o MeI@sdZddlmmZddlmZejjjZ ejjj Z ejjj Z ejjjZejjjZejjjjZejjjZejjjZeddgdde edd gdde ed d gdde ed gddeed dgddeeddgddeedgddeedgddeegddGdddejjjZegddGdddejjjZeddgdGdddejjjZedgdGdd d ejjjZed!gdGd"d#d#ejjjZ ed$gdGd%d&d&ejjjZ!ed'gdGd(d)d)ejjjZ"dS)*zKeras initializers for TF 1.N) keras_exportzkeras.initializers.Zeroszkeras.initializers.zerosT)v1Zallow_multiple_exportszkeras.initializers.Oneszkeras.initializers.oneszkeras.initializers.Constantzkeras.initializers.constantz"keras.initializers.VarianceScalingzkeras.initializers.Orthogonalzkeras.initializers.orthogonalzkeras.initializers.Identityzkeras.initializers.identityz!keras.initializers.glorot_uniformz keras.initializers.glorot_normal)zkeras.initializers.RandomNormalz keras.initializers.random_normalzkeras.initializers.normal)rc,eZdZdZdddejffdd ZZS) RandomNormalaInitializer that generates a normal distribution. 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. seed: A Python integer. Used to create random seeds. See `tf.compat.v1.set_random_seed` for behavior. dtype: Default data type, used if no `dtype` argument is provided when calling the initializer. Only floating point types are supported. @compatibility(TF2) Although it is a legacy compat.v1 api, `tf.compat.v1.keras.initializers.RandomNormal` is compatible with eager execution and `tf.function`. To switch to native TF2, switch to using `tf.keras.initializers.RandomNormal` (not from `compat.v1`) and if you need to change the default dtype use `tf.keras.backend.set_floatx(float_dtype)` or pass the dtype when calling the initializer, rather than passing it when constructing the initializer. Random seed behavior: Also be aware that if you pass a seed to the TF2 initializer API it will reuse that same seed for every single initialization (unlike the TF1 initializer) #### Structural Mapping to Native TF2 Before: ```python initializer = tf.compat.v1.keras.initializers.RandomNormal( mean=mean, stddev=stddev, seed=seed, dtype=dtype) weight_one = tf.Variable(initializer(shape_one)) weight_two = tf.Variable(initializer(shape_two)) ``` After: ```python initializer = tf.keras.initializers.RandomNormal( mean=mean, # seed=seed, # Setting a seed in the native TF2 API # causes it to produce the same initializations # across multiple calls of the same initializer. stddev=stddev) weight_one = tf.Variable(initializer(shape_one, dtype=dtype)) weight_two = tf.Variable(initializer(shape_two, dtype=dtype)) ``` #### How to Map Arguments | TF1 Arg Name | TF2 Arg Name | Note | | :---------------- | :-------------- | :------------------------- | | `mean` | `mean` | No change to defaults | | `stddev` | `stddev` | No change to defaults | | `seed` | `seed` | Different random number generation | : : : semantics (to change in a : : : : future version). If set, the TF2 version : : : : will use stateless random number : : : : generation which will produce the exact : : : : same initialization even across multiple : : : : calls of the initializer instance. the : : : : `compat.v1` version will generate new : : : : initializations each time. Do not set : : : : a seed if you need different : : : : initializations each time. Instead : : : : either set a global tf seed with : : : : `tf.random.set_seed` if you need : : : : determinism, or initialize each weight: : : : with a separate initializer instance : : : : and a different seed. : | `dtype` | `dtype` | The TF2 native api only takes it | : : : as a `__call__` arg, not a constructor arg. : | `partition_info` | - | (`__call__` arg in TF1) Not supported | #### Example of fixed-seed behavior differences `compat.v1` Fixed seed behavior: >>> initializer = tf.compat.v1.keras.initializers.RandomNormal(seed=10) >>> a = initializer(shape=(2, 2)) >>> b = initializer(shape=(2, 2)) >>> tf.reduce_sum(a - b) == 0 After: >>> initializer = tf.keras.initializers.RandomNormal(seed=10) >>> a = initializer(shape=(2, 2)) >>> b = initializer(shape=(2, 2)) >>> tf.reduce_sum(a - b) == 0 @end_compatibility 皙?Nctj||||ddS)Nmeanstddevseeddtypesuper__init__selfr r r r  __class__RD:\Projects\ConvertPro\env\Lib\site-packages\keras/initializers/initializers_v1.pyrzRandomNormal.__init____name__ __module__ __qualname____doc__tfZfloat32r __classcell__rrrrr?s ir)z keras.initializers.RandomUniformz!keras.initializers.random_uniformzkeras.initializers.uniformcr) RandomUniforma6Initializer that generates tensors with a uniform distribution. Args: minval: A python scalar or a scalar tensor. Lower bound of the range of random values to generate. maxval: A python scalar or a scalar tensor. Upper bound of the range of random values to generate. Defaults to 1 for float types. seed: A Python integer. Used to create random seeds. See `tf.compat.v1.set_random_seed` for behavior. dtype: Default data type, used if no `dtype` argument is provided when calling the initializer. @compatibility(TF2) Although it is a legacy `compat.v1` api, `tf.compat.v1.keras.initializers.RandomUniform` is compatible with eager execution and `tf.function`. To switch to native TF2, switch to using `tf.keras.initializers.RandomUniform` (not from `compat.v1`) and if you need to change the default dtype use `tf.keras.backend.set_floatx(float_dtype)` or pass the dtype when calling the initializer, rather than passing it when constructing the initializer. Random seed behavior: Also be aware that if you pass a seed to the TF2 initializer API it will reuse that same seed for every single initialization (unlike the TF1 initializer) #### Structural Mapping to Native TF2 Before: ```python initializer = tf.compat.v1.keras.initializers.RandomUniform( minval=minval, maxval=maxval, seed=seed, dtype=dtype) weight_one = tf.Variable(initializer(shape_one)) weight_two = tf.Variable(initializer(shape_two)) ``` After: ```python initializer = tf.keras.initializers.RandomUniform( minval=minval, maxval=maxval, # seed=seed, # Setting a seed in the native TF2 API # causes it to produce the same initializations # across multiple calls of the same initializer. ) weight_one = tf.Variable(initializer(shape_one, dtype=dtype)) weight_two = tf.Variable(initializer(shape_two, dtype=dtype)) ``` #### How to Map Arguments | TF1 Arg Name | TF2 Arg Name | Note | | :---------------- | :-------------- | :------------------------- | | `minval` | `minval` | No change to defaults | | `maxval` | `maxval` | No change to defaults | | `seed` | `seed` | Different random number generation | : : : semantics (to change in a : : : : future version). If set, the TF2 version : : : : will use stateless random number : : : : generation which will produce the exact : : : : same initialization even across multiple : : : : calls of the initializer instance. the : : : : `compat.v1` version will generate new : : : : initializations each time. Do not set : : : : a seed if you need different : : : : initializations each time. Instead : : : : either set a global tf seed with : : : `tf.random.set_seed` if you need : : : : determinism, or initialize each weight : : : : with a separate initializer instance : : : : and a different seed. : | `dtype` | `dtype` | The TF2 native api only takes it | : : : as a `__call__` arg, not a constructor arg. : | `partition_info` | - | (`__call__` arg in TF1) Not supported | #### Example of fixed-seed behavior differences `compat.v1` Fixed seed behavior: >>> initializer = tf.compat.v1.keras.initializers.RandomUniform(seed=10) >>> a = initializer(shape=(2, 2)) >>> b = initializer(shape=(2, 2)) >>> tf.reduce_sum(a - b) == 0 After: >>> initializer = tf.keras.initializers.RandomUniform(seed=10) >>> a = initializer(shape=(2, 2)) >>> b = initializer(shape=(2, 2)) >>> tf.reduce_sum(a - b) == 0 @end_compatibility grNcr)N)minvalmaxvalr r r)rr r!r r rrrr(rzRandomUniform.__init__rrrrrrs lrz"keras.initializers.TruncatedNormalz#keras.initializers.truncated_normalcr)TruncatedNormalaInitializer that generates a truncated normal distribution. These values are similar to values from a `random_normal_initializer` except that values more than two standard deviations from the mean are discarded and re-drawn. This is the recommended initializer for neural network weights and filters. 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. seed: A Python integer. Used to create random seeds. See `tf.compat.v1.set_random_seed` for behavior. dtype: Default data type, used if no `dtype` argument is provided when calling the initializer. Only floating point types are supported. @compatibility(TF2) Although it is a legacy compat.v1 api, `tf.compat.v1.keras.initializers.TruncatedNormal` is compatible with eager execution and `tf.function`. To switch to native TF2, switch to using `tf.keras.initializers.TruncatedNormal` (not from `compat.v1`) and if you need to change the default dtype use `tf.keras.backend.set_floatx(float_dtype)` or pass the dtype when calling the initializer, rather than passing it when constructing the initializer. Random seed behavior: Also be aware that if you pass a seed to the TF2 initializer API it will reuse that same seed for every single initialization (unlike the TF1 initializer) #### Structural Mapping to Native TF2 Before: ```python initializer = tf.compat.v1.keras.initializers.TruncatedNormal( mean=mean, stddev=stddev, seed=seed, dtype=dtype) weight_one = tf.Variable(initializer(shape_one)) weight_two = tf.Variable(initializer(shape_two)) ``` After: ```python initializer = tf.keras.initializers.TruncatedNormal( mean=mean, # seed=seed, # Setting a seed in the native TF2 API # causes it to produce the same initializations # across multiple calls of the same initializer. stddev=stddev) weight_one = tf.Variable(initializer(shape_one, dtype=dtype)) weight_two = tf.Variable(initializer(shape_two, dtype=dtype)) ``` #### How to Map Arguments | TF1 Arg Name | TF2 Arg Name | Note | | :---------------- | :-------------- | :------------------------- | | `mean` | `mean` | No change to defaults | | `stddev` | `stddev` | No change to defaults | | `seed` | `seed` | Different random number generation | : : : semantics (to change in a : : : : future version). If set, the TF2 version : : : : will use stateless random number : : : : generation which will produce the exact : : : : same initialization even across multiple : : : : calls of the initializer instance. the : : : : `compat.v1` version will generate new : : : : initializations each time. Do not set : : : : a seed if you need different : : : : initializations each time. Instead : : : : either set a global tf seed with : : : `tf.random.set_seed` if you need : : : : determinism, or initialize each weight : : : : with a separate initializer instance : : : : and a different seed. : | `dtype` | `dtype` | The TF2 native api only takes it | : : : as a `__call__` arg, not a constructor arg. : | `partition_info` | - | (`__call__` arg in TF1) Not supported | #### Example of fixed-seed behavior differences `compat.v1` Fixed seed behavior: >>> initializer = tf.compat.v1.keras.initializers.TruncatedNormal(seed=10) >>> a = initializer(shape=(2, 2)) >>> b = initializer(shape=(2, 2)) >>> tf.reduce_sum(a - b) == 0 After: >>> initializer = tf.keras.initializers.TruncatedNormal(seed=10) >>> a = initializer(shape=(2, 2)) >>> b = initializer(shape=(2, 2)) >>> tf.reduce_sum(a - b) == 0 @end_compatibility rrNcstj||||ddS)aMInitializer that generates a truncated normal distribution. 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. seed: A Python integer. Used to create random seeds. See `tf.compat.v1.set_random_seed` for behavior. dtype: Default data type, used if no `dtype` argument is provided when calling the initializer. 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