public void GlorotUniformTest()
{
var init = new GlorotUniform();
var m = init.Initialize(new Matrix(2, 2));
Assert.IsTrue(Math.Abs(m.FrobeniusNorm()) > 0.01, init.Type().ToString() + " Initializer.");
}
/// <summary>
/// Turns positive integers (indexes) into dense vectors of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]. This layer can only be used as the first layer in a model.
/// </summary>
/// <param name="shape">The shape of the input.</param>
/// <param name="embeddingDim">The dim of the output space.</param>
/// <param name="initializers">The weight initializers for this layer.</param>
/// <returns></returns>
public static Function Embedding(Variable layer, int embeddingDim, Initializer initializers = null)
{
if (initializers == null)
{
initializers = new GlorotUniform();
}
var embeddingParameters = new Parameter(new int[] { embeddingDim, layer.Shape[0] }, DataType.Float, initializers.Get(), GlobalParameters.Device);
return(CNTKLib.Times(embeddingParameters, layer));
}
/// <summary>
/// Turns positive integers (indexes) into dense vectors of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]. This layer can only be used as the first layer in a model.
/// </summary>
/// <param name="shape">The shape of the input.</param>
/// <param name="embeddingDim">The dim of the output space.</param>
/// <param name="initializers">The weight initializers for this layer.</param>
/// <returns></returns>
public static Function Embedding(int shape, int embeddingDim, Initializer initializers = null)
{
if (initializers == null)
{
initializers = new GlorotUniform();
}
var input = CNTKLib.InputVariable(new int[] { shape }, true, DataType.Float);
var embeddingParameters = new Parameter(new int[] { embeddingDim, shape }, DataType.Float, initializers.Get(), GlobalParameters.Device);
return(CNTKLib.Times(embeddingParameters, input));
}
