niftynet.engine.application_initializer module

Loading modules from a string representing the class name or a short name that matches the dictionary item defined in this module

all classes and docs are taken from https://github.com/tensorflow/tensorflow/blob/r1.3/tensorflow/python/ops/init_ops.py

class Constant[source]

Bases: object

initialize with a constant value

static get_instance(args)[source]

create an instance of the initializer

class Zeros[source]

Bases: object

initialize with zeros

static get_instance(args)[source]

create an instance of the initializer

class Ones[source]

Bases: object

initialize with ones

static get_instance(args)[source]

create an instance of the initializer

class UniformUnitScaling[source]

Bases: object

static get_instance(args)[source]

create an instance of the initializer

class Orthogonal[source]

Bases: object

static get_instance(args)[source]

create an instance of the initializer

class VarianceScaling[source]

Bases: object

static get_instance(args)[source]

create an instance of the initializer

class GlorotNormal[source]

Bases: object

static get_instance(args)[source]

create an instance of the initializer

class GlorotUniform[source]

Bases: object

static get_instance(args)[source]

create an instance of the initializer

class HeUniform[source]

Bases: object

He uniform variance scaling initializer.

It draws samples from a uniform distribution within [-limit, limit] where limit is sqrt(6 / fan_in) where fan_in is the number of input units in the weight tensor. # Arguments seed: A Python integer. Used to seed the random generator. # Returns An initializer. # References He et al., https://arxiv.org/abs/1502.01852

static get_instance(args)[source]

create an instance of the initializer

class HeNormal[source]

Bases: object

He normal initializer.

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. # Arguments seed: A Python integer. Used to seed the random generator. # Returns An initializer. # References He et al., https://arxiv.org/abs/1502.01852

static get_instance(args)[source]

create an instance of the initializer