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| <p><a id='Initializer-1'></a></p> |
| <h1 id="initializer">Initializer</h1> |
| <p><a id='MXNet.mx.AbstractInitializer' href='#MXNet.mx.AbstractInitializer'>#</a> |
| <strong><code>MXNet.mx.AbstractInitializer</code></strong> — <em>Type</em>.</p> |
| <pre><code class="julia">AbstractInitializer |
| </code></pre> |
| |
| <p>The abstract base class for all initializers.</p> |
| <p>To define a new initializer, it is enough to derive a new type, and implement one or more of the following methods:</p> |
| <pre><code>_init_weight(self :: AbstractInitializer, name :: Base.Symbol, array :: NDArray) |
| _init_bias(self :: AbstractInitializer, name :: Base.Symbol, array :: NDArray) |
| _init_gamma(self :: AbstractInitializer, name :: Base.Symbol, array :: NDArray) |
| _init_beta(self :: AbstractInitializer, name :: Base.Symbol, array :: NDArray) |
| </code></pre> |
| |
| <p>Or, if full behavior customization is needed, override the following function</p> |
| <pre><code>init(self :: AbstractInitializer, name :: Base.Symbol, array :: NDArray) |
| </code></pre> |
| |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/initializer.jl#L18-L34' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx.NormalInitializer' href='#MXNet.mx.NormalInitializer'>#</a> |
| <strong><code>MXNet.mx.NormalInitializer</code></strong> — <em>Type</em>.</p> |
| <pre><code class="julia">NormalInitializer |
| </code></pre> |
| |
| <p>Initialize weights according to a univariate Gaussian distribution.</p> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/initializer.jl#L127-L131' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx.NormalInitializer-Tuple{}' href='#MXNet.mx.NormalInitializer-Tuple{}'>#</a> |
| <strong><code>MXNet.mx.NormalInitializer</code></strong> — <em>Method</em>.</p> |
| <pre><code class="julia">NormalInitializer(; mu=0, sigma=0.01) |
| </code></pre> |
| |
| <p>Construct a <code>NormalInitializer</code> with mean <code>mu</code> and variance <code>sigma</code>.</p> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/initializer.jl#L136-L140' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx.UniformInitializer' href='#MXNet.mx.UniformInitializer'>#</a> |
| <strong><code>MXNet.mx.UniformInitializer</code></strong> — <em>Type</em>.</p> |
| <pre><code class="julia">UniformInitializer |
| </code></pre> |
| |
| <p>Initialize weights according to a uniform distribution within the provided scale.</p> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/initializer.jl#L109-L113' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx.UniformInitializer-Tuple{}' href='#MXNet.mx.UniformInitializer-Tuple{}'>#</a> |
| <strong><code>MXNet.mx.UniformInitializer</code></strong> — <em>Method</em>.</p> |
| <pre><code class="julia">UniformInitializer(scale=0.07) |
| </code></pre> |
| |
| <p>Construct a <code>UniformInitializer</code> with the specified scale.</p> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/initializer.jl#L117-L121' class='documenter-source'>source</a><br></p> |
| <p><a id='MXNet.mx.XavierInitializer' href='#MXNet.mx.XavierInitializer'>#</a> |
| <strong><code>MXNet.mx.XavierInitializer</code></strong> — <em>Type</em>.</p> |
| <pre><code class="julia">XavierInitializer |
| </code></pre> |
| |
| <p>The initializer documented in the paper [Bengio and Glorot 2010]: <em>Understanding the difficulty of training deep feedforward neuralnetworks</em>.</p> |
| <p>There are several different version of the XavierInitializer used in the wild. The general idea is that the variance of the initialization distribution is controlled by the dimensionality of the input and output. As a distribution one can either choose a normal distribution with μ = 0 and σ² or a uniform distribution from -σ to σ.</p> |
| <p>Several different ways of calculating the variance are given in the literature or are used by various libraries.</p> |
| <ul> |
| <li>[Bengio and Glorot 2010]: <code>mx.XavierInitializer(distribution = mx.xv_uniform, regularization = mx.xv_avg, magnitude = 1)</code></li> |
| <li>[K. He, X. Zhang, S. Ren, and J. Sun 2015]: <code>mx.XavierInitializer(distribution = mx.xv_gaussian, regularization = mx.xv_in, magnitude = 2)</code></li> |
| <li>caffe*avg: <code>mx.XavierInitializer(distribution = mx.xv*uniform, regularization = mx.xv_avg, magnitude = 3)</code></li> |
| </ul> |
| <p><a target='_blank' href='https://github.com/apache/mxnet/blob/26a5ad1f39784a60d1564f6f740e5c7bd971cd65/julia/src/initializer.jl#L150-L167' class='documenter-source'>source</a><br></p> |
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