Table Of Contents

mxnet.initializer

Weight initializer.

Initialization methods

Bilinear()	Initialize weight for upsampling layers.
Constant(value)	Initializes the weights to a given value.
FusedRNN(init, num_hidden, num_layers, mode)	Initialize parameters for fused rnn layers.
InitDesc	Descriptor for the initialization pattern.
Initializer(**kwargs)	The base class of an initializer.
LSTMBias([forget_bias])	Initialize all biases of an LSTMCell to 0.0 except for the forget gate whose bias is set to custom value.
Load(param[, default_init, verbose])	Initializes variables by loading data from file or dict.
MSRAPrelu([factor_type, slope])	Initialize the weight according to a MSRA paper.
Mixed(patterns, initializers)	Initialize parameters using multiple initializers.
Normal([sigma])	Initializes weights with random values sampled from a normal distribution with a mean of zero and standard deviation of sigma.
One()	Initializes weights to one.
Orthogonal([scale, rand_type])	Initialize weight as orthogonal matrix.
Uniform([scale])	Initializes weights with random values uniformly sampled from a given range.
Xavier([rnd_type, factor_type, magnitude])	Returns an initializer performing “Xavier” initialization for weights.
Zero()	Initializes weights to zero.

Helper functions

register(klass)

Registers a custom initializer.

Table Of Contents

• mxnet.initializer
  • Initialization methods
  • Helper functions

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