public void EmbedIDRandomTest()
{
Python.Initialize();
Chainer.Initialize();
int inputCount = Mother.Dice.Next(2, 30);
int outputCount = Mother.Dice.Next(1, 30);
int batchCount = Mother.Dice.Next(1, 5);
int[,] input = (int[, ])Enumerable.Repeat(0, batchCount * inputCount).ToNdArray(batchCount, inputCount);
input[0, 0] = 1;
Real[,,] dummyGy = Initializer.GetRandomValues <Real[, , ]>(batchCount, inputCount, outputCount);
Real[,] w = Initializer.GetRandomValues <Real[, ]>(inputCount, outputCount);
//Chainer
NChainer.EmbedID <Real> cEmbedId = new NChainer.EmbedID <Real>(inputCount, outputCount, w);
Variable <int> cX = new Variable <int>(input);
Variable <Real> cY = cEmbedId.Forward(cX);
cY.Grad = dummyGy;
cY.Backward();
//KelpNet
EmbedID <Real> embedId = new EmbedID <Real>(inputCount, outputCount, w);
NdArray <Real> x = new NdArray <Real>(input, asBatch: true);
NdArray <Real> y = embedId.Forward(x)[0];
y.Grad = dummyGy.Flatten();
y.Backward();
Real[] cYdata = ((Real[, , ])cY.Data).Flatten();
Real[] cWgrad = ((Real[, ])cEmbedId.W.Grad).Flatten();
//許容範囲を算出
Real delta = 0.00001f;
//y
Assert.AreEqual(cYdata.Length, y.Data.Length);
for (int i = 0; i < y.Data.Length; i++)
{
Assert.AreEqual(cYdata[i], y.Data[i], delta);
}
//W.grad
Assert.AreEqual(cWgrad.Length, embedId.Weight.Grad.Length);
for (int i = 0; i < embedId.Weight.Grad.Length; i++)
{
Assert.AreEqual(cWgrad[i], embedId.Weight.Grad[i], delta);
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
/// <summary> Sets the parameters. </summary>
///
/// <param name="func"> The function. </param>
/// <param name="modelData"> Information describing the model. </param>
////////////////////////////////////////////////////////////////////////////////////////////////////
static void SetParams(Function func, NpzDictionary modelData)
{
if (func is Linear)
{
Linear linear = (Linear)func;
Array.Copy(Real.GetArray(modelData[func.Name + "/W.npy"]), linear.Weight.Data, linear.Weight.Data.Length);
if (!linear.NoBias)
{
Array.Copy(Real.GetArray(modelData[func.Name + "/b.npy"]), linear.Bias.Data, linear.Bias.Data.Length);
}
}
else if (func is Convolution2D)
{
Convolution2D conv2D = (Convolution2D)func;
Array.Copy(Real.GetArray(modelData[func.Name + "/W.npy"]), conv2D.Weight.Data, conv2D.Weight.Data.Length);
if (!conv2D.NoBias)
{
Array.Copy(Real.GetArray(modelData[func.Name + "/b.npy"]), conv2D.Bias.Data, conv2D.Bias.Data.Length);
}
}
else if (func is Deconvolution2D)
{
Deconvolution2D deconv2D = (Deconvolution2D)func;
Array.Copy(Real.GetArray(modelData[func.Name + "/W.npy"]), deconv2D.Weight.Data, deconv2D.Weight.Data.Length);
if (!deconv2D.NoBias)
{
Array.Copy(Real.GetArray(modelData[func.Name + "/b.npy"]), deconv2D.Bias.Data, deconv2D.Bias.Data.Length);
}
}
else if (func is EmbedID)
{
EmbedID embed = (EmbedID)func;
Array.Copy(Real.GetArray(modelData[func.Name + "/W.npy"]), embed.Weight.Data, embed.Weight.Data.Length);
}
else if (func is BatchNormalization)
{
BatchNormalization bn = (BatchNormalization)func;
Array.Copy(Real.GetArray(modelData[func.Name + "/beta.npy"]), bn.Beta.Data, bn.Beta.Data.Length);
Array.Copy(Real.GetArray(modelData[func.Name + "/gamma.npy"]), bn.Gamma.Data, bn.Gamma.Data.Length);
if (bn.IsTrain)
{
if (modelData.ContainsKey(func.Name + "/avg_mean.npy"))
{
Array.Copy(Real.GetArray(modelData[func.Name + "/avg_mean.npy"]), bn.AvgMean.Data, bn.AvgMean.Data.Length);
}
if (modelData.ContainsKey(func.Name + "/avg_var.npy"))
{
Array.Copy(Real.GetArray(modelData[func.Name + "/avg_var.npy"]), bn.AvgVar.Data, bn.AvgVar.Data.Length);
}
}
}
else if (func is MultiplyScale)
{
MultiplyScale scale = (MultiplyScale)func;
Array.Copy(Real.GetArray(modelData[func.Name + "/W.npy"]), scale.Weight.Data, scale.Weight.Data.Length);
if (scale.BiasTerm)
{
Array.Copy(Real.GetArray(modelData[func.Name + "/bias/b.npy"]), scale.Bias.Data, scale.Bias.Data.Length);
}
}
}
static void SetParams <T>(Function <T> func, NpzDictionary modelData) where T : unmanaged, IComparable <T>
{
if (func is Linear <T> )
{
Linear <T> linear = (Linear <T>)func;
linear.Weight.Data = modelData[func.Name + "/W.npy"].FlattenEx <T>();
if (linear.Bias != null)
{
linear.Bias.Data = modelData[func.Name + "/b.npy"].FlattenEx <T>();
}
}
else if (func is Convolution2D <T> )
{
Convolution2D <T> conv2D = (Convolution2D <T>)func;
conv2D.Weight.Data = modelData[func.Name + "/W.npy"].FlattenEx <T>();
if (conv2D.Bias != null)
{
conv2D.Bias.Data = modelData[func.Name + "/b.npy"].FlattenEx <T>();
}
}
else if (func is Deconvolution2D <T> )
{
Deconvolution2D <T> deconv2D = (Deconvolution2D <T>)func;
deconv2D.Weight.Data = modelData[func.Name + "/W.npy"].FlattenEx <T>();
if (deconv2D.Bias != null)
{
deconv2D.Bias.Data = modelData[func.Name + "/b.npy"].FlattenEx <T>();
}
}
else if (func is EmbedID <T> )
{
EmbedID <T> embed = (EmbedID <T>)func;
embed.Weight.Data = modelData[func.Name + "/W.npy"].FlattenEx <T>();
}
else if (func is BatchNormalization <T> )
{
BatchNormalization <T> bn = (BatchNormalization <T>)func;
bn.Beta.Data = modelData[func.Name + "/beta.npy"].FlattenEx <T>();
bn.Gamma.Data = modelData[func.Name + "/gamma.npy"].FlattenEx <T>();
if (bn.Train)
{
if (modelData.ContainsKey(func.Name + "/avg_mean.npy"))
{
bn.AvgMean.Data = modelData[func.Name + "/avg_mean.npy"].FlattenEx <T>();
}
if (modelData.ContainsKey(func.Name + "/avg_var.npy"))
{
bn.AvgVar.Data = modelData[func.Name + "/avg_var.npy"].FlattenEx <T>();
}
}
}
else if (func is MultiplyScale <T> )
{
MultiplyScale <T> scale = (MultiplyScale <T>)func;
scale.Weight.Data = modelData[func.Name + "/W.npy"].FlattenEx <T>();
if (scale.BiasTerm)
{
scale.Bias.Data = modelData[func.Name + "/bias/b.npy"].FlattenEx <T>();
}
}
else if (func is LSTM <T> )
{
LSTM <T> lstm = (LSTM <T>)func;
lstm.lateral.Weight.Data = modelData[func.Name + "/lateral/W.npy"].FlattenEx <T>();
lstm.upward.Weight.Data = modelData[func.Name + "/upward/W.npy"].FlattenEx <T>();
lstm.upward.Bias.Data = modelData[func.Name + "/upward/b.npy"].FlattenEx <T>();
}
}
