From a6d96c1f6f4cbf0a66b6160bdd8046d93dbb36d2 Mon Sep 17 00:00:00 2001 From: zhiqwang Date: Fri, 5 Mar 2021 04:20:41 -0500 Subject: [PATCH] Update the formats and notes in initializer --- mindspore/common/initializer.py | 32 +++++++++++++++++++++----------- 1 file changed, 21 insertions(+), 11 deletions(-) diff --git a/mindspore/common/initializer.py b/mindspore/common/initializer.py index e954de69ff..f78147b1fb 100644 --- a/mindspore/common/initializer.py +++ b/mindspore/common/initializer.py @@ -227,15 +227,16 @@ def _calculate_in_and_out(arr): class XavierUniform(Initializer): r""" Initialize the array with xavier uniform algorithm, and from a uniform distribution collect samples within - U[-boundary, boundary] The boundary is defined as : + U[-boundary, boundary] The boundary is defined as: - where :math:`boundary = gain * \sqrt{\frac{6}{n_{in} + n_{out}}}`. + .. math:: + boundary = gain * \sqrt{\frac{6}{n_{in} + n_{out}}} - where :math:`n_{in}` is the number of input units in the weight tensor. - where :math:`n_{out}` is the number of output units in the weight tensor. Args: - gain (Array): The array to be assigned. Default: 1. + gain (float): An optional scaling factor. Default: 1. Returns: Array, assigned array. @@ -257,14 +258,19 @@ class XavierUniform(Initializer): class HeUniform(Initializer): r""" Initialize the array with He kaiming uniform algorithm, and from a uniform distribution collect samples within - U[-boundary, boundary] The boundary is defined as : + U[-boundary, boundary] The boundary is defined as: - where :math:`boundary = \sqrt{\frac{6}{(1 + a^2) \times \text{fan_in}}}`. + .. math:: + boundary = \sqrt{\frac{6}{(1 + a^2) \times \text{fan_in}}} Args: - negative_slope (int, float, bool): Default: 0, used when nonlinearity is 'leaky_relu'. - mode (str): Default: fan_in. - nonlinearity (str): Default: leaky_relu. + negative_slope (int, float, bool): The negativa slope of the rectifier used after this layer + (only used when `nonlinearity` is 'leaky_relu'). Default: 0. + mode (str): Either 'fan_in' or 'fan_out'. Choosing 'fan_in' preserves the magnitude of the + variance of the weights in the forward pass. Choosing 'fan_out' preserves the magnitudes + in the backwards pass. Default: fan_in. + nonlinearity (str): The non-linear function, recommended to use only with 'relu' or 'leaky_relu'. + Default: leaky_relu. Returns: Array, assigned array. @@ -292,9 +298,13 @@ class HeNormal(Initializer): N(0, sigma). Args: - negative_slope (int, float, bool): Default: 0, used when nonlinearity is 'leaky_relu'. - mode (str): Default: fan_in. - nonlinearity (str): Default: leaky_relu. + negative_slope (int, float, bool): The negativa slope of the rectifier used after this layer + (only used when `nonlinearity` is 'leaky_relu'). Default: 0. + mode (str): Either 'fan_in' or 'fan_out'. Choosing 'fan_in' preserves the magnitude of the + variance of the weights in the forward pass. Choosing 'fan_out' preserves the magnitudes + in the backwards pass. Default: fan_in. + nonlinearity (str): The non-linear function, recommended to use only with 'relu' or 'leaky_relu'. + Default: leaky_relu. Returns: Array, assigned array.