Beispiel #1
0
        protected override float Outputs(OutputVector result, Floatarray v)
        {
            result.Clear();
            if (v.Rank() == 1)
            {
                v.Reshape(csize, csize, 0, 0);
            }
            // byte array input
            StdInput vinput = new StdInput(v);

            byte[] buffer = vinput.GetDataBuffer();

            // char classifier compute outputs
            if (CharClass.AsciiTarget)
            {
                // net output 0..~ (lower - winner)
                CharClass.ComputeOutputs(buffer, vinput.Length, vinput.Height, vinput.Width, result);
            }
            else
            {
                // net output 0..1; (higher - winner)
                CharClass.ComputeOutputsRaw(buffer, vinput.Length, vinput.Height, vinput.Width, result);
            }

            // junk classifier
            if (PGetb("junk") && !DisableJunk && !JunkClass.IsEmpty)
            {
                OutputVector jv = new OutputVector();
                if (JunkClass.AsciiTarget)
                {
                    JunkClass.ComputeOutputs(buffer, vinput.Length, vinput.Height, vinput.Width, jv);
                    result[jc()] = jv.Value(1);
                }
                else
                {
                    //result.Normalize();
                    result.Normalize2();
                    JunkClass.ComputeOutputsRaw(buffer, vinput.Length, vinput.Height, vinput.Width, jv);
                    jv.Normalize2();
                    for (int i = 0; i < result.nKeys(); i++)
                    {
                        result.Values[i] *= jv.Value(0);
                    }
                    result[jc()] = jv.Value(1);
                }
            }

            return(0.0f);
        }
Beispiel #2
0
        private void DoTestRecognize(LenetClassifier classifier)
        {
            OutputVector ov = new OutputVector();
            Floatarray   v  = new Floatarray();
            Bytearray    ba = new Bytearray(1, 1);

            ImgIo.read_image_gray(ba, testPngFileName);
            NarrayUtil.Sub(255, ba);
            v.Copy(ba);
            v /= 255.0;
            classifier.XOutputs(ov, v);
            Console.WriteLine("Featured output class '{0}', score '{1}'", (char)ov.Key(ov.BestIndex), ov.Value(ov.BestIndex));
        }
Beispiel #3
0
        protected override float Outputs(OutputVector result, Floatarray v)
        {
            result.Clear();
            if (v.Rank() == 1)
                v.Reshape(csize, csize, 0, 0);
            // byte array input
            StdInput vinput = new StdInput(v);
            byte[] buffer = vinput.GetDataBuffer();

            // char classifier compute outputs
            if (CharClass.AsciiTarget)
                // net output 0..~ (lower - winner)
                CharClass.ComputeOutputs(buffer, vinput.Length, vinput.Height, vinput.Width, result);
            else
                // net output 0..1; (higher - winner)
                CharClass.ComputeOutputsRaw(buffer, vinput.Length, vinput.Height, vinput.Width, result);

            // junk classifier
            if (PGetb("junk") && !DisableJunk && !JunkClass.IsEmpty)
            {
                OutputVector jv = new OutputVector();
                if (JunkClass.AsciiTarget)
                {
                    JunkClass.ComputeOutputs(buffer, vinput.Length, vinput.Height, vinput.Width, jv);
                    result[jc()] = jv.Value(1);
                }
                else
                {
                    //result.Normalize();
                    result.Normalize2();
                    JunkClass.ComputeOutputsRaw(buffer, vinput.Length, vinput.Height, vinput.Width, jv);
                    jv.Normalize2();
                    for (int i = 0; i < result.nKeys(); i++)
                        result.Values[i] *= jv.Value(0);
                    result[jc()] = jv.Value(1);
                }
            }

            return 0.0f;
        }