# 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.