public override FloatTensor Forward(FloatTensor prediction, FloatTensor target)
{
// Note: prediction should be logits, basically pre-softmax. This method applies softmax first.
// TODO check shapes
FloatTensor output = ((target.Mul(prediction.Log())).Sum()).Neg();
return(output);
}
protected override FloatTensor Forward(FloatTensor prediction, FloatTensor target)
{
// Note: prediction should be logits, basically pre-softmax. This method applies softmax first.
// TODO check shapes
FloatTensor softmax = Functional.Softmax(prediction);
FloatTensor output = ((target.Mul(softmax.Log1p())).Sum()).Mul(-1);
return(output);
}
public void ElementwiseMultiplicationUnequalShapes()
{
float[] data1 = { 1, 2, 3, 4, 5, 6 };
int[] shape1 = { 2, 3 };
var tensor1 = new FloatTensor(data1, shape1);
float[] data2 = { 1, 2, 3, 4, 5, 6 };
int[] shape2 = { 3, 2 };
var tensor2 = new FloatTensor(data2, shape2);
Assert.That(() => tensor1.Mul(tensor2),
Throws.TypeOf <InvalidOperationException>());
}
public override FloatTensor Forward(FloatTensor input)
{
if (_mask_source == null || input.Size != _mask_source.Size)
{
_mask_source = input.emptyTensorCopy(hook_graph: false);
_mask_source.Fill(1 - rate, inline: true);
}
;
FloatTensor output = input.Mul(_mask_source.SampleMask());
activation = output.Id;
return(output);
}
public void ScalarMultiplication()
{
float[] data1 = { float.MinValue, -10, -1.5f, 0, 1.5f, 10, 20, float.MaxValue };
int[] shape1 = { 2, 4 };
var tensor1 = new FloatTensor(data1, shape1);
var tensor2 = new FloatTensor(data1, shape1);
// Test multiplication by 0
float scalar = 0;
var result = tensor1.Mul(scalar);
for (int i = 0; i < tensor1.Size; i++)
{
Assert.AreEqual(tensor2.Data [i] * scalar, result.Data [i]);
}
// Test multiplication by positive
tensor1 = new FloatTensor(data1, shape1);
scalar = 99;
result = tensor1.Mul(scalar);
for (int i = 0; i < tensor1.Size; i++)
{
Assert.AreEqual(tensor2.Data [i] * scalar, result.Data [i]);
}
// Test multiplication by negative
tensor1 = new FloatTensor(data1, shape1);
scalar = -99;
result = tensor1.Mul(scalar);
for (int i = 0; i < tensor1.Size; i++)
{
Assert.AreEqual(tensor2.Data [i] * scalar, result.Data [i]);
}
// Test multiplication by decimal
tensor1 = new FloatTensor(data1, shape1);
scalar = 0.000001f;
result = tensor1.Mul(scalar);
for (int i = 0; i < tensor1.Size; i++)
{
Assert.AreEqual(tensor2.Data [i] * scalar, result.Data [i]);
}
}
public void ElementwiseMultiplication()
{
float[] data1 = { float.MinValue, -10, -1.5f, 0, 1.5f, 10, 20, float.MaxValue };
int[] shape1 = { 2, 4 };
var tensor1 = new FloatTensor(data1, shape1);
float[] data2 = { float.MinValue, -10, -1.5f, 0, 1.5f, 10, 20, float.MaxValue };
int[] shape2 = { 2, 4 };
var tensor2 = new FloatTensor(data2, shape2);
var tensorMult = tensor1.Mul(tensor2);
for (int i = 0; i < tensorMult.Size; i++)
{
float current = tensor1.Data [i] * tensor2.Data [i];
Assert.AreEqual(tensorMult.Data [i], current);
}
}