public static void Main(string [] args)
{
var x = new FloatTensor(10);
var b = new FloatTensor(10);
b.Fill(30);
Dump(b);
x.Random(new RandomGenerator(), 10);
FloatTensor.Add(x, 100, b);
Dump(x);
Dump(b);
#if false
Dump(x);
var y = x.Add(100);
Dump(y);
#endif
for (int i = 0; i < 1000; i++)
{
using (var a = new FloatTensor(1000)){
using (var c = new FloatTensor(1000)) {
var d = a.Add(10);
a.CAdd(0, d);
d.Dispose();
}
}
}
}
public FloatTensor createOnesTensorLike(FloatTensor tensor)
{
FloatTensor new_tensor = tensor.Copy();
new_tensor.Zero_();
new_tensor.Add((float)1, true);
return(new_tensor);
}
public override FloatTensor Forward(FloatTensor input)
{
FloatTensor unbiased_output = input.MM(_weights);
FloatTensor output = unbiased_output.Add(_bias.Expand(unbiased_output.Shape).Contiguous());
activation = output.Id;
return(output);
}
public void AddUnequalShapes()
{
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.Add(tensor2),
Throws.TypeOf <InvalidOperationException>());
}
public void Add()
{
float[] data1 = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
int[] shape1 = { 2, 5 };
var tensor1 = new FloatTensor(data1, shape1);
float[] data2 = { 3, 2, 6, 9, 10, 1, 4, 8, 5, 7 };
int[] shape2 = { 2, 5 };
var tensor2 = new FloatTensor(data2, shape2);
var tensorSum = tensor1.Add(tensor2);
for (int i = 0; i < tensorSum.Size; i++)
{
Assert.AreEqual(tensor1.Data [i] + tensor2.Data [i], tensorSum.Data [i]);
}
}
public void Sigmoid_()
{
float[] data1 = { 0.0f };
int[] shape1 = { 1 };
var tensor1 = new FloatTensor(data1, shape1);
tensor1.Sigmoid_();
Assert.AreEqual(tensor1.Data[0], 0.5f);
float[] data2 = { 0.1f, 0.5f, 1.0f, 2.0f };
float[] data3 = { -0.1f, -0.5f, -1.0f, -2.0f };
int[] shape2 = { 4 };
var tensor2 = new FloatTensor(data2, shape2);
var tensor3 = new FloatTensor(data3, shape2);
tensor2.Sigmoid_();
tensor3.Sigmoid_();
var sum = tensor2.Add(tensor3);
for (int i = 0; i < sum.Size; i++)
{
Assert.AreEqual(sum.Data[i], 1.0f);
}
}
public string processMessage(string json_message)
{
//Debug.LogFormat("<color=green>SyftController.processMessage {0}</color>", json_message);
Command msgObj = JsonUtility.FromJson <Command>(json_message);
if (msgObj.functionCall == "createTensor")
{
FloatTensor tensor = new FloatTensor(msgObj.data, msgObj.shape);
tensor.Shader = shader;
tensors.Add(tensor.Id, tensor);
Debug.LogFormat("<color=magenta>createTensor:</color> {0}", string.Join(", ", tensor.Data));
string id = tensor.Id.ToString();
return(id);
}
else
{
if (msgObj.objectType == "tensor")
{
//Below check needs additions/fix.
bool success = true;
if (msgObj.objectIndex > FloatTensor.CreatedObjectCount)
{
return("Invalid objectIndex: " + msgObj.objectIndex);
}
FloatTensor tensor = tensors[msgObj.objectIndex];
if (msgObj.functionCall == "init_add_matrix_multiply")
{
FloatTensor tensor_1 = tensors [msgObj.tensorIndexParams [0]];
tensor.ElementwiseMultiplication(tensor_1);
}
else if (msgObj.functionCall == "inline_elementwise_subtract")
{
FloatTensor tensor_1 = tensors [msgObj.tensorIndexParams [0]];
tensor.ElementwiseSubtract(tensor_1);
}
else if (msgObj.functionCall == "multiply_derivative")
{
FloatTensor tensor_1 = tensors [msgObj.tensorIndexParams [0]];
tensor.MultiplyDerivative(tensor_1);
}
else if (msgObj.functionCall == "add_matrix_multiply")
{
FloatTensor tensor_1 = tensors [msgObj.tensorIndexParams [0]];
FloatTensor tensor_2 = tensors [msgObj.tensorIndexParams [1]];
tensor.AddMatrixMultiply(tensor_1, tensor_2);
}
else if (msgObj.functionCall == "print")
{
return(tensor.Print());
}
else if (msgObj.functionCall == "abs")
{
// calls the function on our tensor object
tensor.Abs();
}
else if (msgObj.functionCall == "neg")
{
tensor.Neg();
}
else if (msgObj.functionCall == "add")
{
FloatTensor tensor_1 = tensors [msgObj.tensorIndexParams [0]];
FloatTensor output = tensor_1.Add(tensor_1);
tensors.Add(output.Id, output);
string id = output.Id.ToString();
return(id);
}
else if (msgObj.functionCall == "scalar_multiply")
{
//get the scalar, cast it and multiply
tensor.ScalarMultiplication((float)msgObj.tensorIndexParams[0]);
}
else
{
success = false;
}
if (success)
{
return(msgObj.functionCall + ": OK");
}
}
}
return("SyftController.processMessage: Command not found.");
}