public string Evaluate(FloatTensor test_input, FloatTensor test_target, Loss.Loss criterion, int batch_size)
{
if (test_input.Shape[0] != test_target.Shape[0])
{
throw new InvalidDataException("Input and Target tensors don't seem to have the right dims");
}
int[] input_buffer_shape = new int[test_input.Shape.Length];
input_buffer_shape[0] = batch_size;
for (int i = 1; i < test_input.Shape.Length; i++)
{
input_buffer_shape[i] = test_input.Shape[i];
}
FloatTensor test_input_buffer = controller.floatTensorFactory.Create(_shape: input_buffer_shape, _autograd: true);
FloatTensor test_loss = controller.floatTensorFactory.Create(_shape: new int[] { 1 });
int[] target_buffer_shape = new int[test_target.Shape.Length];
target_buffer_shape[0] = batch_size;
for (int i = 1; i < test_target.Shape.Length; i++)
{
target_buffer_shape[i] = test_target.Shape[i];
}
FloatTensor test_target_buffer = controller.floatTensorFactory.Create(_shape: target_buffer_shape, _autograd: true);
float loss = 0;
int num_batches = (int)(test_input.Shape[0] / batch_size);
FloatTensor predictions = controller.floatTensorFactory.Create(test_target.Shape);
int test_input_batch_offset = batch_size;
for (int i = 1; i < test_input.Shape.Length; i++)
{
test_input_batch_offset *= test_input.Shape[i];
}
int test_target_batch_offset = batch_size;
for (int i = 1; i < test_target.Shape.Length; i++)
{
test_target_batch_offset *= test_target.Shape[i];
}
for (int batch_i = 0; batch_i < num_batches; batch_i++)
{
test_input_buffer.Fill(test_input, starting_offset: batch_i * test_input_batch_offset,
length_to_fill: test_input_batch_offset);
test_target_buffer.Fill(test_target, starting_offset: batch_i * test_target_batch_offset,
length_to_fill: test_target_batch_offset);
var pred = Forward(test_input_buffer);
var batch_loss = criterion.Forward(pred, test_target_buffer);
List <int> tensor_ids = new List <int> {
predictions.Id, pred.Id
};
predictions.Fill(pred, starting_offset: 0, length_to_fill: test_target_batch_offset, starting_offset_fill: test_target_batch_offset * batch_i);
loss += (batch_loss.Data[0] / batch_size);
}
test_loss.Fill(loss / num_batches);
return(test_loss.Id.ToString() + "," + predictions.Id.ToString());
}
public float FitBatch(int batch_i, int iteration)
{
if (((batch_i + 1) * _input_batch_offset) < _input_tensor_origin.Size)
{
input_buffer.Fill(_input_tensor_origin, starting_offset: batch_i * _input_batch_offset,
length_to_fill: _input_batch_offset);
target_buffer.Fill(_target_tensor_origin, starting_offset: batch_i * _target_batch_offset,
length_to_fill: _target_batch_offset);
var pred = Forward(input_buffer);
var loss = _criterion.Forward(pred, target_buffer);
if (cached_ones_grad_for_backprop == null || cached_ones_grad_for_backprop.Size != loss.Size)
{
cached_ones_grad_for_backprop = loss.createOnesTensorLike();
cached_ones_grad_for_backprop.Autograd = false;
}
loss.Backward(cached_ones_grad_for_backprop);
_optimizer.Step(this.input_buffer.Shape[0], iteration);
return(loss.Data[0]);
}
else
{
return(0);
}
}