982 lines
43 KiB
Python
982 lines
43 KiB
Python
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"""Python wrappers around TensorFlow ops.
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This file is MACHINE GENERATED! Do not edit.
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"""
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import collections
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from tensorflow.python import pywrap_tfe as pywrap_tfe
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from tensorflow.python.eager import context as _context
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from tensorflow.python.eager import core as _core
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from tensorflow.python.eager import execute as _execute
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from tensorflow.python.framework import dtypes as _dtypes
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from tensorflow.security.fuzzing.py import annotation_types as _atypes
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from tensorflow.python.framework import op_def_registry as _op_def_registry
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from tensorflow.python.framework import ops as _ops
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from tensorflow.python.framework import op_def_library as _op_def_library
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from tensorflow.python.util.deprecation import deprecated_endpoints
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from tensorflow.python.util import dispatch as _dispatch
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from tensorflow.python.util.tf_export import tf_export
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from typing import TypeVar, List, Any
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from typing_extensions import Annotated
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TV_Multinomial_T = TypeVar("TV_Multinomial_T", _atypes.BFloat16, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
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TV_Multinomial_output_dtype = TypeVar("TV_Multinomial_output_dtype", _atypes.Int32, _atypes.Int64)
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def multinomial(logits: Annotated[Any, TV_Multinomial_T], num_samples: Annotated[Any, _atypes.Int32], seed:int=0, seed2:int=0, output_dtype:TV_Multinomial_output_dtype=_dtypes.int64, name=None) -> Annotated[Any, TV_Multinomial_output_dtype]:
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r"""Draws samples from a multinomial distribution.
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Args:
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logits: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
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2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]`
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represents the unnormalized log probabilities for all classes.
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num_samples: A `Tensor` of type `int32`.
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0-D. Number of independent samples to draw for each row slice.
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seed: An optional `int`. Defaults to `0`.
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If either seed or seed2 is set to be non-zero, the internal random number
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generator is seeded by the given seed. Otherwise, a random seed is used.
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seed2: An optional `int`. Defaults to `0`.
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A second seed to avoid seed collision.
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output_dtype: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int64`.
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name: A name for the operation (optional).
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Returns:
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A `Tensor` of type `output_dtype`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "Multinomial", name, logits, num_samples, "seed", seed, "seed2",
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seed2, "output_dtype", output_dtype)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return multinomial_eager_fallback(
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logits, num_samples, seed=seed, seed2=seed2,
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output_dtype=output_dtype, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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if seed is None:
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seed = 0
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seed = _execute.make_int(seed, "seed")
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if seed2 is None:
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seed2 = 0
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seed2 = _execute.make_int(seed2, "seed2")
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if output_dtype is None:
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output_dtype = _dtypes.int64
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output_dtype = _execute.make_type(output_dtype, "output_dtype")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"Multinomial", logits=logits, num_samples=num_samples, seed=seed,
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seed2=seed2, output_dtype=output_dtype, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("seed", _op._get_attr_int("seed"), "seed2",
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_op._get_attr_int("seed2"), "T", _op._get_attr_type("T"),
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"output_dtype", _op._get_attr_type("output_dtype"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"Multinomial", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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Multinomial = tf_export("raw_ops.Multinomial")(_ops.to_raw_op(multinomial))
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def multinomial_eager_fallback(logits: Annotated[Any, TV_Multinomial_T], num_samples: Annotated[Any, _atypes.Int32], seed: int, seed2: int, output_dtype: TV_Multinomial_output_dtype, name, ctx) -> Annotated[Any, TV_Multinomial_output_dtype]:
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if seed is None:
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seed = 0
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seed = _execute.make_int(seed, "seed")
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if seed2 is None:
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seed2 = 0
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seed2 = _execute.make_int(seed2, "seed2")
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if output_dtype is None:
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output_dtype = _dtypes.int64
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output_dtype = _execute.make_type(output_dtype, "output_dtype")
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_attr_T, (logits,) = _execute.args_to_matching_eager([logits], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.int64, _dtypes.bfloat16, _dtypes.uint16, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
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num_samples = _ops.convert_to_tensor(num_samples, _dtypes.int32)
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_inputs_flat = [logits, num_samples]
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_attrs = ("seed", seed, "seed2", seed2, "T", _attr_T, "output_dtype",
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output_dtype)
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_result = _execute.execute(b"Multinomial", 1, inputs=_inputs_flat,
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attrs=_attrs, ctx=ctx, name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"Multinomial", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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TV_ParameterizedTruncatedNormal_dtype = TypeVar("TV_ParameterizedTruncatedNormal_dtype", _atypes.BFloat16, _atypes.Float32, _atypes.Float64, _atypes.Half)
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TV_ParameterizedTruncatedNormal_T = TypeVar("TV_ParameterizedTruncatedNormal_T", _atypes.Int32, _atypes.Int64)
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def parameterized_truncated_normal(shape: Annotated[Any, TV_ParameterizedTruncatedNormal_T], means: Annotated[Any, TV_ParameterizedTruncatedNormal_dtype], stdevs: Annotated[Any, TV_ParameterizedTruncatedNormal_dtype], minvals: Annotated[Any, TV_ParameterizedTruncatedNormal_dtype], maxvals: Annotated[Any, TV_ParameterizedTruncatedNormal_dtype], seed:int=0, seed2:int=0, name=None) -> Annotated[Any, TV_ParameterizedTruncatedNormal_dtype]:
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r"""Outputs random values from a normal distribution. The parameters may each be a
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scalar which applies to the entire output, or a vector of length shape[0] which
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stores the parameters for each batch.
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Args:
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shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
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The shape of the output tensor. Batches are indexed by the 0th dimension.
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means: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`.
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The mean parameter of each batch.
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stdevs: A `Tensor`. Must have the same type as `means`.
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The standard deviation parameter of each batch. Must be greater than 0.
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minvals: A `Tensor`. Must have the same type as `means`.
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The minimum cutoff. May be -infinity.
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maxvals: A `Tensor`. Must have the same type as `means`.
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The maximum cutoff. May be +infinity, and must be more than the minval
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for each batch.
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seed: An optional `int`. Defaults to `0`.
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If either `seed` or `seed2` are set to be non-zero, the random number
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generator is seeded by the given seed. Otherwise, it is seeded by a
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random seed.
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seed2: An optional `int`. Defaults to `0`.
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A second seed to avoid seed collision.
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `means`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "ParameterizedTruncatedNormal", name, shape, means, stdevs,
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minvals, maxvals, "seed", seed, "seed2", seed2)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return parameterized_truncated_normal_eager_fallback(
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shape, means, stdevs, minvals, maxvals, seed=seed, seed2=seed2,
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name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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# Add nodes to the TensorFlow graph.
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if seed is None:
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seed = 0
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seed = _execute.make_int(seed, "seed")
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if seed2 is None:
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seed2 = 0
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seed2 = _execute.make_int(seed2, "seed2")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"ParameterizedTruncatedNormal", shape=shape, means=means,
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stdevs=stdevs, minvals=minvals,
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maxvals=maxvals, seed=seed,
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seed2=seed2, name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("seed", _op._get_attr_int("seed"), "seed2",
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_op._get_attr_int("seed2"), "dtype",
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_op._get_attr_type("dtype"), "T", _op._get_attr_type("T"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"ParameterizedTruncatedNormal", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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ParameterizedTruncatedNormal = tf_export("raw_ops.ParameterizedTruncatedNormal")(_ops.to_raw_op(parameterized_truncated_normal))
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def parameterized_truncated_normal_eager_fallback(shape: Annotated[Any, TV_ParameterizedTruncatedNormal_T], means: Annotated[Any, TV_ParameterizedTruncatedNormal_dtype], stdevs: Annotated[Any, TV_ParameterizedTruncatedNormal_dtype], minvals: Annotated[Any, TV_ParameterizedTruncatedNormal_dtype], maxvals: Annotated[Any, TV_ParameterizedTruncatedNormal_dtype], seed: int, seed2: int, name, ctx) -> Annotated[Any, TV_ParameterizedTruncatedNormal_dtype]:
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if seed is None:
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seed = 0
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seed = _execute.make_int(seed, "seed")
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if seed2 is None:
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seed2 = 0
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seed2 = _execute.make_int(seed2, "seed2")
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_attr_dtype, _inputs_dtype = _execute.args_to_matching_eager([means, stdevs, minvals, maxvals], ctx, [_dtypes.half, _dtypes.bfloat16, _dtypes.float32, _dtypes.float64, ])
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(means, stdevs, minvals, maxvals) = _inputs_dtype
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_attr_T, (shape,) = _execute.args_to_matching_eager([shape], ctx, [_dtypes.int32, _dtypes.int64, ])
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_inputs_flat = [shape, means, stdevs, minvals, maxvals]
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_attrs = ("seed", seed, "seed2", seed2, "dtype", _attr_dtype, "T", _attr_T)
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_result = _execute.execute(b"ParameterizedTruncatedNormal", 1,
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inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
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name=name)
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if _execute.must_record_gradient():
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_execute.record_gradient(
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"ParameterizedTruncatedNormal", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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TV_RandomGamma_S = TypeVar("TV_RandomGamma_S", _atypes.Int32, _atypes.Int64)
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TV_RandomGamma_T = TypeVar("TV_RandomGamma_T", _atypes.Float32, _atypes.Float64, _atypes.Half)
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def random_gamma(shape: Annotated[Any, TV_RandomGamma_S], alpha: Annotated[Any, TV_RandomGamma_T], seed:int=0, seed2:int=0, name=None) -> Annotated[Any, TV_RandomGamma_T]:
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r"""Outputs random values from the Gamma distribution(s) described by alpha.
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This op uses the algorithm by Marsaglia et al. to acquire samples via
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transformation-rejection from pairs of uniform and normal random variables.
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See http://dl.acm.org/citation.cfm?id=358414
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Args:
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shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
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1-D integer tensor. Shape of independent samples to draw from each
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distribution described by the shape parameters given in alpha.
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alpha: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
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A tensor in which each scalar is a "shape" parameter describing the
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associated gamma distribution.
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seed: An optional `int`. Defaults to `0`.
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If either `seed` or `seed2` are set to be non-zero, the random number
|
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|
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generator is seeded by the given seed. Otherwise, it is seeded by a
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random seed.
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seed2: An optional `int`. Defaults to `0`.
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A second seed to avoid seed collision.
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name: A name for the operation (optional).
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Returns:
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A `Tensor`. Has the same type as `alpha`.
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"""
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_ctx = _context._context or _context.context()
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tld = _ctx._thread_local_data
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if tld.is_eager:
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try:
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_result = pywrap_tfe.TFE_Py_FastPathExecute(
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_ctx, "RandomGamma", name, shape, alpha, "seed", seed, "seed2", seed2)
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return _result
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except _core._NotOkStatusException as e:
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_ops.raise_from_not_ok_status(e, name)
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except _core._FallbackException:
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pass
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try:
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return random_gamma_eager_fallback(
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shape, alpha, seed=seed, seed2=seed2, name=name, ctx=_ctx)
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except _core._SymbolicException:
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pass # Add nodes to the TensorFlow graph.
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||
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# Add nodes to the TensorFlow graph.
|
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if seed is None:
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seed = 0
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seed = _execute.make_int(seed, "seed")
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if seed2 is None:
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seed2 = 0
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seed2 = _execute.make_int(seed2, "seed2")
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_, _, _op, _outputs = _op_def_library._apply_op_helper(
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"RandomGamma", shape=shape, alpha=alpha, seed=seed, seed2=seed2,
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name=name)
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_result = _outputs[:]
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if _execute.must_record_gradient():
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_attrs = ("seed", _op._get_attr_int("seed"), "seed2",
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_op._get_attr_int("seed2"), "S", _op._get_attr_type("S"), "T",
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_op._get_attr_type("T"))
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_inputs_flat = _op.inputs
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_execute.record_gradient(
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"RandomGamma", _inputs_flat, _attrs, _result)
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_result, = _result
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return _result
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RandomGamma = tf_export("raw_ops.RandomGamma")(_ops.to_raw_op(random_gamma))
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def random_gamma_eager_fallback(shape: Annotated[Any, TV_RandomGamma_S], alpha: Annotated[Any, TV_RandomGamma_T], seed: int, seed2: int, name, ctx) -> Annotated[Any, TV_RandomGamma_T]:
|
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if seed is None:
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seed = 0
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seed = _execute.make_int(seed, "seed")
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if seed2 is None:
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seed2 = 0
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seed2 = _execute.make_int(seed2, "seed2")
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|
|
_attr_S, (shape,) = _execute.args_to_matching_eager([shape], ctx, [_dtypes.int32, _dtypes.int64, ])
|
||
|
|
_attr_T, (alpha,) = _execute.args_to_matching_eager([alpha], ctx, [_dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [shape, alpha]
|
||
|
|
_attrs = ("seed", seed, "seed2", seed2, "S", _attr_S, "T", _attr_T)
|
||
|
|
_result = _execute.execute(b"RandomGamma", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomGamma", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_RandomGammaGrad_T = TypeVar("TV_RandomGammaGrad_T", _atypes.Float32, _atypes.Float64)
|
||
|
|
|
||
|
|
def random_gamma_grad(alpha: Annotated[Any, TV_RandomGammaGrad_T], sample: Annotated[Any, TV_RandomGammaGrad_T], name=None) -> Annotated[Any, TV_RandomGammaGrad_T]:
|
||
|
|
r"""Computes the derivative of a Gamma random sample w.r.t. `alpha`.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
alpha: A `Tensor`. Must be one of the following types: `float32`, `float64`.
|
||
|
|
sample: A `Tensor`. Must have the same type as `alpha`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `alpha`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "RandomGammaGrad", name, alpha, sample)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return random_gamma_grad_eager_fallback(
|
||
|
|
alpha, sample, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"RandomGammaGrad", alpha=alpha, sample=sample, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomGammaGrad", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
RandomGammaGrad = tf_export("raw_ops.RandomGammaGrad")(_ops.to_raw_op(random_gamma_grad))
|
||
|
|
|
||
|
|
|
||
|
|
def random_gamma_grad_eager_fallback(alpha: Annotated[Any, TV_RandomGammaGrad_T], sample: Annotated[Any, TV_RandomGammaGrad_T], name, ctx) -> Annotated[Any, TV_RandomGammaGrad_T]:
|
||
|
|
_attr_T, _inputs_T = _execute.args_to_matching_eager([alpha, sample], ctx, [_dtypes.float32, _dtypes.float64, ])
|
||
|
|
(alpha, sample) = _inputs_T
|
||
|
|
_inputs_flat = [alpha, sample]
|
||
|
|
_attrs = ("T", _attr_T)
|
||
|
|
_result = _execute.execute(b"RandomGammaGrad", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomGammaGrad", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_RandomPoisson_S = TypeVar("TV_RandomPoisson_S", _atypes.Int32, _atypes.Int64)
|
||
|
|
TV_RandomPoisson_dtype = TypeVar("TV_RandomPoisson_dtype", _atypes.Float32, _atypes.Float64, _atypes.Half)
|
||
|
|
|
||
|
|
def random_poisson(shape: Annotated[Any, TV_RandomPoisson_S], rate: Annotated[Any, TV_RandomPoisson_dtype], seed:int=0, seed2:int=0, name=None) -> Annotated[Any, TV_RandomPoisson_dtype]:
|
||
|
|
r"""Use RandomPoissonV2 instead.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
|
||
|
|
rate: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
|
||
|
|
seed: An optional `int`. Defaults to `0`.
|
||
|
|
seed2: An optional `int`. Defaults to `0`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `rate`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "RandomPoisson", name, shape, rate, "seed", seed, "seed2",
|
||
|
|
seed2)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return random_poisson_eager_fallback(
|
||
|
|
shape, rate, seed=seed, seed2=seed2, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"RandomPoisson", shape=shape, rate=rate, seed=seed, seed2=seed2,
|
||
|
|
name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("seed", _op._get_attr_int("seed"), "seed2",
|
||
|
|
_op._get_attr_int("seed2"), "S", _op._get_attr_type("S"),
|
||
|
|
"dtype", _op._get_attr_type("dtype"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomPoisson", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
RandomPoisson = tf_export("raw_ops.RandomPoisson")(_ops.to_raw_op(random_poisson))
|
||
|
|
|
||
|
|
|
||
|
|
def random_poisson_eager_fallback(shape: Annotated[Any, TV_RandomPoisson_S], rate: Annotated[Any, TV_RandomPoisson_dtype], seed: int, seed2: int, name, ctx) -> Annotated[Any, TV_RandomPoisson_dtype]:
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
_attr_S, (shape,) = _execute.args_to_matching_eager([shape], ctx, [_dtypes.int32, _dtypes.int64, ])
|
||
|
|
_attr_dtype, (rate,) = _execute.args_to_matching_eager([rate], ctx, [_dtypes.half, _dtypes.float32, _dtypes.float64, ])
|
||
|
|
_inputs_flat = [shape, rate]
|
||
|
|
_attrs = ("seed", seed, "seed2", seed2, "S", _attr_S, "dtype", _attr_dtype)
|
||
|
|
_result = _execute.execute(b"RandomPoisson", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomPoisson", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_RandomPoissonV2_S = TypeVar("TV_RandomPoissonV2_S", _atypes.Int32, _atypes.Int64)
|
||
|
|
TV_RandomPoissonV2_R = TypeVar("TV_RandomPoissonV2_R", _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int32, _atypes.Int64)
|
||
|
|
TV_RandomPoissonV2_dtype = TypeVar("TV_RandomPoissonV2_dtype", _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int32, _atypes.Int64)
|
||
|
|
|
||
|
|
def random_poisson_v2(shape: Annotated[Any, TV_RandomPoissonV2_S], rate: Annotated[Any, TV_RandomPoissonV2_R], seed:int=0, seed2:int=0, dtype:TV_RandomPoissonV2_dtype=_dtypes.int64, name=None) -> Annotated[Any, TV_RandomPoissonV2_dtype]:
|
||
|
|
r"""Outputs random values from the Poisson distribution(s) described by rate.
|
||
|
|
|
||
|
|
This op uses two algorithms, depending on rate. If rate >= 10, then
|
||
|
|
the algorithm by Hormann is used to acquire samples via
|
||
|
|
transformation-rejection.
|
||
|
|
See http://www.sciencedirect.com/science/article/pii/0167668793909974.
|
||
|
|
|
||
|
|
Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform
|
||
|
|
random variables.
|
||
|
|
See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer
|
||
|
|
Programming, Volume 2. Addison Wesley
|
||
|
|
|
||
|
|
Args:
|
||
|
|
shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
|
||
|
|
1-D integer tensor. Shape of independent samples to draw from each
|
||
|
|
distribution described by the shape parameters given in rate.
|
||
|
|
rate: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`.
|
||
|
|
A tensor in which each scalar is a "rate" parameter describing the
|
||
|
|
associated poisson distribution.
|
||
|
|
seed: An optional `int`. Defaults to `0`.
|
||
|
|
If either `seed` or `seed2` are set to be non-zero, the random number
|
||
|
|
generator is seeded by the given seed. Otherwise, it is seeded by a
|
||
|
|
random seed.
|
||
|
|
seed2: An optional `int`. Defaults to `0`.
|
||
|
|
A second seed to avoid seed collision.
|
||
|
|
dtype: An optional `tf.DType` from: `tf.half, tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.int64`.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `dtype`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "RandomPoissonV2", name, shape, rate, "seed", seed, "seed2",
|
||
|
|
seed2, "dtype", dtype)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return random_poisson_v2_eager_fallback(
|
||
|
|
shape, rate, seed=seed, seed2=seed2, dtype=dtype, name=name,
|
||
|
|
ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
if dtype is None:
|
||
|
|
dtype = _dtypes.int64
|
||
|
|
dtype = _execute.make_type(dtype, "dtype")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"RandomPoissonV2", shape=shape, rate=rate, seed=seed, seed2=seed2,
|
||
|
|
dtype=dtype, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("seed", _op._get_attr_int("seed"), "seed2",
|
||
|
|
_op._get_attr_int("seed2"), "S", _op._get_attr_type("S"), "R",
|
||
|
|
_op._get_attr_type("R"), "dtype", _op._get_attr_type("dtype"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomPoissonV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
RandomPoissonV2 = tf_export("raw_ops.RandomPoissonV2")(_ops.to_raw_op(random_poisson_v2))
|
||
|
|
|
||
|
|
|
||
|
|
def random_poisson_v2_eager_fallback(shape: Annotated[Any, TV_RandomPoissonV2_S], rate: Annotated[Any, TV_RandomPoissonV2_R], seed: int, seed2: int, dtype: TV_RandomPoissonV2_dtype, name, ctx) -> Annotated[Any, TV_RandomPoissonV2_dtype]:
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
if dtype is None:
|
||
|
|
dtype = _dtypes.int64
|
||
|
|
dtype = _execute.make_type(dtype, "dtype")
|
||
|
|
_attr_S, (shape,) = _execute.args_to_matching_eager([shape], ctx, [_dtypes.int32, _dtypes.int64, ])
|
||
|
|
_attr_R, (rate,) = _execute.args_to_matching_eager([rate], ctx, [_dtypes.half, _dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.int64, ], _dtypes.float64)
|
||
|
|
_inputs_flat = [shape, rate]
|
||
|
|
_attrs = ("seed", seed, "seed2", seed2, "S", _attr_S, "R", _attr_R, "dtype",
|
||
|
|
dtype)
|
||
|
|
_result = _execute.execute(b"RandomPoissonV2", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomPoissonV2", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_RandomShuffle_T = TypeVar("TV_RandomShuffle_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
|
||
|
|
|
||
|
|
def random_shuffle(value: Annotated[Any, TV_RandomShuffle_T], seed:int=0, seed2:int=0, name=None) -> Annotated[Any, TV_RandomShuffle_T]:
|
||
|
|
r"""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:
|
||
|
|
|
||
|
|
```
|
||
|
|
[[1, 2], [[5, 6],
|
||
|
|
[3, 4], ==> [1, 2],
|
||
|
|
[5, 6]] [3, 4]]
|
||
|
|
```
|
||
|
|
|
||
|
|
Args:
|
||
|
|
value: A `Tensor`. The tensor to be shuffled.
|
||
|
|
seed: An optional `int`. Defaults to `0`.
|
||
|
|
If either `seed` or `seed2` are set to be non-zero, the random number
|
||
|
|
generator is seeded by the given seed. Otherwise, it is seeded by a
|
||
|
|
random seed.
|
||
|
|
seed2: An optional `int`. Defaults to `0`.
|
||
|
|
A second seed to avoid seed collision.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `value`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "RandomShuffle", name, value, "seed", seed, "seed2", seed2)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return random_shuffle_eager_fallback(
|
||
|
|
value, seed=seed, seed2=seed2, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"RandomShuffle", value=value, seed=seed, seed2=seed2, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("seed", _op._get_attr_int("seed"), "seed2",
|
||
|
|
_op._get_attr_int("seed2"), "T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomShuffle", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
RandomShuffle = tf_export("raw_ops.RandomShuffle")(_ops.to_raw_op(random_shuffle))
|
||
|
|
|
||
|
|
|
||
|
|
def random_shuffle_eager_fallback(value: Annotated[Any, TV_RandomShuffle_T], seed: int, seed2: int, name, ctx) -> Annotated[Any, TV_RandomShuffle_T]:
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
_attr_T, (value,) = _execute.args_to_matching_eager([value], ctx, [])
|
||
|
|
_inputs_flat = [value]
|
||
|
|
_attrs = ("seed", seed, "seed2", seed2, "T", _attr_T)
|
||
|
|
_result = _execute.execute(b"RandomShuffle", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomShuffle", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_RandomStandardNormal_dtype = TypeVar("TV_RandomStandardNormal_dtype", _atypes.BFloat16, _atypes.Float32, _atypes.Float64, _atypes.Half)
|
||
|
|
TV_RandomStandardNormal_T = TypeVar("TV_RandomStandardNormal_T", _atypes.Int32, _atypes.Int64)
|
||
|
|
|
||
|
|
def random_standard_normal(shape: Annotated[Any, TV_RandomStandardNormal_T], dtype: TV_RandomStandardNormal_dtype, seed:int=0, seed2:int=0, name=None) -> Annotated[Any, TV_RandomStandardNormal_dtype]:
|
||
|
|
r"""Outputs random values from a normal distribution.
|
||
|
|
|
||
|
|
The generated values will have mean 0 and standard deviation 1.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
|
||
|
|
The shape of the output tensor.
|
||
|
|
dtype: A `tf.DType` from: `tf.half, tf.bfloat16, tf.float32, tf.float64`.
|
||
|
|
The type of the output.
|
||
|
|
seed: An optional `int`. Defaults to `0`.
|
||
|
|
If either `seed` or `seed2` are set to be non-zero, the random number
|
||
|
|
generator is seeded by the given seed. Otherwise, it is seeded by a
|
||
|
|
random seed.
|
||
|
|
seed2: An optional `int`. Defaults to `0`.
|
||
|
|
A second seed to avoid seed collision.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `dtype`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "RandomStandardNormal", name, shape, "seed", seed, "seed2",
|
||
|
|
seed2, "dtype", dtype)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return random_standard_normal_eager_fallback(
|
||
|
|
shape, seed=seed, seed2=seed2, dtype=dtype, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
dtype = _execute.make_type(dtype, "dtype")
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"RandomStandardNormal", shape=shape, dtype=dtype, seed=seed,
|
||
|
|
seed2=seed2, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("seed", _op._get_attr_int("seed"), "seed2",
|
||
|
|
_op._get_attr_int("seed2"), "dtype",
|
||
|
|
_op._get_attr_type("dtype"), "T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomStandardNormal", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
RandomStandardNormal = tf_export("raw_ops.RandomStandardNormal")(_ops.to_raw_op(random_standard_normal))
|
||
|
|
|
||
|
|
|
||
|
|
def random_standard_normal_eager_fallback(shape: Annotated[Any, TV_RandomStandardNormal_T], dtype: TV_RandomStandardNormal_dtype, seed: int, seed2: int, name, ctx) -> Annotated[Any, TV_RandomStandardNormal_dtype]:
|
||
|
|
dtype = _execute.make_type(dtype, "dtype")
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
_attr_T, (shape,) = _execute.args_to_matching_eager([shape], ctx, [_dtypes.int32, _dtypes.int64, ])
|
||
|
|
_inputs_flat = [shape]
|
||
|
|
_attrs = ("seed", seed, "seed2", seed2, "dtype", dtype, "T", _attr_T)
|
||
|
|
_result = _execute.execute(b"RandomStandardNormal", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomStandardNormal", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_RandomUniform_dtype = TypeVar("TV_RandomUniform_dtype", _atypes.BFloat16, _atypes.Float32, _atypes.Float64, _atypes.Half)
|
||
|
|
TV_RandomUniform_T = TypeVar("TV_RandomUniform_T", _atypes.Int32, _atypes.Int64)
|
||
|
|
|
||
|
|
def random_uniform(shape: Annotated[Any, TV_RandomUniform_T], dtype: TV_RandomUniform_dtype, seed:int=0, seed2:int=0, name=None) -> Annotated[Any, TV_RandomUniform_dtype]:
|
||
|
|
r"""Outputs random values from a uniform distribution.
|
||
|
|
|
||
|
|
The generated values follow a uniform distribution in the range `[0, 1)`. The
|
||
|
|
lower bound 0 is included in the range, while the upper bound 1 is excluded.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
|
||
|
|
The shape of the output tensor.
|
||
|
|
dtype: A `tf.DType` from: `tf.half, tf.bfloat16, tf.float32, tf.float64`.
|
||
|
|
The type of the output.
|
||
|
|
seed: An optional `int`. Defaults to `0`.
|
||
|
|
If either `seed` or `seed2` are set to be non-zero, the random number
|
||
|
|
generator is seeded by the given seed. Otherwise, it is seeded by a
|
||
|
|
random seed.
|
||
|
|
seed2: An optional `int`. Defaults to `0`.
|
||
|
|
A second seed to avoid seed collision.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `dtype`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "RandomUniform", name, shape, "seed", seed, "seed2", seed2,
|
||
|
|
"dtype", dtype)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return random_uniform_eager_fallback(
|
||
|
|
shape, seed=seed, seed2=seed2, dtype=dtype, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
dtype = _execute.make_type(dtype, "dtype")
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"RandomUniform", shape=shape, dtype=dtype, seed=seed, seed2=seed2,
|
||
|
|
name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("seed", _op._get_attr_int("seed"), "seed2",
|
||
|
|
_op._get_attr_int("seed2"), "dtype",
|
||
|
|
_op._get_attr_type("dtype"), "T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomUniform", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
RandomUniform = tf_export("raw_ops.RandomUniform")(_ops.to_raw_op(random_uniform))
|
||
|
|
|
||
|
|
|
||
|
|
def random_uniform_eager_fallback(shape: Annotated[Any, TV_RandomUniform_T], dtype: TV_RandomUniform_dtype, seed: int, seed2: int, name, ctx) -> Annotated[Any, TV_RandomUniform_dtype]:
|
||
|
|
dtype = _execute.make_type(dtype, "dtype")
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
_attr_T, (shape,) = _execute.args_to_matching_eager([shape], ctx, [_dtypes.int32, _dtypes.int64, ])
|
||
|
|
_inputs_flat = [shape]
|
||
|
|
_attrs = ("seed", seed, "seed2", seed2, "dtype", dtype, "T", _attr_T)
|
||
|
|
_result = _execute.execute(b"RandomUniform", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomUniform", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_RandomUniformInt_Tout = TypeVar("TV_RandomUniformInt_Tout", _atypes.Int32, _atypes.Int64)
|
||
|
|
TV_RandomUniformInt_T = TypeVar("TV_RandomUniformInt_T", _atypes.Int32, _atypes.Int64)
|
||
|
|
|
||
|
|
def random_uniform_int(shape: Annotated[Any, TV_RandomUniformInt_T], minval: Annotated[Any, TV_RandomUniformInt_Tout], maxval: Annotated[Any, TV_RandomUniformInt_Tout], seed:int=0, seed2:int=0, name=None) -> Annotated[Any, TV_RandomUniformInt_Tout]:
|
||
|
|
r"""Outputs random integers from a uniform distribution.
|
||
|
|
|
||
|
|
The generated values are uniform integers in the range `[minval, maxval)`.
|
||
|
|
The lower bound `minval` is included in the range, while the upper bound
|
||
|
|
`maxval` is excluded.
|
||
|
|
|
||
|
|
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 `Tensor`. Must be one of the following types: `int32`, `int64`.
|
||
|
|
The shape of the output tensor.
|
||
|
|
minval: A `Tensor`. Must be one of the following types: `int32`, `int64`.
|
||
|
|
0-D. Inclusive lower bound on the generated integers.
|
||
|
|
maxval: A `Tensor`. Must have the same type as `minval`.
|
||
|
|
0-D. Exclusive upper bound on the generated integers.
|
||
|
|
seed: An optional `int`. Defaults to `0`.
|
||
|
|
If either `seed` or `seed2` are set to be non-zero, the random number
|
||
|
|
generator is seeded by the given seed. Otherwise, it is seeded by a
|
||
|
|
random seed.
|
||
|
|
seed2: An optional `int`. Defaults to `0`.
|
||
|
|
A second seed to avoid seed collision.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor`. Has the same type as `minval`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "RandomUniformInt", name, shape, minval, maxval, "seed", seed,
|
||
|
|
"seed2", seed2)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return random_uniform_int_eager_fallback(
|
||
|
|
shape, minval, maxval, seed=seed, seed2=seed2, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"RandomUniformInt", shape=shape, minval=minval, maxval=maxval,
|
||
|
|
seed=seed, seed2=seed2, name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("seed", _op._get_attr_int("seed"), "seed2",
|
||
|
|
_op._get_attr_int("seed2"), "Tout", _op._get_attr_type("Tout"),
|
||
|
|
"T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomUniformInt", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
RandomUniformInt = tf_export("raw_ops.RandomUniformInt")(_ops.to_raw_op(random_uniform_int))
|
||
|
|
|
||
|
|
|
||
|
|
def random_uniform_int_eager_fallback(shape: Annotated[Any, TV_RandomUniformInt_T], minval: Annotated[Any, TV_RandomUniformInt_Tout], maxval: Annotated[Any, TV_RandomUniformInt_Tout], seed: int, seed2: int, name, ctx) -> Annotated[Any, TV_RandomUniformInt_Tout]:
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
_attr_Tout, _inputs_Tout = _execute.args_to_matching_eager([minval, maxval], ctx, [_dtypes.int32, _dtypes.int64, ])
|
||
|
|
(minval, maxval) = _inputs_Tout
|
||
|
|
_attr_T, (shape,) = _execute.args_to_matching_eager([shape], ctx, [_dtypes.int32, _dtypes.int64, ])
|
||
|
|
_inputs_flat = [shape, minval, maxval]
|
||
|
|
_attrs = ("seed", seed, "seed2", seed2, "Tout", _attr_Tout, "T", _attr_T)
|
||
|
|
_result = _execute.execute(b"RandomUniformInt", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"RandomUniformInt", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
|
||
|
|
TV_TruncatedNormal_dtype = TypeVar("TV_TruncatedNormal_dtype", _atypes.BFloat16, _atypes.Float32, _atypes.Float64, _atypes.Half)
|
||
|
|
TV_TruncatedNormal_T = TypeVar("TV_TruncatedNormal_T", _atypes.Int32, _atypes.Int64)
|
||
|
|
|
||
|
|
def truncated_normal(shape: Annotated[Any, TV_TruncatedNormal_T], dtype: TV_TruncatedNormal_dtype, seed:int=0, seed2:int=0, name=None) -> Annotated[Any, TV_TruncatedNormal_dtype]:
|
||
|
|
r"""Outputs random values from a truncated normal distribution.
|
||
|
|
|
||
|
|
The generated values follow a normal distribution with mean 0 and standard
|
||
|
|
deviation 1, except that values whose magnitude is more than 2 standard
|
||
|
|
deviations from the mean are dropped and re-picked.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
|
||
|
|
The shape of the output tensor.
|
||
|
|
dtype: A `tf.DType` from: `tf.half, tf.bfloat16, tf.float32, tf.float64`.
|
||
|
|
The type of the output.
|
||
|
|
seed: An optional `int`. Defaults to `0`.
|
||
|
|
If either `seed` or `seed2` are set to be non-zero, the random number
|
||
|
|
generator is seeded by the given seed. Otherwise, it is seeded by a
|
||
|
|
random seed.
|
||
|
|
seed2: An optional `int`. Defaults to `0`.
|
||
|
|
A second seed to avoid seed collision.
|
||
|
|
name: A name for the operation (optional).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
A `Tensor` of type `dtype`.
|
||
|
|
"""
|
||
|
|
_ctx = _context._context or _context.context()
|
||
|
|
tld = _ctx._thread_local_data
|
||
|
|
if tld.is_eager:
|
||
|
|
try:
|
||
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
||
|
|
_ctx, "TruncatedNormal", name, shape, "seed", seed, "seed2", seed2,
|
||
|
|
"dtype", dtype)
|
||
|
|
return _result
|
||
|
|
except _core._NotOkStatusException as e:
|
||
|
|
_ops.raise_from_not_ok_status(e, name)
|
||
|
|
except _core._FallbackException:
|
||
|
|
pass
|
||
|
|
try:
|
||
|
|
return truncated_normal_eager_fallback(
|
||
|
|
shape, seed=seed, seed2=seed2, dtype=dtype, name=name, ctx=_ctx)
|
||
|
|
except _core._SymbolicException:
|
||
|
|
pass # Add nodes to the TensorFlow graph.
|
||
|
|
# Add nodes to the TensorFlow graph.
|
||
|
|
dtype = _execute.make_type(dtype, "dtype")
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
||
|
|
"TruncatedNormal", shape=shape, dtype=dtype, seed=seed, seed2=seed2,
|
||
|
|
name=name)
|
||
|
|
_result = _outputs[:]
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_attrs = ("seed", _op._get_attr_int("seed"), "seed2",
|
||
|
|
_op._get_attr_int("seed2"), "dtype",
|
||
|
|
_op._get_attr_type("dtype"), "T", _op._get_attr_type("T"))
|
||
|
|
_inputs_flat = _op.inputs
|
||
|
|
_execute.record_gradient(
|
||
|
|
"TruncatedNormal", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|
||
|
|
TruncatedNormal = tf_export("raw_ops.TruncatedNormal")(_ops.to_raw_op(truncated_normal))
|
||
|
|
|
||
|
|
|
||
|
|
def truncated_normal_eager_fallback(shape: Annotated[Any, TV_TruncatedNormal_T], dtype: TV_TruncatedNormal_dtype, seed: int, seed2: int, name, ctx) -> Annotated[Any, TV_TruncatedNormal_dtype]:
|
||
|
|
dtype = _execute.make_type(dtype, "dtype")
|
||
|
|
if seed is None:
|
||
|
|
seed = 0
|
||
|
|
seed = _execute.make_int(seed, "seed")
|
||
|
|
if seed2 is None:
|
||
|
|
seed2 = 0
|
||
|
|
seed2 = _execute.make_int(seed2, "seed2")
|
||
|
|
_attr_T, (shape,) = _execute.args_to_matching_eager([shape], ctx, [_dtypes.int32, _dtypes.int64, ])
|
||
|
|
_inputs_flat = [shape]
|
||
|
|
_attrs = ("seed", seed, "seed2", seed2, "dtype", dtype, "T", _attr_T)
|
||
|
|
_result = _execute.execute(b"TruncatedNormal", 1, inputs=_inputs_flat,
|
||
|
|
attrs=_attrs, ctx=ctx, name=name)
|
||
|
|
if _execute.must_record_gradient():
|
||
|
|
_execute.record_gradient(
|
||
|
|
"TruncatedNormal", _inputs_flat, _attrs, _result)
|
||
|
|
_result, = _result
|
||
|
|
return _result
|
||
|
|
|