bf734b0eec
* Add torch backend - random * Add Random Unit Test * Adds Random Unit Test * Random Test * Adds Random Test
103 lines
4.6 KiB
Python
103 lines
4.6 KiB
Python
from keras_core import backend
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from keras_core.api_export import keras_core_export
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@keras_core_export("keras_core.random.normal")
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def normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
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"""Draw random samples from a normal (Gaussian) distribution.
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Args:
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shape: The shape of the random values to generate.
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mean: Floats, defaults to 0. Mean of the random values to generate.
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stddev: Floats, defaults to 1. Standard deviation of the random values
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to generate.
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dtype: Optional dtype of the tensor. Only floating point types are
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supported. If not specified, `keras_core.config.floatx()` is used,
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which defaults to `float32` unless you configured it otherwise (via
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`keras_core.config.set_floatx(float_dtype)`).
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seed: A Python integer or instance of
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`keras_core.random.SeedGenerator`.
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Used to make the behavior of the initializer
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deterministic. Note that an initializer seeded with an integer
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or None (unseeded) will produce the same random values
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across multiple calls. To get different random values
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across multiple calls, use as seed an instance
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of `keras_core.random.SeedGenerator`.
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"""
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return backend.random.normal(
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shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed
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)
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@keras_core_export("keras_core.random.uniform")
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def uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
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"""Draw samples from a uniform distribution.
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The generated values follow a uniform distribution in the range
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`[minval, maxval)`. The lower bound `minval` is included in the range,
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while the upper bound `maxval` is excluded.
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For floats, the default range is `[0, 1)`. For ints, at least `maxval`
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must be specified explicitly.
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Args:
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shape: The shape of the random values to generate.
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minval: Floats, defaults to 0. Lower bound of the range of
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random values to generate (inclusive).
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maxval: Floats, defaults to 1. Upper bound of the range of
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random values to generate (exclusive).
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dtype: Optional dtype of the tensor. Only floating point types are
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supported. If not specified, `keras_core.config.floatx()` is used,
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which defaults to `float32` unless you configured it otherwise (via
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`keras_core.config.set_floatx(float_dtype)`)
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seed: A Python integer or instance of
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`keras_core.random.SeedGenerator`.
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Used to make the behavior of the initializer
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deterministic. Note that an initializer seeded with an integer
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or None (unseeded) will produce the same random values
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across multiple calls. To get different random values
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across multiple calls, use as seed an instance
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of `keras_core.random.SeedGenerator`.
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"""
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return backend.random.uniform(
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shape, minval=minval, maxval=maxval, dtype=dtype, seed=seed
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)
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@keras_core_export("keras_core.random.truncated_normal")
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def truncated_normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
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"""Draw samples from a truncated normal distribution.
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The values are drawn from a normal distribution with specified mean and
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standard deviation, discarding and re-drawing any samples that are more
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than two standard deviations from the mean.
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Args:
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shape: The shape of the random values to generate.
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mean: Floats, defaults to 0. Mean of the random values to generate.
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stddev: Floats, defaults to 1. Standard deviation of the random values
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to generate.
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dtype: Optional dtype of the tensor. Only floating point types are
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supported. If not specified, `keras.config.floatx()` is used,
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which defaults to `float32` unless you configured it otherwise (via
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`keras.config.set_floatx(float_dtype)`)
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seed: A Python integer or instance of
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`keras_core.random.SeedGenerator`.
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Used to make the behavior of the initializer
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deterministic. Note that an initializer seeded with an integer
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or None (unseeded) will produce the same random values
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across multiple calls. To get different random values
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across multiple calls, use as seed an instance
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of `keras_core.random.SeedGenerator`.
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"""
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return backend.random.truncated_normal(
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shape, mean=mean, stddev=stddev, dtype=dtype, seed=seed
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)
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@keras_core_export("keras_core.random.dropout")
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def dropout(inputs, rate, noise_shape=None, seed=None):
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return backend.random.dropout(
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inputs, rate, noise_shape=noise_shape, seed=seed
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)
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