Table Of Contents
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.