1182 lines
41 KiB
Python
1182 lines
41 KiB
Python
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# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Keras initializers."""
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import math
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import warnings
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import tensorflow.compat.v2 as tf
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from keras import backend
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from keras.dtensor import utils
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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_PARTITION_SHAPE = "partition_shape"
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_PARTITION_OFFSET = "partition_offset"
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_LAYOUT = "layout"
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_ALLOWED_INITIALIZER_KWARGS = [_PARTITION_SHAPE, _PARTITION_OFFSET, _LAYOUT]
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@keras_export("keras.initializers.Initializer")
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class Initializer:
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"""Initializer base class: all Keras initializers inherit from this class.
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Initializers should implement a `__call__` method with the following
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signature:
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```python
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def __call__(self, shape, dtype=None, **kwargs):
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# returns a tensor of shape `shape` and dtype `dtype`
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# containing values drawn from a distribution of your choice.
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```
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Optionally, you an also implement the method `get_config` and the class
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method `from_config` in order to support serialization -- just like with
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any Keras object.
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Here's a simple example: a random normal initializer.
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```python
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import tensorflow as tf
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class ExampleRandomNormal(tf.keras.initializers.Initializer):
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def __init__(self, mean, stddev):
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self.mean = mean
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self.stddev = stddev
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def __call__(self, shape, dtype=None, **kwargs):
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return tf.random.normal(
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shape, mean=self.mean, stddev=self.stddev, dtype=dtype)
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def get_config(self): # To support serialization
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return {"mean": self.mean, "stddev": self.stddev}
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```
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Note that we don't have to implement `from_config` in the example above
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since the constructor arguments of the class the keys in the config returned
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by `get_config` are the same. In this case, the default `from_config` works
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fine.
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"""
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def __call__(self, shape, dtype=None, **kwargs):
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"""Returns a tensor object initialized as specified by the initializer.
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Args:
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shape: Shape of the tensor.
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dtype: Optional dtype of the tensor.
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**kwargs: Additional keyword arguments.
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"""
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raise NotImplementedError(
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"Initializer subclasses must implement the `__call__()` method."
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)
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def get_config(self):
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"""Returns the initializer's configuration as a JSON-serializable dict.
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Returns:
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A JSON-serializable Python dict.
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"""
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return {}
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@classmethod
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def from_config(cls, config):
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"""Instantiates an initializer from a configuration dictionary.
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Example:
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```python
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initializer = RandomUniform(-1, 1)
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config = initializer.get_config()
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initializer = RandomUniform.from_config(config)
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```
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Args:
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config: A Python dictionary, the output of `get_config`.
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Returns:
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A `tf.keras.initializers.Initializer` instance.
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"""
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config.pop("dtype", None)
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return cls(**config)
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def _warn_reuse(self):
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if getattr(self, "_used", False):
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if getattr(self, "seed", None) is None:
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warnings.warn(
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f"The initializer {self.__class__.__name__} is unseeded "
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"and being called multiple times, which will return "
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"identical values each time (even if the initializer is "
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"unseeded). Please update your code to provide a seed to "
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"the initializer, or avoid using the same initalizer "
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"instance more than once."
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)
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else:
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self._used = True
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@keras_export("keras.initializers.Zeros", "keras.initializers.zeros", v1=[])
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class Zeros(Initializer):
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"""Initializer that generates tensors initialized to 0.
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Also available via the shortcut function `tf.keras.initializers.zeros`.
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Examples:
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>>> # Standalone usage:
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>>> initializer = tf.keras.initializers.Zeros()
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>>> values = initializer(shape=(2, 2))
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>>> # Usage in a Keras layer:
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>>> initializer = tf.keras.initializers.Zeros()
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>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
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"""
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def __call__(self, shape, dtype=None, **kwargs):
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"""Returns a tensor object initialized as specified by the initializer.
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Args:
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shape: Shape of the tensor.
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dtype: Optional dtype of the tensor. Only numeric or boolean dtypes
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are supported. If not specified, `tf.keras.backend.floatx()` is
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used, which default to `float32` unless you configured it otherwise
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(via `tf.keras.backend.set_floatx(float_dtype)`).
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**kwargs: Additional keyword arguments.
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"""
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_validate_kwargs(self.__class__.__name__, kwargs)
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dtype = _get_dtype(dtype)
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if not dtype.is_numpy_compatible or dtype == tf.string:
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raise ValueError(f"Expected numeric or boolean dtype, got {dtype}.")
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if _PARTITION_SHAPE in kwargs:
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shape = kwargs[_PARTITION_SHAPE]
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layout = kwargs.pop("layout", None)
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if layout:
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return utils.call_with_layout(
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tf.zeros, layout, shape=shape, dtype=dtype
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)
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return tf.zeros(shape, dtype)
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@keras_export("keras.initializers.Ones", "keras.initializers.ones", v1=[])
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class Ones(Initializer):
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"""Initializer that generates tensors initialized to 1.
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Also available via the shortcut function `tf.keras.initializers.ones`.
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Examples:
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>>> # Standalone usage:
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>>> initializer = tf.keras.initializers.Ones()
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>>> values = initializer(shape=(2, 2))
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>>> # Usage in a Keras layer:
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>>> initializer = tf.keras.initializers.Ones()
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>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
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"""
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def __call__(self, shape, dtype=None, **kwargs):
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"""Returns a tensor object initialized as specified by the initializer.
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Args:
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shape: Shape of the tensor.
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dtype: Optional dtype of the tensor. Only numeric or boolean dtypes
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are supported. If not specified, `tf.keras.backend.floatx()` is
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used, which default to `float32` unless you configured it otherwise
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(via `tf.keras.backend.set_floatx(float_dtype)`).
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**kwargs: Additional keyword arguments.
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"""
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_validate_kwargs(self.__class__.__name__, kwargs)
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dtype = _get_dtype(dtype)
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if not dtype.is_numpy_compatible or dtype == tf.string:
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raise ValueError(f"Expected numeric or boolean dtype, got {dtype}.")
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if _PARTITION_SHAPE in kwargs:
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shape = kwargs[_PARTITION_SHAPE]
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layout = kwargs.pop("layout", None)
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if layout:
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return utils.call_with_layout(
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tf.ones, layout, shape=shape, dtype=dtype
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)
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return tf.ones(shape, dtype)
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@keras_export(
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"keras.initializers.Constant", "keras.initializers.constant", v1=[]
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)
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class Constant(Initializer):
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"""Initializer that generates tensors with constant values.
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Also available via the shortcut function `tf.keras.initializers.constant`.
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Only scalar values are allowed.
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The constant value provided must be convertible to the dtype requested
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when calling the initializer.
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Examples:
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>>> # Standalone usage:
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>>> initializer = tf.keras.initializers.Constant(3.)
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>>> values = initializer(shape=(2, 2))
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>>> # Usage in a Keras layer:
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>>> initializer = tf.keras.initializers.Constant(3.)
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>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
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Args:
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value: A Python scalar.
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"""
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def __init__(self, value=0):
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self.value = value
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def __call__(self, shape, dtype=None, **kwargs):
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"""Returns a tensor object initialized to `self.value`.
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Args:
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shape: Shape of the tensor.
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dtype: Optional dtype of the tensor. If not specified,
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`tf.keras.backend.floatx()` is used,
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which default to `float32` unless you configured it otherwise
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(via `tf.keras.backend.set_floatx(float_dtype)`).
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**kwargs: Additional keyword arguments.
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"""
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_validate_kwargs(self.__class__.__name__, kwargs)
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dtype = _get_dtype(dtype)
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if _PARTITION_SHAPE in kwargs:
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shape = kwargs[_PARTITION_SHAPE]
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layout = kwargs.pop("layout", None)
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if layout:
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return utils.call_with_layout(
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tf.constant, layout, self.value, shape=shape, dtype=dtype
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)
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return tf.constant(self.value, dtype=_get_dtype(dtype), shape=shape)
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def get_config(self):
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return {"value": self.value}
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@keras_export(
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"keras.initializers.RandomUniform",
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"keras.initializers.random_uniform",
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v1=[],
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)
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class RandomUniform(Initializer):
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"""Initializer that generates tensors with a uniform distribution.
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Also available via the shortcut function
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`tf.keras.initializers.random_uniform`.
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Examples:
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>>> # Standalone usage:
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>>> initializer = tf.keras.initializers.RandomUniform(minval=0., maxval=1.)
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>>> values = initializer(shape=(2, 2))
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>>> # Usage in a Keras layer:
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>>> initializer = tf.keras.initializers.RandomUniform(minval=0., maxval=1.)
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>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
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Args:
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minval: A python scalar or a scalar tensor. Lower bound of the range of
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random values to generate (inclusive).
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maxval: A python scalar or a scalar tensor. Upper bound of the range of
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random values to generate (exclusive).
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seed: A Python integer. Used to make the behavior of the initializer
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deterministic. Note that a seeded initializer will produce the same
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random values across multiple calls.
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"""
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def __init__(self, minval=-0.05, maxval=0.05, seed=None):
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self.minval = minval
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self.maxval = maxval
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self.seed = seed
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self._random_generator = backend.RandomGenerator(
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seed, rng_type="stateless"
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)
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def __call__(self, shape, dtype=None, **kwargs):
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"""Returns a tensor object initialized as specified by the initializer.
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Args:
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shape: Shape of the tensor.
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dtype: Optional dtype of the tensor. Only floating point and integer
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types are supported. If not specified,
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`tf.keras.backend.floatx()` is used,
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which default to `float32` unless you configured it otherwise
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(via `tf.keras.backend.set_floatx(float_dtype)`).
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**kwargs: Additional keyword arguments.
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"""
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_validate_kwargs(self.__class__.__name__, kwargs)
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dtype = _get_dtype(dtype)
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if not dtype.is_floating and not dtype.is_integer:
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raise ValueError(f"Expected float or integer dtype, got {dtype}.")
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if _PARTITION_SHAPE in kwargs:
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shape = kwargs[_PARTITION_SHAPE]
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partition_offset = kwargs.get(_PARTITION_OFFSET, None)
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if partition_offset is None:
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# We skip the reuse warning for partitioned variable, since the same
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# initializer will be called multiple times for each partition.
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self._warn_reuse()
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nonce = hash(partition_offset) if partition_offset else None
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layout = kwargs.pop("layout", None)
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if layout:
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_ensure_keras_seeded()
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return utils.call_with_layout(
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self._random_generator.random_uniform,
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layout,
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shape,
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self.minval,
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self.maxval,
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dtype,
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nonce,
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)
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return self._random_generator.random_uniform(
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shape, self.minval, self.maxval, dtype, nonce
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)
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def get_config(self):
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return {"minval": self.minval, "maxval": self.maxval, "seed": self.seed}
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@keras_export(
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"keras.initializers.RandomNormal", "keras.initializers.random_normal", v1=[]
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)
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class RandomNormal(Initializer):
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"""Initializer that generates tensors with a normal distribution.
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Also available via the shortcut function
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`tf.keras.initializers.random_normal`.
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Examples:
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>>> # Standalone usage:
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>>> initializer = tf.keras.initializers.RandomNormal(mean=0., stddev=1.)
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>>> values = initializer(shape=(2, 2))
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>>> # Usage in a Keras layer:
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>>> initializer = tf.keras.initializers.RandomNormal(mean=0., stddev=1.)
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>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
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Args:
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mean: a python scalar or a scalar tensor. Mean of the random values to
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generate.
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stddev: a python scalar or a scalar tensor. Standard deviation of the
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random values to generate.
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seed: A Python integer. Used to make the behavior of the initializer
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deterministic. Note that a seeded initializer will produce the same
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random values across multiple calls.
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"""
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def __init__(self, mean=0.0, stddev=0.05, seed=None):
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self.mean = mean
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self.stddev = stddev
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self.seed = seed
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self._random_generator = backend.RandomGenerator(
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seed, rng_type="stateless"
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)
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def __call__(self, shape, dtype=None, **kwargs):
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"""Returns a tensor object initialized to random normal values.
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Args:
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shape: Shape of the tensor.
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dtype: Optional dtype of the tensor. Only floating point types are
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supported. If not specified, `tf.keras.backend.floatx()` is used,
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which default to `float32` unless you configured it otherwise (via
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`tf.keras.backend.set_floatx(float_dtype)`)
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**kwargs: Additional keyword arguments.
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"""
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_validate_kwargs(self.__class__.__name__, kwargs)
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dtype = _assert_float_dtype(_get_dtype(dtype))
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if _PARTITION_SHAPE in kwargs:
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shape = kwargs[_PARTITION_SHAPE]
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partition_offset = kwargs.get(_PARTITION_OFFSET, None)
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if partition_offset is None:
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# We skip the reuse warning for partitioned variable, since the same
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# initializer will be called multiple times for each partition.
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self._warn_reuse()
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nonce = hash(partition_offset) if partition_offset else None
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layout = kwargs.pop("layout", None)
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if layout:
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_ensure_keras_seeded()
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return utils.call_with_layout(
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self._random_generator.random_normal,
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layout,
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shape,
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self.mean,
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self.stddev,
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dtype,
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nonce,
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)
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return self._random_generator.random_normal(
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shape, self.mean, self.stddev, dtype, nonce
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)
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def get_config(self):
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return {"mean": self.mean, "stddev": self.stddev, "seed": self.seed}
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@keras_export(
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"keras.initializers.TruncatedNormal",
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"keras.initializers.truncated_normal",
|
||
|
v1=[],
|
||
|
)
|
||
|
class TruncatedNormal(Initializer):
|
||
|
"""Initializer that generates a truncated normal distribution.
|
||
|
|
||
|
Also available via the shortcut function
|
||
|
`tf.keras.initializers.truncated_normal`.
|
||
|
|
||
|
The values generated are similar to values from a
|
||
|
`tf.keras.initializers.RandomNormal` initializer except that values more
|
||
|
than two standard deviations from the mean are
|
||
|
discarded and re-drawn.
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
>>> # Standalone usage:
|
||
|
>>> initializer = tf.keras.initializers.TruncatedNormal(mean=0., stddev=1.)
|
||
|
>>> values = initializer(shape=(2, 2))
|
||
|
|
||
|
>>> # Usage in a Keras layer:
|
||
|
>>> initializer = tf.keras.initializers.TruncatedNormal(mean=0., stddev=1.)
|
||
|
>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
|
||
|
|
||
|
Args:
|
||
|
mean: a python scalar or a scalar tensor. Mean of the random values
|
||
|
to generate.
|
||
|
stddev: a python scalar or a scalar tensor. Standard deviation of the
|
||
|
random values to generate before truncation.
|
||
|
seed: A Python integer. Used to make the behavior of the initializer
|
||
|
deterministic. Note that a seeded initializer will produce the same
|
||
|
random values across multiple calls.
|
||
|
"""
|
||
|
|
||
|
def __init__(self, mean=0.0, stddev=0.05, seed=None):
|
||
|
self.mean = mean
|
||
|
self.stddev = stddev
|
||
|
self.seed = seed
|
||
|
self._random_generator = backend.RandomGenerator(
|
||
|
seed, rng_type="stateless"
|
||
|
)
|
||
|
|
||
|
def __call__(self, shape, dtype=None, **kwargs):
|
||
|
"""Returns a tensor initialized to random normal values (truncated).
|
||
|
|
||
|
Args:
|
||
|
shape: Shape of the tensor.
|
||
|
dtype: Optional dtype of the tensor. Only floating point types are
|
||
|
supported. If not specified, `tf.keras.backend.floatx()` is used,
|
||
|
which default to `float32` unless you configured it otherwise (via
|
||
|
`tf.keras.backend.set_floatx(float_dtype)`)
|
||
|
**kwargs: Additional keyword arguments.
|
||
|
"""
|
||
|
_validate_kwargs(self.__class__.__name__, kwargs)
|
||
|
dtype = _assert_float_dtype(_get_dtype(dtype))
|
||
|
if _PARTITION_SHAPE in kwargs:
|
||
|
shape = kwargs[_PARTITION_SHAPE]
|
||
|
partition_offset = kwargs.get(_PARTITION_OFFSET, None)
|
||
|
if partition_offset is None:
|
||
|
# We skip the reuse warning for partitioned variable, since the same
|
||
|
# initializer will be called multiple times for each partition.
|
||
|
self._warn_reuse()
|
||
|
nonce = hash(partition_offset) if partition_offset else None
|
||
|
layout = kwargs.pop("layout", None)
|
||
|
if layout:
|
||
|
# TODO(scottzhu): Remove this once the forward compat period above
|
||
|
# is expired.
|
||
|
self._random_generator._rng_type = (
|
||
|
self._random_generator.RNG_STATEFUL
|
||
|
)
|
||
|
_ensure_keras_seeded()
|
||
|
return utils.call_with_layout(
|
||
|
self._random_generator.truncated_normal,
|
||
|
layout,
|
||
|
shape,
|
||
|
self.mean,
|
||
|
self.stddev,
|
||
|
dtype,
|
||
|
nonce,
|
||
|
)
|
||
|
return self._random_generator.truncated_normal(
|
||
|
shape, self.mean, self.stddev, dtype, nonce
|
||
|
)
|
||
|
|
||
|
def get_config(self):
|
||
|
return {"mean": self.mean, "stddev": self.stddev, "seed": self.seed}
|
||
|
|
||
|
|
||
|
@keras_export(
|
||
|
"keras.initializers.VarianceScaling",
|
||
|
"keras.initializers.variance_scaling",
|
||
|
v1=[],
|
||
|
)
|
||
|
class VarianceScaling(Initializer):
|
||
|
"""Initializer that adapts its scale to the shape of its input tensors.
|
||
|
|
||
|
Also available via the shortcut function
|
||
|
`tf.keras.initializers.variance_scaling`.
|
||
|
|
||
|
With `distribution="truncated_normal" or "untruncated_normal"`, samples are
|
||
|
drawn from a truncated/untruncated normal distribution with a mean of zero
|
||
|
and a standard deviation (after truncation, if used) `stddev = sqrt(scale /
|
||
|
n)`, where `n` is:
|
||
|
|
||
|
- number of input units in the weight tensor, if `mode="fan_in"`
|
||
|
- number of output units, if `mode="fan_out"`
|
||
|
- average of the numbers of input and output units, if `mode="fan_avg"`
|
||
|
|
||
|
With `distribution="uniform"`, samples are drawn from a uniform distribution
|
||
|
within `[-limit, limit]`, where `limit = sqrt(3 * scale / n)`.
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
>>> # Standalone usage:
|
||
|
>>> initializer = tf.keras.initializers.VarianceScaling(
|
||
|
... scale=0.1, mode='fan_in', distribution='uniform')
|
||
|
>>> values = initializer(shape=(2, 2))
|
||
|
|
||
|
>>> # Usage in a Keras layer:
|
||
|
>>> initializer = tf.keras.initializers.VarianceScaling(
|
||
|
... scale=0.1, mode='fan_in', distribution='uniform')
|
||
|
>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
|
||
|
|
||
|
Args:
|
||
|
scale: Scaling factor (positive float).
|
||
|
mode: One of "fan_in", "fan_out", "fan_avg".
|
||
|
distribution: Random distribution to use. One of "truncated_normal",
|
||
|
"untruncated_normal" and "uniform".
|
||
|
seed: A Python integer. Used to make the behavior of the initializer
|
||
|
deterministic. Note that a seeded initializer will produce the same
|
||
|
random values across multiple calls.
|
||
|
"""
|
||
|
|
||
|
def __init__(
|
||
|
self,
|
||
|
scale=1.0,
|
||
|
mode="fan_in",
|
||
|
distribution="truncated_normal",
|
||
|
seed=None,
|
||
|
):
|
||
|
if scale <= 0.0:
|
||
|
raise ValueError(
|
||
|
f"`scale` must be positive float. Received: scale={scale}."
|
||
|
)
|
||
|
allowed_modes = {"fan_in", "fan_out", "fan_avg"}
|
||
|
if mode not in allowed_modes:
|
||
|
raise ValueError(
|
||
|
f"Invalid `mode` argument: {mode}. "
|
||
|
f"Please use one of the {allowed_modes}."
|
||
|
)
|
||
|
distribution = distribution.lower()
|
||
|
# Compatibility with keras-team/keras.
|
||
|
if distribution == "normal":
|
||
|
distribution = "truncated_normal"
|
||
|
allowed_distributions = {
|
||
|
"uniform",
|
||
|
"truncated_normal",
|
||
|
"untruncated_normal",
|
||
|
}
|
||
|
if distribution not in allowed_distributions:
|
||
|
raise ValueError(
|
||
|
f"Invalid `distribution` argument: {distribution}."
|
||
|
f"Allowed distributions: {allowed_distributions}."
|
||
|
)
|
||
|
self.scale = scale
|
||
|
self.mode = mode
|
||
|
self.distribution = distribution
|
||
|
self.seed = seed
|
||
|
self._random_generator = backend.RandomGenerator(
|
||
|
seed, rng_type="stateless"
|
||
|
)
|
||
|
|
||
|
def __call__(self, shape, dtype=None, **kwargs):
|
||
|
"""Returns a tensor object initialized as specified by the initializer.
|
||
|
|
||
|
Args:
|
||
|
shape: Shape of the tensor.
|
||
|
dtype: Optional dtype of the tensor. Only floating point types are
|
||
|
supported. If not specified, `tf.keras.backend.floatx()` is used,
|
||
|
which default to `float32` unless you configured it otherwise (via
|
||
|
`tf.keras.backend.set_floatx(float_dtype)`)
|
||
|
**kwargs: Additional keyword arguments.
|
||
|
"""
|
||
|
_validate_kwargs(self.__class__.__name__, kwargs)
|
||
|
dtype = _assert_float_dtype(_get_dtype(dtype))
|
||
|
if _PARTITION_SHAPE in kwargs:
|
||
|
shape = kwargs[_PARTITION_SHAPE]
|
||
|
partition_offset = kwargs.get(_PARTITION_OFFSET, None)
|
||
|
if partition_offset is None:
|
||
|
# We skip the reuse warning for partitioned variable, since the same
|
||
|
# initializer will be called multiple times for each partition.
|
||
|
self._warn_reuse()
|
||
|
nonce = hash(partition_offset) if partition_offset else None
|
||
|
layout = kwargs.pop("layout", None)
|
||
|
if layout:
|
||
|
_ensure_keras_seeded()
|
||
|
return utils.call_with_layout(
|
||
|
self._generate_init_val,
|
||
|
layout,
|
||
|
shape=shape,
|
||
|
dtype=dtype,
|
||
|
nonce=nonce,
|
||
|
)
|
||
|
return self._generate_init_val(shape=shape, dtype=dtype, nonce=nonce)
|
||
|
|
||
|
def _generate_init_val(self, shape, dtype, nonce):
|
||
|
scale = self.scale
|
||
|
fan_in, fan_out = _compute_fans(shape)
|
||
|
if self.mode == "fan_in":
|
||
|
scale /= max(1.0, fan_in)
|
||
|
elif self.mode == "fan_out":
|
||
|
scale /= max(1.0, fan_out)
|
||
|
else:
|
||
|
scale /= max(1.0, (fan_in + fan_out) / 2.0)
|
||
|
if self.distribution == "truncated_normal":
|
||
|
# constant from scipy.stats.truncnorm.std(a=-2, b=2, loc=0.,
|
||
|
# scale=1.)
|
||
|
stddev = math.sqrt(scale) / 0.87962566103423978
|
||
|
return self._random_generator.truncated_normal(
|
||
|
shape, 0.0, stddev, dtype, nonce
|
||
|
)
|
||
|
elif self.distribution == "untruncated_normal":
|
||
|
stddev = math.sqrt(scale)
|
||
|
return self._random_generator.random_normal(
|
||
|
shape, 0.0, stddev, dtype, nonce
|
||
|
)
|
||
|
else:
|
||
|
limit = math.sqrt(3.0 * scale)
|
||
|
return self._random_generator.random_uniform(
|
||
|
shape, -limit, limit, dtype, nonce
|
||
|
)
|
||
|
|
||
|
def get_config(self):
|
||
|
return {
|
||
|
"scale": self.scale,
|
||
|
"mode": self.mode,
|
||
|
"distribution": self.distribution,
|
||
|
"seed": self.seed,
|
||
|
}
|
||
|
|
||
|
|
||
|
@keras_export(
|
||
|
"keras.initializers.Orthogonal", "keras.initializers.orthogonal", v1=[]
|
||
|
)
|
||
|
class Orthogonal(Initializer):
|
||
|
"""Initializer that generates an orthogonal matrix.
|
||
|
|
||
|
Also available via the shortcut function `tf.keras.initializers.orthogonal`.
|
||
|
|
||
|
If the shape of the tensor to initialize is two-dimensional, it is
|
||
|
initialized with an orthogonal matrix obtained from the QR decomposition of
|
||
|
a matrix of random numbers drawn from a normal distribution. If the matrix
|
||
|
has fewer rows than columns then the output will have orthogonal rows.
|
||
|
Otherwise, the output will have orthogonal columns.
|
||
|
|
||
|
If the shape of the tensor to initialize is more than two-dimensional,
|
||
|
a matrix of shape `(shape[0] * ... * shape[n - 2], shape[n - 1])`
|
||
|
is initialized, where `n` is the length of the shape vector.
|
||
|
The matrix is subsequently reshaped to give a tensor of the desired shape.
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
>>> # Standalone usage:
|
||
|
>>> initializer = tf.keras.initializers.Orthogonal()
|
||
|
>>> values = initializer(shape=(2, 2))
|
||
|
|
||
|
>>> # Usage in a Keras layer:
|
||
|
>>> initializer = tf.keras.initializers.Orthogonal()
|
||
|
>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
|
||
|
|
||
|
Args:
|
||
|
gain: multiplicative factor to apply to the orthogonal matrix
|
||
|
seed: A Python integer. Used to make the behavior of the initializer
|
||
|
deterministic. Note that a seeded initializer will produce the same
|
||
|
random values across multiple calls.
|
||
|
|
||
|
References:
|
||
|
- [Saxe et al., 2014](https://openreview.net/forum?id=_wzZwKpTDF_9C)
|
||
|
"""
|
||
|
|
||
|
def __init__(self, gain=1.0, seed=None):
|
||
|
self.gain = gain
|
||
|
self.seed = seed
|
||
|
self._random_generator = backend.RandomGenerator(
|
||
|
seed, rng_type="stateless"
|
||
|
)
|
||
|
|
||
|
def __call__(self, shape, dtype=None, **kwargs):
|
||
|
"""Returns a tensor object initialized to an orthogonal matrix.
|
||
|
|
||
|
Args:
|
||
|
shape: Shape of the tensor.
|
||
|
dtype: Optional dtype of the tensor. Only floating point types are
|
||
|
supported. If not specified, `tf.keras.backend.floatx()` is used,
|
||
|
which default to `float32` unless you configured it otherwise
|
||
|
(via `tf.keras.backend.set_floatx(float_dtype)`)
|
||
|
**kwargs: Additional keyword arguments.
|
||
|
"""
|
||
|
_validate_kwargs(
|
||
|
self.__class__.__name__, kwargs, support_partition=False
|
||
|
)
|
||
|
dtype = _assert_float_dtype(_get_dtype(dtype))
|
||
|
# Check the shape
|
||
|
if len(shape) < 2:
|
||
|
raise ValueError(
|
||
|
"The tensor to initialize must be "
|
||
|
"at least two-dimensional. Received: "
|
||
|
f"shape={shape} of rank {len(shape)}."
|
||
|
)
|
||
|
self._warn_reuse()
|
||
|
layout = kwargs.pop("layout", None)
|
||
|
if layout:
|
||
|
_ensure_keras_seeded()
|
||
|
return utils.call_with_layout(
|
||
|
self._generate_init_val, layout, shape=shape, dtype=dtype
|
||
|
)
|
||
|
return self._generate_init_val(shape, dtype)
|
||
|
|
||
|
def _generate_init_val(self, shape, dtype):
|
||
|
# Flatten the input shape with the last dimension remaining
|
||
|
# its original shape so it works for conv2d
|
||
|
num_rows = 1
|
||
|
for dim in shape[:-1]:
|
||
|
num_rows *= dim
|
||
|
num_cols = shape[-1]
|
||
|
flat_shape = (max(num_cols, num_rows), min(num_cols, num_rows))
|
||
|
|
||
|
# Generate a random matrix
|
||
|
a = self._random_generator.random_normal(flat_shape, dtype=dtype)
|
||
|
# Compute the qr factorization
|
||
|
q, r = tf.linalg.qr(a, full_matrices=False)
|
||
|
# Make Q uniform
|
||
|
d = tf.linalg.tensor_diag_part(r)
|
||
|
q *= tf.sign(d)
|
||
|
if num_rows < num_cols:
|
||
|
q = tf.linalg.matrix_transpose(q)
|
||
|
return self.gain * tf.reshape(q, shape)
|
||
|
|
||
|
def get_config(self):
|
||
|
return {"gain": self.gain, "seed": self.seed}
|
||
|
|
||
|
|
||
|
@keras_export(
|
||
|
"keras.initializers.Identity", "keras.initializers.identity", v1=[]
|
||
|
)
|
||
|
class Identity(Initializer):
|
||
|
"""Initializer that generates the identity matrix.
|
||
|
|
||
|
Also available via the shortcut function `tf.keras.initializers.identity`.
|
||
|
|
||
|
Only usable for generating 2D matrices.
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
>>> # Standalone usage:
|
||
|
>>> initializer = tf.keras.initializers.Identity()
|
||
|
>>> values = initializer(shape=(2, 2))
|
||
|
|
||
|
>>> # Usage in a Keras layer:
|
||
|
>>> initializer = tf.keras.initializers.Identity()
|
||
|
>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
|
||
|
|
||
|
Args:
|
||
|
gain: Multiplicative factor to apply to the identity matrix.
|
||
|
"""
|
||
|
|
||
|
def __init__(self, gain=1.0):
|
||
|
self.gain = gain
|
||
|
|
||
|
def __call__(self, shape, dtype=None, **kwargs):
|
||
|
"""Returns a tensor object initialized to a 2D identity matrix.
|
||
|
|
||
|
Args:
|
||
|
shape: Shape of the tensor. It should have exactly rank 2.
|
||
|
dtype: Optional dtype of the tensor. Only floating point types are
|
||
|
supported. If not specified, `tf.keras.backend.floatx()` is used,
|
||
|
which default to `float32` unless you configured it otherwise
|
||
|
(via `tf.keras.backend.set_floatx(float_dtype)`)
|
||
|
**kwargs: Additional keyword arguments.
|
||
|
"""
|
||
|
_validate_kwargs(
|
||
|
self.__class__.__name__, kwargs, support_partition=False
|
||
|
)
|
||
|
dtype = _assert_float_dtype(_get_dtype(dtype))
|
||
|
if len(shape) != 2:
|
||
|
raise ValueError(
|
||
|
"Identity matrix initializer can only be used for 2D matrices. "
|
||
|
f"Received: shape={shape} of rank {len(shape)}."
|
||
|
)
|
||
|
layout = kwargs.pop("layout", None)
|
||
|
if layout:
|
||
|
return utils.call_with_layout(
|
||
|
self._generate_init_val, layout, shape=shape, dtype=dtype
|
||
|
)
|
||
|
return self._generate_init_val(shape, dtype)
|
||
|
|
||
|
def _generate_init_val(self, shape, dtype):
|
||
|
initializer = tf.eye(*shape, dtype=dtype)
|
||
|
return self.gain * initializer
|
||
|
|
||
|
def get_config(self):
|
||
|
return {"gain": self.gain}
|
||
|
|
||
|
|
||
|
@keras_export(
|
||
|
"keras.initializers.GlorotUniform",
|
||
|
"keras.initializers.glorot_uniform",
|
||
|
v1=[],
|
||
|
)
|
||
|
class GlorotUniform(VarianceScaling):
|
||
|
"""The Glorot uniform initializer, also called Xavier uniform initializer.
|
||
|
|
||
|
Also available via the shortcut function
|
||
|
`tf.keras.initializers.glorot_uniform`.
|
||
|
|
||
|
Draws samples from a uniform distribution within `[-limit, limit]`, where
|
||
|
`limit = sqrt(6 / (fan_in + fan_out))` (`fan_in` is the number of input
|
||
|
units in the weight tensor and `fan_out` is the number of output units).
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
>>> # Standalone usage:
|
||
|
>>> initializer = tf.keras.initializers.GlorotUniform()
|
||
|
>>> values = initializer(shape=(2, 2))
|
||
|
|
||
|
>>> # Usage in a Keras layer:
|
||
|
>>> initializer = tf.keras.initializers.GlorotUniform()
|
||
|
>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
|
||
|
|
||
|
Args:
|
||
|
seed: A Python integer. Used to make the behavior of the initializer
|
||
|
deterministic. Note that a seeded initializer will not produce the same
|
||
|
random values across multiple calls, but multiple initializers will
|
||
|
produce the same sequence when constructed with the same seed value.
|
||
|
|
||
|
References:
|
||
|
- [Glorot et al., 2010](http://proceedings.mlr.press/v9/glorot10a.html)
|
||
|
"""
|
||
|
|
||
|
def __init__(self, seed=None):
|
||
|
super().__init__(
|
||
|
scale=1.0, mode="fan_avg", distribution="uniform", seed=seed
|
||
|
)
|
||
|
|
||
|
def get_config(self):
|
||
|
return {"seed": self.seed}
|
||
|
|
||
|
|
||
|
@keras_export(
|
||
|
"keras.initializers.GlorotNormal", "keras.initializers.glorot_normal", v1=[]
|
||
|
)
|
||
|
class GlorotNormal(VarianceScaling):
|
||
|
"""The Glorot normal initializer, also called Xavier normal initializer.
|
||
|
|
||
|
Also available via the shortcut function
|
||
|
`tf.keras.initializers.glorot_normal`.
|
||
|
|
||
|
Draws samples from a truncated normal distribution centered on 0 with
|
||
|
`stddev = sqrt(2 / (fan_in + fan_out))` where `fan_in` is the number of
|
||
|
input units in the weight tensor and `fan_out` is the number of output units
|
||
|
in the weight tensor.
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
>>> # Standalone usage:
|
||
|
>>> initializer = tf.keras.initializers.GlorotNormal()
|
||
|
>>> values = initializer(shape=(2, 2))
|
||
|
|
||
|
>>> # Usage in a Keras layer:
|
||
|
>>> initializer = tf.keras.initializers.GlorotNormal()
|
||
|
>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
|
||
|
|
||
|
Args:
|
||
|
seed: A Python integer. Used to make the behavior of the initializer
|
||
|
deterministic. Note that a seeded initializer will not produce the same
|
||
|
random values across multiple calls, but multiple initializers will
|
||
|
produce the same sequence when constructed with the same seed value.
|
||
|
|
||
|
References:
|
||
|
- [Glorot et al., 2010](http://proceedings.mlr.press/v9/glorot10a.html)
|
||
|
"""
|
||
|
|
||
|
def __init__(self, seed=None):
|
||
|
super().__init__(
|
||
|
scale=1.0,
|
||
|
mode="fan_avg",
|
||
|
distribution="truncated_normal",
|
||
|
seed=seed,
|
||
|
)
|
||
|
|
||
|
def get_config(self):
|
||
|
return {"seed": self.seed}
|
||
|
|
||
|
|
||
|
@keras_export(
|
||
|
"keras.initializers.LecunNormal", "keras.initializers.lecun_normal", v1=[]
|
||
|
)
|
||
|
class LecunNormal(VarianceScaling):
|
||
|
"""Lecun normal initializer.
|
||
|
|
||
|
Also available via the shortcut function
|
||
|
`tf.keras.initializers.lecun_normal`.
|
||
|
|
||
|
Initializers allow you to pre-specify an initialization strategy, encoded in
|
||
|
the Initializer object, without knowing the shape and dtype of the variable
|
||
|
being initialized.
|
||
|
|
||
|
Draws samples from a truncated normal distribution centered on 0 with
|
||
|
`stddev = sqrt(1 / fan_in)` where `fan_in` is the number of input units in
|
||
|
the weight tensor.
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
>>> # Standalone usage:
|
||
|
>>> initializer = tf.keras.initializers.LecunNormal()
|
||
|
>>> values = initializer(shape=(2, 2))
|
||
|
|
||
|
>>> # Usage in a Keras layer:
|
||
|
>>> initializer = tf.keras.initializers.LecunNormal()
|
||
|
>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
|
||
|
|
||
|
Args:
|
||
|
seed: A Python integer. Used to make the behavior of the initializer
|
||
|
deterministic. Note that a seeded initializer will not produce the same
|
||
|
random values across multiple calls, but multiple initializers will
|
||
|
produce the same sequence when constructed with the same seed value.
|
||
|
|
||
|
References:
|
||
|
- [Klambauer et al., 2017](https://arxiv.org/abs/1706.02515)
|
||
|
"""
|
||
|
|
||
|
def __init__(self, seed=None):
|
||
|
super().__init__(
|
||
|
scale=1.0, mode="fan_in", distribution="truncated_normal", seed=seed
|
||
|
)
|
||
|
|
||
|
def get_config(self):
|
||
|
return {"seed": self.seed}
|
||
|
|
||
|
|
||
|
@keras_export(
|
||
|
"keras.initializers.LecunUniform", "keras.initializers.lecun_uniform", v1=[]
|
||
|
)
|
||
|
class LecunUniform(VarianceScaling):
|
||
|
"""Lecun uniform initializer.
|
||
|
|
||
|
Also available via the shortcut function
|
||
|
`tf.keras.initializers.lecun_uniform`.
|
||
|
|
||
|
Draws samples from a uniform distribution within `[-limit, limit]`, where
|
||
|
`limit = sqrt(3 / fan_in)` (`fan_in` is the number of input units in the
|
||
|
weight tensor).
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
>>> # Standalone usage:
|
||
|
>>> initializer = tf.keras.initializers.LecunUniform()
|
||
|
>>> values = initializer(shape=(2, 2))
|
||
|
|
||
|
>>> # Usage in a Keras layer:
|
||
|
>>> initializer = tf.keras.initializers.LecunUniform()
|
||
|
>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
|
||
|
|
||
|
Args:
|
||
|
seed: A Python integer. Used to make the behavior of the initializer
|
||
|
deterministic. Note that a seeded initializer will not produce the same
|
||
|
random values across multiple calls, but multiple initializers will
|
||
|
produce the same sequence when constructed with the same seed value.
|
||
|
|
||
|
References:
|
||
|
- [Klambauer et al., 2017](https://arxiv.org/abs/1706.02515)
|
||
|
"""
|
||
|
|
||
|
def __init__(self, seed=None):
|
||
|
super().__init__(
|
||
|
scale=1.0, mode="fan_in", distribution="uniform", seed=seed
|
||
|
)
|
||
|
|
||
|
def get_config(self):
|
||
|
return {"seed": self.seed}
|
||
|
|
||
|
|
||
|
@keras_export(
|
||
|
"keras.initializers.HeNormal", "keras.initializers.he_normal", v1=[]
|
||
|
)
|
||
|
class HeNormal(VarianceScaling):
|
||
|
"""He normal initializer.
|
||
|
|
||
|
Also available via the shortcut function
|
||
|
`tf.keras.initializers.he_normal`.
|
||
|
|
||
|
It draws samples from a truncated normal distribution centered on 0 with
|
||
|
`stddev = sqrt(2 / fan_in)` where `fan_in` is the number of input units in
|
||
|
the weight tensor.
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
>>> # Standalone usage:
|
||
|
>>> initializer = tf.keras.initializers.HeNormal()
|
||
|
>>> values = initializer(shape=(2, 2))
|
||
|
|
||
|
>>> # Usage in a Keras layer:
|
||
|
>>> initializer = tf.keras.initializers.HeNormal()
|
||
|
>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
|
||
|
|
||
|
Args:
|
||
|
seed: A Python integer. Used to make the behavior of the initializer
|
||
|
deterministic. Note that a seeded initializer will not produce the same
|
||
|
random values across multiple calls, but multiple initializers will
|
||
|
produce the same sequence when constructed with the same seed value.
|
||
|
|
||
|
References:
|
||
|
- [He et al., 2015](https://arxiv.org/abs/1502.01852)
|
||
|
"""
|
||
|
|
||
|
def __init__(self, seed=None):
|
||
|
super().__init__(
|
||
|
scale=2.0, mode="fan_in", distribution="truncated_normal", seed=seed
|
||
|
)
|
||
|
|
||
|
def get_config(self):
|
||
|
return {"seed": self.seed}
|
||
|
|
||
|
|
||
|
@keras_export(
|
||
|
"keras.initializers.HeUniform", "keras.initializers.he_uniform", v1=[]
|
||
|
)
|
||
|
class HeUniform(VarianceScaling):
|
||
|
"""He uniform variance scaling initializer.
|
||
|
|
||
|
Also available via the shortcut function
|
||
|
`tf.keras.initializers.he_uniform`.
|
||
|
|
||
|
Draws samples from a uniform distribution within `[-limit, limit]`, where
|
||
|
`limit = sqrt(6 / fan_in)` (`fan_in` is the number of input units in the
|
||
|
weight tensor).
|
||
|
|
||
|
Examples:
|
||
|
|
||
|
>>> # Standalone usage:
|
||
|
>>> initializer = tf.keras.initializers.HeUniform()
|
||
|
>>> values = initializer(shape=(2, 2))
|
||
|
|
||
|
>>> # Usage in a Keras layer:
|
||
|
>>> initializer = tf.keras.initializers.HeUniform()
|
||
|
>>> layer = tf.keras.layers.Dense(3, kernel_initializer=initializer)
|
||
|
|
||
|
Args:
|
||
|
seed: A Python integer. Used to make the behavior of the initializer
|
||
|
deterministic. Note that a seeded initializer will not produce the same
|
||
|
random values across multiple calls, but multiple initializers will
|
||
|
produce the same sequence when constructed with the same seed value.
|
||
|
|
||
|
References:
|
||
|
- [He et al., 2015](https://arxiv.org/abs/1502.01852)
|
||
|
"""
|
||
|
|
||
|
def __init__(self, seed=None):
|
||
|
super().__init__(
|
||
|
scale=2.0, mode="fan_in", distribution="uniform", seed=seed
|
||
|
)
|
||
|
|
||
|
def get_config(self):
|
||
|
return {"seed": self.seed}
|
||
|
|
||
|
|
||
|
def _get_dtype(dtype):
|
||
|
if dtype is None:
|
||
|
dtype = backend.floatx()
|
||
|
return tf.as_dtype(dtype)
|
||
|
|
||
|
|
||
|
def _assert_float_dtype(dtype):
|
||
|
"""Validate and return floating point type based on `dtype`.
|
||
|
|
||
|
`dtype` must be a floating point type.
|
||
|
|
||
|
Args:
|
||
|
dtype: The data type to validate.
|
||
|
|
||
|
Returns:
|
||
|
Validated type.
|
||
|
|
||
|
Raises:
|
||
|
ValueError: if `dtype` is not a floating point type.
|
||
|
"""
|
||
|
dtype = tf.as_dtype(dtype)
|
||
|
if not dtype.is_floating:
|
||
|
raise ValueError(f"Expected floating point type, got {dtype}.")
|
||
|
return dtype
|
||
|
|
||
|
|
||
|
def _compute_fans(shape):
|
||
|
"""Computes the number of input and output units for a weight shape.
|
||
|
|
||
|
Args:
|
||
|
shape: Integer shape tuple or TF tensor shape.
|
||
|
|
||
|
Returns:
|
||
|
A tuple of integer scalars (fan_in, fan_out).
|
||
|
"""
|
||
|
if len(shape) < 1: # Just to avoid errors for constants.
|
||
|
fan_in = fan_out = 1
|
||
|
elif len(shape) == 1:
|
||
|
fan_in = fan_out = shape[0]
|
||
|
elif len(shape) == 2:
|
||
|
fan_in = shape[0]
|
||
|
fan_out = shape[1]
|
||
|
else:
|
||
|
# Assuming convolution kernels (2D, 3D, or more).
|
||
|
# kernel shape: (..., input_depth, depth)
|
||
|
receptive_field_size = 1
|
||
|
for dim in shape[:-2]:
|
||
|
receptive_field_size *= dim
|
||
|
fan_in = shape[-2] * receptive_field_size
|
||
|
fan_out = shape[-1] * receptive_field_size
|
||
|
return int(fan_in), int(fan_out)
|
||
|
|
||
|
|
||
|
def _validate_kwargs(cls_name, kwargs, support_partition=True):
|
||
|
invalid_kwargs = [k for k in kwargs if k not in _ALLOWED_INITIALIZER_KWARGS]
|
||
|
if invalid_kwargs:
|
||
|
raise TypeError(
|
||
|
f"Unknown keyword arguments: {invalid_kwargs}. Allowed "
|
||
|
f"keyword arguments: {_ALLOWED_INITIALIZER_KWARGS}."
|
||
|
)
|
||
|
if not support_partition and (
|
||
|
_PARTITION_SHAPE in kwargs or _PARTITION_OFFSET in kwargs
|
||
|
):
|
||
|
raise ValueError(
|
||
|
f"{cls_name} initializer doesn't support "
|
||
|
"partition-related arguments."
|
||
|
)
|
||
|
|
||
|
|
||
|
def _ensure_keras_seeded():
|
||
|
"""Make sure the keras.backend global seed generator is set.
|
||
|
|
||
|
This is important for DTensor use case to ensure that each client are
|
||
|
initialized with same seed for tf.random.Generator, so that the value
|
||
|
created are in sync among all the clients.
|
||
|
"""
|
||
|
if not getattr(backend._SEED_GENERATOR, "generator", None):
|
||
|
raise ValueError(
|
||
|
"When using DTensor APIs, you need to set the global seed "
|
||
|
"before using any Keras initializers. Please make sure "
|
||
|
"to call `tf.keras.utils.set_random_seed()` in your code."
|
||
|
)
|