1 | /**
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2 | * @license
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3 | * Copyright 2018 Google LLC
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4 | *
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5 | * Use of this source code is governed by an MIT-style
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6 | * license that can be found in the LICENSE file or at
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7 | * https://opensource.org/licenses/MIT.
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8 | * =============================================================================
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9 | */
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10 | // tslint:disable-next-line:max-line-length
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11 | import { Constant, GlorotNormal, GlorotUniform, HeNormal, HeUniform, Identity, LeCunNormal, LeCunUniform, Ones, Orthogonal, RandomNormal, RandomUniform, TruncatedNormal, VarianceScaling, Zeros } from './initializers';
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12 | /**
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13 | * Initializer that generates tensors initialized to 0.
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14 | *
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15 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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16 | */
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17 | export function zeros() {
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18 | return new Zeros();
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19 | }
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20 | /**
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21 | * Initializer that generates tensors initialized to 1.
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22 | *
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23 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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24 | */
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25 | export function ones() {
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26 | return new Ones();
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27 | }
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28 | /**
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29 | * Initializer that generates values initialized to some constant.
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30 | *
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31 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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32 | */
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33 | export function constant(args) {
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34 | return new Constant(args);
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35 | }
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36 | /**
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37 | * Initializer that generates random values initialized to a uniform
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38 | * distribution.
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39 | *
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40 | * Values will be distributed uniformly between the configured minval and
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41 | * maxval.
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42 | *
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43 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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44 | */
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45 | export function randomUniform(args) {
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46 | return new RandomUniform(args);
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47 | }
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48 | /**
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49 | * Initializer that generates random values initialized to a normal
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50 | * distribution.
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51 | *
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52 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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53 | */
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54 | export function randomNormal(args) {
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55 | return new RandomNormal(args);
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56 | }
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57 | /**
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58 | * Initializer that generates random values initialized to a truncated normal.
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59 | * distribution.
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60 | *
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61 | * These values are similar to values from a `RandomNormal` except that values
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62 | * more than two standard deviations from the mean are discarded and re-drawn.
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63 | * This is the recommended initializer for neural network weights and filters.
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64 | *
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65 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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66 | */
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67 | export function truncatedNormal(args) {
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68 | return new TruncatedNormal(args);
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69 | }
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70 | /**
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71 | * Initializer that generates the identity matrix.
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72 | * Only use for square 2D matrices.
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73 | *
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74 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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75 | */
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76 | export function identity(args) {
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77 | return new Identity(args);
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78 | }
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79 | /**
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80 | * Initializer capable of adapting its scale to the shape of weights.
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81 | * With distribution=NORMAL, samples are drawn from a truncated normal
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82 | * distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:
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83 | * - number of input units in the weight tensor, if mode = FAN_IN.
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84 | * - number of output units, if mode = FAN_OUT.
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85 | * - average of the numbers of input and output units, if mode = FAN_AVG.
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86 | * With distribution=UNIFORM,
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87 | * samples are drawn from a uniform distribution
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88 | * within [-limit, limit], with `limit = sqrt(3 * scale / n)`.
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89 | *
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90 | * @doc {heading: 'Initializers',namespace: 'initializers'}
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91 | */
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92 | export function varianceScaling(config) {
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93 | return new VarianceScaling(config);
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94 | }
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95 | /**
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96 | * Glorot uniform initializer, also called Xavier uniform initializer.
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97 | * It draws samples from a uniform distribution within [-limit, limit]
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98 | * where `limit` is `sqrt(6 / (fan_in + fan_out))`
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99 | * where `fan_in` is the number of input units in the weight tensor
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100 | * and `fan_out` is the number of output units in the weight tensor
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101 | *
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102 | * Reference:
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103 | * Glorot & Bengio, AISTATS 2010
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104 | * http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.
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105 | *
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106 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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107 | */
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108 | export function glorotUniform(args) {
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109 | return new GlorotUniform(args);
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110 | }
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111 | /**
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112 | * Glorot normal initializer, also called Xavier normal initializer.
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113 | * It draws samples from a truncated normal distribution centered on 0
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114 | * with `stddev = sqrt(2 / (fan_in + fan_out))`
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115 | * where `fan_in` is the number of input units in the weight tensor
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116 | * and `fan_out` is the number of output units in the weight tensor.
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117 | *
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118 | * Reference:
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119 | * Glorot & Bengio, AISTATS 2010
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120 | * http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf
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121 | *
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122 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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123 | */
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124 | export function glorotNormal(args) {
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125 | return new GlorotNormal(args);
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126 | }
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127 | /**
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128 | * He normal initializer.
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129 | *
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130 | * It draws samples from a truncated normal distribution centered on 0
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131 | * with `stddev = sqrt(2 / fanIn)`
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132 | * where `fanIn` is the number of input units in the weight tensor.
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133 | *
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134 | * Reference:
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135 | * He et al., http://arxiv.org/abs/1502.01852
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136 | *
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137 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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138 | */
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139 | export function heNormal(args) {
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140 | return new HeNormal(args);
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141 | }
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142 | /**
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143 | * He uniform initializer.
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144 | *
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145 | * It draws samples from a uniform distribution within [-limit, limit]
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146 | * where `limit` is `sqrt(6 / fan_in)`
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147 | * where `fanIn` is the number of input units in the weight tensor.
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148 | *
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149 | * Reference:
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150 | * He et al., http://arxiv.org/abs/1502.01852
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151 | *
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152 | * @doc {heading: 'Initializers',namespace: 'initializers'}
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153 | */
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154 | export function heUniform(args) {
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155 | return new HeUniform(args);
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156 | }
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157 | /**
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158 | * LeCun normal initializer.
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159 | *
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160 | * It draws samples from a truncated normal distribution centered on 0
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161 | * with `stddev = sqrt(1 / fanIn)`
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162 | * where `fanIn` is the number of input units in the weight tensor.
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163 | *
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164 | * References:
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165 | * [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
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166 | * [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)
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167 | *
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168 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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169 | */
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170 | export function leCunNormal(args) {
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171 | return new LeCunNormal(args);
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172 | }
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173 | /**
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174 | * LeCun uniform initializer.
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175 | *
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176 | * It draws samples from a uniform distribution in the interval
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177 | * `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,
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178 | * where `fanIn` is the number of input units in the weight tensor.
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179 | *
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180 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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181 | */
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182 | export function leCunUniform(args) {
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183 | return new LeCunUniform(args);
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184 | }
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185 | /**
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186 | * Initializer that generates a random orthogonal matrix.
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187 | *
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188 | * Reference:
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189 | * [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)
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190 | *
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191 | * @doc {heading: 'Initializers', namespace: 'initializers'}
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192 | */
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193 | export function orthogonal(args) {
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194 | return new Orthogonal(args);
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195 | }
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number of input units in the weight tensor, if mode = FAN_IN.\n *   - number of output units, if mode = FAN_OUT.\n *   - average of the numbers of input and output units, if mode = FAN_AVG.\n * With distribution=UNIFORM,\n * samples are drawn from a uniform distribution\n * within [-limit, limit], with `limit = sqrt(3 * scale / n)`.\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function varianceScaling(config: VarianceScalingArgs): Initializer {\n  return new VarianceScaling(config);\n}\n\n/**\n * Glorot uniform initializer, also called Xavier uniform initializer.\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotUniform(args: SeedOnlyInitializerArgs): Initializer {\n  return new GlorotUniform(args);\n}\n\n/**\n * Glorot normal initializer, also called Xavier normal initializer.\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / (fan_in + fan_out))`\n * where `fan_in` is the number of input units in the weight tensor\n * and `fan_out` is the number of output units in the weight tensor.\n *\n * Reference:\n *   Glorot & Bengio, AISTATS 2010\n *       http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function glorotNormal(args: SeedOnlyInitializerArgs): Initializer {\n  return new GlorotNormal(args);\n}\n\n/**\n * He normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(2 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function heNormal(args: SeedOnlyInitializerArgs): Initializer {\n  return new HeNormal(args);\n}\n\n/**\n * He uniform initializer.\n *\n * It draws samples from a uniform distribution within [-limit, limit]\n * where `limit` is `sqrt(6 / fan_in)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * Reference:\n *     He et al., http://arxiv.org/abs/1502.01852\n *\n * @doc {heading: 'Initializers',namespace: 'initializers'}\n */\nexport function heUniform(args: SeedOnlyInitializerArgs): Initializer {\n  return new HeUniform(args);\n}\n\n/**\n * LeCun normal initializer.\n *\n * It draws samples from a truncated normal distribution centered on 0\n * with `stddev = sqrt(1 / fanIn)`\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * References:\n *   [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)\n *   [Efficient Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunNormal(args: SeedOnlyInitializerArgs): Initializer {\n  return new LeCunNormal(args);\n}\n\n/**\n * LeCun uniform initializer.\n *\n * It draws samples from a uniform distribution in the interval\n * `[-limit, limit]` with `limit = sqrt(3 / fanIn)`,\n * where `fanIn` is the number of input units in the weight tensor.\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function leCunUniform(args: SeedOnlyInitializerArgs): Initializer {\n  return new LeCunUniform(args);\n}\n\n/**\n * Initializer that generates a random orthogonal matrix.\n *\n * Reference:\n * [Saxe et al., http://arxiv.org/abs/1312.6120](http://arxiv.org/abs/1312.6120)\n *\n * @doc {heading: 'Initializers', namespace: 'initializers'}\n */\nexport function orthogonal(args: OrthogonalArgs): Initializer {\n  return new Orthogonal(args);\n}\n"]} |
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