tf.keras.initializers.GlorotNormal
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The Glorot normal initializer, also called Xavier normal initializer.
Inherits From: VarianceScaling
tf.keras.initializers.GlorotNormal(
seed=None
)
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(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:
def make_variables(k, initializer):
return (tf.Variable(initializer(shape=[k, k], dtype=tf.float32)),
tf.Variable(initializer(shape=[k, k, k], dtype=tf.float32)))
v1, v2 = make_variables(3, tf.initializers.GlorotNormal())
v1
<tf.Variable ... shape=(3, 3) ...
v2
<tf.Variable ... shape=(3, 3, 3) ...
make_variables(4, tf.initializers.RandomNormal())
(<tf.Variable ... shape=(4, 4) dtype=float32...
<tf.Variable ... shape=(4, 4, 4) dtype=float32...
Args |
|---|
seed
|
A Python integer. Used to create random seeds. See
tf.random.set_seed for behavior.
|
References:
Glorot et al., 2010
(pdf)
Methods
from_config
View source
@classmethod
from_config(
config
)
Instantiates an initializer from a configuration dictionary.
Example:
initializer = RandomUniform(-1, 1)
config = initializer.get_config()
initializer = RandomUniform.from_config(config)
| Args |
|---|
config
|
A Python dictionary.
It will typically be the output of get_config.
|
| Returns |
|---|
|
An Initializer instance.
|
get_config
View source
get_config()
Returns the configuration of the initializer as a JSON-serializable dict.
| Returns |
|---|
|
A JSON-serializable Python dict.
|
__call__
View source
__call__(
shape, dtype=tf.dtypes.float32
)
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.
|
| Raises |
|---|
ValueError
|
If the dtype is not floating point
|
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Last updated 2020-10-01 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[],null,["# tf.keras.initializers.GlorotNormal\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.2.0/tensorflow/python/ops/init_ops_v2.py#L760-L803) |\n\nThe Glorot normal initializer, also called Xavier normal initializer.\n\nInherits From: [`VarianceScaling`](../../../tf/keras/initializers/VarianceScaling)\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.initializers.GlorotNormal`](/api_docs/python/tf/keras/initializers/GlorotNormal), [`tf.initializers.glorot_normal`](/api_docs/python/tf/keras/initializers/GlorotNormal), [`tf.keras.initializers.glorot_normal`](/api_docs/python/tf/keras/initializers/GlorotNormal)\n\n\u003cbr /\u003e\n\n tf.keras.initializers.GlorotNormal(\n seed=None\n )\n\nInitializers allow you to pre-specify an initialization strategy, encoded in\nthe Initializer object, without knowing the shape and dtype of the variable\nbeing initialized.\n\nDraws samples from a truncated normal distribution centered on 0 with `stddev\n= sqrt(2 / (fan_in + fan_out))` where `fan_in` is the number of input units in\nthe weight tensor and `fan_out` is the number of output units in the weight\ntensor.\n\n#### Examples:\n\n def make_variables(k, initializer):\n return (tf.Variable(initializer(shape=[k, k], dtype=tf.float32)),\n tf.Variable(initializer(shape=[k, k, k], dtype=tf.float32)))\n v1, v2 = make_variables(3, tf.initializers.GlorotNormal())\n v1\n \u003ctf.Variable ... shape=(3, 3) ...\n v2\n \u003ctf.Variable ... shape=(3, 3, 3) ...\n make_variables(4, tf.initializers.RandomNormal())\n (\u003ctf.Variable ... shape=(4, 4) dtype=float32...\n \u003ctf.Variable ... shape=(4, 4, 4) dtype=float32...\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------|----------------------------------------------------------------------------------------------------------------------|\n| `seed` | A Python integer. Used to create random seeds. See [`tf.random.set_seed`](../../../tf/random/set_seed) for behavior. |\n\n\u003cbr /\u003e\n\n#### References:\n\n[Glorot et al., 2010](http://proceedings.mlr.press/v9/glorot10a.html)\n([pdf](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf))\n\nMethods\n-------\n\n### `from_config`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v2.2.0/tensorflow/python/ops/init_ops_v2.py#L69-L89) \n\n @classmethod\n from_config(\n config\n )\n\nInstantiates an initializer from a configuration dictionary.\n\n#### Example:\n\n initializer = RandomUniform(-1, 1)\n config = initializer.get_config()\n initializer = RandomUniform.from_config(config)\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|----------|-----------------------------------------------------------------------|\n| `config` | A Python dictionary. It will typically be the output of `get_config`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| An Initializer instance. ||\n\n\u003cbr /\u003e\n\n### `get_config`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v2.2.0/tensorflow/python/ops/init_ops_v2.py#L802-L803) \n\n get_config()\n\nReturns the configuration of the initializer as a JSON-serializable dict.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ||\n|---|---|\n| A JSON-serializable Python dict. ||\n\n\u003cbr /\u003e\n\n### `__call__`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v2.2.0/tensorflow/python/ops/init_ops_v2.py#L525-L558) \n\n __call__(\n shape, dtype=tf.dtypes.float32\n )\n\nReturns a tensor object initialized as specified by the initializer.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|---------|------------------------------------------------------------------------|\n| `shape` | Shape of the tensor. |\n| `dtype` | Optional dtype of the tensor. Only floating point types are supported. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ||\n|--------------|------------------------------------|\n| `ValueError` | If the dtype is not floating point |\n\n\u003cbr /\u003e"]]
View source on GitHub