of `alpha`, `beta`, and the output. name: A name for the operation (optional). Returns: samples: A Tensor of the specified shape filled with random gamma values. For each i, each `samples[..., i] is an independent draw from the gamma distribution with concentration alpha[i] and scale beta[i]. """ with ops.name_scope(name, "stateless_random_gamma", [shape, seed, alpha, beta]) as name: shape = tensor_util.shape_tensor(shape) alpha = ops.convert_to_tensor(alpha, dtype=dtype, name="alpha") beta = ops.convert_to_tensor( beta if beta is not None else 1, name="beta", dtype=dtype) broadcast_shape = array_ops.broadcast_dynamic_shape( array_ops.shape(alpha), array_ops.shape(beta)) alpha_broadcast = array_ops.broadcast_to(alpha, broadcast_shape) result = math_ops.maximum( np.finfo(alpha.dtype.as_numpy_dtype).tiny, gen_stateless_random_ops.stateless_random_gamma_v2( shape, seed=seed, alpha=alpha_broadcast) / beta) tensor_util.maybe_set_static_shape(result, shape) return result @tf_export("random.stateless_poisson") @dispatch.add_dispatch_support def stateless_random_poisson(shape, seed, lam, dtype=dtypes.int32, name=None): """Outputs deterministic pseudorandom values from a Poisson distribution. The generated values follow a Poisson distribution with specified rate parameter. This is a stateless version of `tf.random.poisson`: if run twice with the same seeds and shapes, it will produce the same pseudorandom numbers. The output is consistent across multiple runs on the same hardware, but may change between versions of TensorFlow or on non-CPU/GPU hardware. A slight difference exists in the interpretation of the `shape` parameter between `stateless_poisson` and `poisson`: in `poisson`, the `shape` is always prepended to the shape of `lam`; whereas in `stateless_poisson` the shape of `lam` must match the trailing dimensions of `shape`. Example: ```python samples = tf.random.stateless_poisson([10, 2], seed=[12, 34], lam=[5, 15]) # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents # the samples drawn from each distribution samples = tf.random.stateless_poisson([7, 5, 2], seed=[12, 34], lam=[5, 15]) # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1] # represents the 7x5 samples drawn from each of the two distributions rate = tf.constant([[1.], [3.], [5.]]) samples = tf.random.stateless_poisson([30, 3, 1], seed=[12, 34], lam=rate) # samples has shape [30, 3, 1], with 30 samples each of 3x1 distributions. ``` Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. seed: A shape [2] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.) lam: Tensor. The rate parameter "lambda" of the Poisson distribution. Shape must match the rightmost dimensions of `shape`. dtype: Dtype of the samples (int or float dtypes are permissible, as samples are discrete). Default: int32. name: A name for the operation (optional). Returns: samples: A Tensor of the specified shape filled with random Poisson values. For each i, each `samples[..., i]` is an independent draw from the Poisson distribution with rate `lam[i]`. """ with ops.name_scope(name, "stateless_random_poisson", [shape, seed, lam]) as name: shape = tensor_util.shape_tensor(shape) result = gen_stateless_random_ops.stateless_random_poisson( shape, seed=seed, lam=lam, dtype=dtype) tensor_util.maybe_set_static_shape(result, shape) return result @tf_export("random.stateless_normal") @dispatch.add_dispatch_support 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 and shapes, 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] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.) 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 = tensor_util.shape_tensor(shape) mean = ops.convert_to_tensor(mean, dtype=dtype, name="mean") stddev = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev") if compat.forward_compatible(2020, 10, 25): key, counter, alg = _get_key_counter_alg(seed) rnd = gen_stateless_random_ops_v2.stateless_random_normal_v2( shape, key=key, counter=counter, dtype=dtype, alg=alg) else: rnd = gen_stateless_random_ops.stateless_random_normal(shape, seed, dtype) result = math_ops.add(rnd * stddev, mean, name=name) tensor_util.maybe_set_static_shape(result, shape) return result @tf_export("random.stateless_truncated_normal") @dispatch.add_dispatch_support 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.random.truncated_normal`: if run twice with the same seeds and shapes, 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] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.) 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 = tensor_util.shape_tensor(shape) mean = ops.convert_to_tensor(mean, dtype=dtype, name="mean") stddev = ops.convert_to_tensor(stddev, dtype=dtype, name="stddev") if compat.forward_compatible(2020, 10, 25): key, counter, alg = _get_key_counter_alg(seed) rnd = gen_stateless_random_ops_v2.stateless_truncated_normal_v2( shape, key=key, counter=counter, dtype=dtype, alg=alg) else: rnd = gen_stateless_random_ops.stateless_truncated_normal( shape, seed, dtype) result = math_ops.add(rnd * stddev, mean, name=name) tensor_util.maybe_set_static_shape(result, shape) return result @tf_export(v1=["random.stateless_multinomial"]) @dispatch.add_dispatch_support @deprecation.deprecated( date=None, instructions="Use `tf.random.stateless_categorical` instead.") 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.random.categorical`: if run twice with the same seeds and shapes, 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.random.stateless_categorical( tf.math.log([[0.5, 0.5]]), 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] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.) output_dtype: integer type to use for the output. Defaults to int64. name: Optional name for the operation. Returns: The drawn samples of shape `[batch_size, num_samples]`. """ with ops.name_scope(name, "stateless_multinomial", [logits, seed]): return stateless_multinomial_categorical_impl(logits, num_samples, output_dtype, seed) @tf_export("random.stateless_categorical") @dispatch.add_dispatch_support def stateless_categorical(logits, num_samples, seed, dtype=dtypes.int64, name=None): """Draws deterministic pseudorandom samples from a categorical distribution. This is a stateless version of `tf.categorical`: if run twice with the same seeds and shapes, 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.random.stateless_categorical( tf.math.log([[0.5, 0.5]]), 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] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.) dtype: integer type to use for the output. Defaults to int64. name: Optional name for the operation. Returns: The drawn samples of shape `[batch_size, num_samples]`. """ with ops.name_scope(name, "stateless_categorical", [logits, seed]): return stateless_multinomial_categorical_impl(logits, num_samples, dtype, seed) def stateless_multinomial_categorical_impl(logits, num_samples, dtype, seed): """Implementation for stateless multinomial/categorical ops (v1/v2).""" logits = ops.convert_to_tensor(logits, name="logits") return gen_stateless_random_ops.stateless_multinomial( logits, num_samples, seed, output_dtype=dtype) @dispatch.add_dispatch_support @tf_export("random.stateless_parameterized_truncated_normal") def stateless_parameterized_truncated_normal(shape, seed, means=0.0, stddevs=1.0, minvals=-2.0, maxvals=2.0, 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. Examples: Sample from a Truncated normal, with deferring shape parameters that broadcast. >>> means = 0. >>> stddevs = tf.math.exp(tf.random.uniform(shape=[2, 3])) >>> minvals = [-1., -2., -1000.] >>> maxvals = [[10000.], [1.]] >>> y = tf.random.stateless_parameterized_truncated_normal( ... shape=[10, 2, 3], seed=[7, 17], ... means=means, stddevs=stddevs, minvals=minvals, maxvals=maxvals) >>> y.shape TensorShape([10, 2, 3]) Args: shape: A 1-D integer `Tensor` or Python array. The shape of the output tensor. seed: A shape [2] Tensor, the seed to the random number generator. Must have dtype `int32` or `int64`. (When using XLA, only `int32` is allowed.) means: A `Tensor` or Python value of type `dtype`. The mean of the truncated normal distribution. This must broadcast with `stddevs`, `minvals` and `maxvals`, and the broadcasted shape must be dominated by `shape`. stddevs: A `Tensor` or Python value of type `dtype`. The standard deviation of the truncated normal distribution. This must broadcast with `means`, `minvals` and `maxvals`, and the broadcasted shape must be dominated by `shape`. minvals: A `Tensor` or Python value of type `dtype`. The minimum value of the truncated normal distribution. This must broadcast with `means`, `stddevs` and `maxvals`, and the broadcasted shape must be dominated by `shape`. maxvals: A `Tensor` or Python value of type `dtype`. The maximum value of the truncated normal distribution. This must broadcast with `means`, `stddevs` and `minvals`, and the broadcasted shape must be dominated by `shape`. 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_parameterized_truncated_normal", [shape, means, stddevs, minvals, maxvals]) as name: shape_tensor = tensor_util.shape_tensor(shape) means_tensor = ops.convert_to_tensor(means, name="means") stddevs_tensor = ops.convert_to_tensor(stddevs, name="stddevs") minvals_tensor = ops.convert_to_tensor(minvals, name="minvals") maxvals_tensor = ops.convert_to_tensor(maxvals, name="maxvals") rnd = gen_stateless_random_ops.stateless_parameterized_truncated_normal( shape_tensor, seed, means_tensor, stddevs_tensor, minvals_tensor, maxvals_tensor) tensor_util.maybe_set_static_shape(rnd, shape) return rnd