public void Test_SoftSign_KerasModel()
{
string path = @"tests\test_softsign_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(1, 8, 1, 1);
inp[0, 0, 0, 0] = 1;
inp[0, 1, 0, 0] = 2;
inp[0, 2, 0, 0] = -1;
inp[0, 3, 0, 0] = 0;
inp[0, 4, 0, 0] = 3;
inp[0, 5, 0, 0] = 1;
inp[0, 6, 0, 0] = 1;
inp[0, 7, 0, 0] = 2;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 4);
Assert.AreEqual(ou.GetDimension().w, 1);
Assert.AreEqual(ou[0, 0, 0, 0], -0.5, 0.00001);
Assert.AreEqual(ou[0, 0, 1, 0], 0.8461538553237915, 0.00001);
Assert.AreEqual(ou[0, 0, 2, 0], 0.9487179517745972, 0.00001);
Assert.AreEqual(ou[0, 0, 3, 0], 0.8999999761581421, 0.00001);
}
public void Test_MaxPool1D_2_KerasModel()
{
string path = @"tests\test_maxpool_1D_2_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(1, 5, 2, 1);
inp[0, 0, 0, 0] = 0;
inp[0, 0, 1, 0] = 1;
inp[0, 1, 0, 0] = 2;
inp[0, 1, 1, 0] = 1;
inp[0, 2, 0, 0] = 0;
inp[0, 2, 1, 0] = 0;
inp[0, 3, 0, 0] = 2;
inp[0, 3, 1, 0] = 1;
inp[0, 4, 0, 0] = 2;
inp[0, 4, 1, 0] = 1;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 2);
Assert.AreEqual(ou.GetDimension().w, 2);
Assert.AreEqual(ou[0, 0, 0, 0], 2.0, 0.00001);
Assert.AreEqual(ou[0, 0, 1, 0], 1.0, 0.00001);
Assert.AreEqual(ou[0, 1, 0, 0], 2.0, 0.00001);
Assert.AreEqual(ou[0, 1, 1, 0], 1.0, 0.00001);
}
public void Test_Sigmoid_KerasModel()
{
string path = @"tests\test_sigmoid_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(1, 8, 1, 1);
inp[0, 0, 0, 0] = 1;
inp[0, 1, 0, 0] = 2;
inp[0, 2, 0, 0] = -1;
inp[0, 3, 0, 0] = 0;
inp[0, 4, 0, 0] = 3;
inp[0, 5, 0, 0] = 1;
inp[0, 6, 0, 0] = 1;
inp[0, 7, 0, 0] = 2;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 4);
Assert.AreEqual(ou.GetDimension().w, 1);
Assert.AreEqual(ou[0, 0, 0, 0], 0.2689414322376251, 0.00001);
Assert.AreEqual(ou[0, 0, 1, 0], 0.9959298968315125, 0.00001);
Assert.AreEqual(ou[0, 0, 2, 0], 1.0, 0.00001);
Assert.AreEqual(ou[0, 0, 3, 0], 0.9998766183853149, 0.00001);
}
public static Brain Import(string path)
{
SequentialModel model = null;
Brain brain = null;
if (path.EndsWith(".json"))
{
model = new ReaderKerasModel(path).GetSequentialExecutor();
}
else if (path.EndsWith(".txt"))
{
model = PersistSequentialModel.DeserializeModel(path);
}
if (model != null)
{
brain = new Brain(model)
{
_weights = model.GetWeights()
}
}
;
return(brain);
}
}
public void Test_AvgPool1D_1_KerasModel()
{
string path = @"tests\test_avgpool_1D_1_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(1, 5, 2, 1);
inp[0, 0, 0, 0] = 0;
inp[0, 0, 1, 0] = 1;
inp[0, 1, 0, 0] = 2;
inp[0, 1, 1, 0] = 1;
inp[0, 2, 0, 0] = 0;
inp[0, 2, 1, 0] = 0;
inp[0, 3, 0, 0] = 2;
inp[0, 3, 1, 0] = 1;
inp[0, 4, 0, 0] = 2;
inp[0, 4, 1, 0] = 1;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 2);
Assert.AreEqual(ou.GetDimension().w, 3);
Assert.AreEqual(ou[0, 0, 0, 0], 0.6666666865348816, 0.00001);
Assert.AreEqual(ou[0, 0, 1, 0], 0.6666666865348816, 0.00001);
Assert.AreEqual(ou[0, 1, 0, 0], 1.3333333730697632, 0.00001);
Assert.AreEqual(ou[0, 1, 1, 0], 0.6666666865348816, 0.00001);
Assert.AreEqual(ou[0, 2, 0, 0], 1.3333333730697632, 0.00001);
Assert.AreEqual(ou[0, 2, 1, 0], 0.6666666865348816, 0.00001);
}
public void Test_SoftPlus_KerasModel()
{
string path = @"tests\test_softplus_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(1, 8, 1, 1);
inp[0, 0, 0, 0] = 1;
inp[0, 1, 0, 0] = 2;
inp[0, 2, 0, 0] = -1;
inp[0, 3, 0, 0] = 0;
inp[0, 4, 0, 0] = 3;
inp[0, 5, 0, 0] = 1;
inp[0, 6, 0, 0] = 1;
inp[0, 7, 0, 0] = 2;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 4);
Assert.AreEqual(ou.GetDimension().w, 1);
Assert.AreEqual(ou[0, 0, 0, 0], 0.31326162815093994, 0.00001);
Assert.AreEqual(ou[0, 0, 1, 0], 5.504078388214111, 0.00001);
Assert.AreEqual(ou[0, 0, 2, 0], 18.5, 0.00001);
Assert.AreEqual(ou[0, 0, 3, 0], 9.000123023986816, 0.00001);
}
public double[] Evaluate(Bitmap img)
{
if (model == null)
{
var reader = new ReaderKerasModel(cnn_nn);
model = reader.GetSequentialExecutor();
}
var array = new Data2D(28, 28, 1, 1);
for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
Color pixel = img.GetPixel(j, i);
double value = 255 - pixel.A;
value = value / 255;
array[i, j, 0, 0] = value;
}
}
var result = model.ExecuteNetwork(array) as Data2D;
double[] toreturn = new double[10];
for (int i = 0; i < 10; i++)
{
toreturn[i] = result[0, 0, i, 0];
}
return(toreturn);
}
public void Test_ReLu_KerasModel()
{
string path = @"tests\test_relu_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(1, 8, 1, 1);
inp[0, 0, 0, 0] = 1;
inp[0, 1, 0, 0] = 2;
inp[0, 2, 0, 0] = -1;
inp[0, 3, 0, 0] = 0;
inp[0, 4, 0, 0] = 3;
inp[0, 5, 0, 0] = 1;
inp[0, 6, 0, 0] = 1;
inp[0, 7, 0, 0] = 2;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 4);
Assert.AreEqual(ou.GetDimension().w, 1);
Assert.AreEqual(ou[0, 0, 0, 0], 0.0, 0.00001);
Assert.AreEqual(ou[0, 0, 1, 0], 5.5, 0.00001);
Assert.AreEqual(ou[0, 0, 2, 0], 18.5, 0.00001);
Assert.AreEqual(ou[0, 0, 3, 0], 9.0, 0.00001);
}
public void Test_TanH_KerasModel()
{
string path = @"tests\test_tanh_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(1, 8, 1, 1);
inp[0, 0, 0, 0] = 1;
inp[0, 1, 0, 0] = 2;
inp[0, 2, 0, 0] = -1;
inp[0, 3, 0, 0] = 0;
inp[0, 4, 0, 0] = 3;
inp[0, 5, 0, 0] = 1;
inp[0, 6, 0, 0] = 1;
inp[0, 7, 0, 0] = 2;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 4);
Assert.AreEqual(ou.GetDimension().w, 1);
Assert.AreEqual(ou[0, 0, 0, 0], -0.7615941762924194, 0.00001);
Assert.AreEqual(ou[0, 0, 1, 0], 0.9999667406082153, 0.00001);
Assert.AreEqual(ou[0, 0, 2, 0], 1.0, 0.00001);
Assert.AreEqual(ou[0, 0, 3, 0], 1.0, 0.00001);
}
public void Test_Softmax_KerasModel()
{
string path = @"tests\test_softmax_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(1, 8, 1, 1);
inp[0, 0, 0, 0] = 1;
inp[0, 1, 0, 0] = 2;
inp[0, 2, 0, 0] = -1;
inp[0, 3, 0, 0] = 0;
inp[0, 4, 0, 0] = 3;
inp[0, 5, 0, 0] = 1;
inp[0, 6, 0, 0] = 1;
inp[0, 7, 0, 0] = 2;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 4);
Assert.AreEqual(ou.GetDimension().w, 1);
Assert.AreEqual(ou[0, 0, 0, 0], 3.3980058766758248e-09, 1e-10);
Assert.AreEqual(ou[0, 0, 1, 0], 2.26015504267707e-06, 1e-7);
Assert.AreEqual(ou[0, 0, 2, 0], 0.9999228715896606, 0.00001);
Assert.AreEqual(ou[0, 0, 3, 0], 7.484605885110795e-05, 1e-6);
}
public void Test_RepeatVector_KerasModel()
{
string path = @"tests\test_repeatvector_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D data = new Data2D(1, 1, 4, 2);
for (int c = 0; c < 4; ++c)
{
data[0, 0, c, 0] = c + 1;
data[0, 0, c, 1] = -(c + 1);
}
Data2D output = model.ExecuteNetwork(data) as Data2D;
// Checking sizes
Dimension dim = output.GetDimension();
Assert.AreEqual(dim.b, 2);
Assert.AreEqual(dim.c, 4);
Assert.AreEqual(dim.h, 1);
Assert.AreEqual(dim.w, 3);
// Checking calculation
Assert.AreEqual(output[0, 0, 0, 0], 1, 0.0000001);
Assert.AreEqual(output[0, 0, 1, 0], 2, 0.0000001);
Assert.AreEqual(output[0, 0, 2, 0], 3, 0.0000001);
Assert.AreEqual(output[0, 0, 3, 0], 4, 0.0000001);
Assert.AreEqual(output[0, 1, 0, 0], 1, 0.0000001);
Assert.AreEqual(output[0, 1, 1, 0], 2, 0.0000001);
Assert.AreEqual(output[0, 1, 2, 0], 3, 0.0000001);
Assert.AreEqual(output[0, 1, 3, 0], 4, 0.0000001);
Assert.AreEqual(output[0, 2, 0, 0], 1, 0.0000001);
Assert.AreEqual(output[0, 2, 1, 0], 2, 0.0000001);
Assert.AreEqual(output[0, 2, 2, 0], 3, 0.0000001);
Assert.AreEqual(output[0, 2, 3, 0], 4, 0.0000001);
Assert.AreEqual(output[0, 0, 0, 1], -1, 0.0000001);
Assert.AreEqual(output[0, 0, 1, 1], -2, 0.0000001);
Assert.AreEqual(output[0, 0, 2, 1], -3, 0.0000001);
Assert.AreEqual(output[0, 0, 3, 1], -4, 0.0000001);
Assert.AreEqual(output[0, 1, 0, 1], -1, 0.0000001);
Assert.AreEqual(output[0, 1, 1, 1], -2, 0.0000001);
Assert.AreEqual(output[0, 1, 2, 1], -3, 0.0000001);
Assert.AreEqual(output[0, 1, 3, 1], -4, 0.0000001);
Assert.AreEqual(output[0, 2, 0, 1], -1, 0.0000001);
Assert.AreEqual(output[0, 2, 1, 1], -2, 0.0000001);
Assert.AreEqual(output[0, 2, 2, 1], -3, 0.0000001);
Assert.AreEqual(output[0, 2, 3, 1], -4, 0.0000001);
}
public void Test_Conv1D_2_KerasModel()
{
string path = @"tests\test_conv_1D_2_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(1, 6, 4, 1);
inp[0, 0, 0, 0] = 0;
inp[0, 0, 1, 0] = 1;
inp[0, 0, 2, 0] = 2;
inp[0, 0, 3, 0] = 1.5;
inp[0, 1, 0, 0] = 1;
inp[0, 1, 1, 0] = 0;
inp[0, 1, 2, 0] = 0;
inp[0, 1, 3, 0] = 0.6;
inp[0, 2, 0, 0] = 2;
inp[0, 2, 1, 0] = 1;
inp[0, 2, 2, 0] = 2;
inp[0, 2, 3, 0] = 2.5;
inp[0, 3, 0, 0] = 1;
inp[0, 3, 1, 0] = 0;
inp[0, 3, 2, 0] = -1;
inp[0, 3, 3, 0] = 0;
inp[0, 4, 0, 0] = 1;
inp[0, 4, 1, 0] = -2;
inp[0, 4, 2, 0] = 3;
inp[0, 4, 3, 0] = 3.5;
inp[0, 5, 0, 0] = 2;
inp[0, 5, 1, 0] = 1;
inp[0, 5, 2, 0] = 4;
inp[0, 5, 3, 0] = 3.5;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 3);
Assert.AreEqual(ou.GetDimension().w, 3);
Assert.AreEqual(ou[0, 0, 0, 0], 9.399999618530273, 0.00001);
Assert.AreEqual(ou[0, 0, 1, 0], -1.6999998092651367, 0.00001);
Assert.AreEqual(ou[0, 0, 2, 0], 4.550000190734863, 0.00001);
Assert.AreEqual(ou[0, 1, 0, 0], 8.5, 0.00001);
Assert.AreEqual(ou[0, 1, 1, 0], -4.0, 0.00001);
Assert.AreEqual(ou[0, 1, 2, 0], 12.25, 0.00001);
Assert.AreEqual(ou[0, 2, 0, 0], 23.0, 0.00001);
Assert.AreEqual(ou[0, 2, 1, 0], 7.5, 0.00001);
Assert.AreEqual(ou[0, 2, 2, 0], 14.5, 0.00001);
}
public void Test_SimpleRNN_KerasModel()
{
string path = @"tests\test_simplernn_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
// Initialize data.
Data2D data = new Data2D(1, 3, 3, 4);
int l = 0;
for (int b = 0; b < 4; ++b)
{
for (int w = 0; w < 3; ++w)
{
for (int c = 0; c < 3; ++c)
{
l += 1;
data[0, w, c, b] = l % 5 + 1;
}
}
}
Data2D output = model.ExecuteNetwork(data) as Data2D;
// Checking sizes
Dimension dim = output.GetDimension();
Assert.AreEqual(dim.b, 4);
Assert.AreEqual(dim.c, 4);
Assert.AreEqual(dim.h, 1);
Assert.AreEqual(dim.w, 1);
// Checking calculation
Assert.AreEqual(output[0, 0, 0, 0], -54, 0.000001);
Assert.AreEqual(output[0, 0, 1, 0], -39, 0.000001);
Assert.AreEqual(output[0, 0, 2, 0], 36, 0.000001);
Assert.AreEqual(output[0, 0, 3, 0], 72, 0.000001);
Assert.AreEqual(output[0, 0, 0, 1], 12, 0.000001);
Assert.AreEqual(output[0, 0, 1, 1], -19, 0.000001);
Assert.AreEqual(output[0, 0, 2, 1], -10, 0.000001);
Assert.AreEqual(output[0, 0, 3, 1], 10, 0.000001);
Assert.AreEqual(output[0, 0, 0, 2], -72, 0.000001);
Assert.AreEqual(output[0, 0, 1, 2], 16, 0.000001);
Assert.AreEqual(output[0, 0, 2, 2], 74, 0.000001);
Assert.AreEqual(output[0, 0, 3, 2], 68, 0.000001);
Assert.AreEqual(output[0, 0, 0, 3], -161, 0.000001);
Assert.AreEqual(output[0, 0, 1, 3], -14, 0.000001);
Assert.AreEqual(output[0, 0, 2, 3], 158, 0.000001);
Assert.AreEqual(output[0, 0, 3, 3], 141, 0.000001);
}
public void Test_Reshape2D_KerasModel()
{
string path = @"tests\test_reshape_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D data = new Data2D(3, 3, 2, 1);
int l = 0;
for (int h = 0; h < 3; ++h)
{
for (int w = 0; w < 3; ++w)
{
for (int c = 0; c < 2; ++c)
{
l += 1;
data[h, w, c, 0] = l + 1;
}
}
}
Data2D output = model.ExecuteNetwork(data) as Data2D;
// Checking sizes
Dimension dim = output.GetDimension();
Assert.AreEqual(dim.b, 1);
Assert.AreEqual(dim.c, 3);
Assert.AreEqual(dim.h, 3);
Assert.AreEqual(dim.w, 2);
// Checking calculation
Assert.AreEqual(output[0, 0, 0, 0], 2, 0.0000001);
Assert.AreEqual(output[0, 0, 1, 0], 3, 0.0000001);
Assert.AreEqual(output[0, 0, 2, 0], 4, 0.0000001);
Assert.AreEqual(output[0, 1, 0, 0], 5, 0.0000001);
Assert.AreEqual(output[0, 1, 1, 0], 6, 0.0000001);
Assert.AreEqual(output[0, 1, 2, 0], 7, 0.0000001);
Assert.AreEqual(output[1, 0, 0, 0], 8, 0.0000001);
Assert.AreEqual(output[1, 0, 1, 0], 9, 0.0000001);
Assert.AreEqual(output[1, 0, 2, 0], 10, 0.0000001);
Assert.AreEqual(output[1, 1, 0, 0], 11, 0.0000001);
Assert.AreEqual(output[1, 1, 1, 0], 12, 0.0000001);
Assert.AreEqual(output[1, 1, 2, 0], 13, 0.0000001);
Assert.AreEqual(output[2, 0, 0, 0], 14, 0.0000001);
Assert.AreEqual(output[2, 0, 1, 0], 15, 0.0000001);
Assert.AreEqual(output[2, 0, 2, 0], 16, 0.0000001);
Assert.AreEqual(output[2, 1, 0, 0], 17, 0.0000001);
Assert.AreEqual(output[2, 1, 1, 0], 18, 0.0000001);
Assert.AreEqual(output[2, 1, 2, 0], 19, 0.0000001);
}
public void Test_LSTM_KerasModel()
{
string path = @"tests\test_lstm_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
// Initialize data.
Data2D data = new Data2D(1, 3, 3, 5);
int l = 0;
for (int b = 0; b < 5; ++b)
{
for (int w = 0; w < 3; ++w)
{
for (int c = 0; c < 3; ++c)
{
l += 1;
data[0, w, c, b] = (l % 5 + 1) / 10.0;
}
}
}
Data2D output = model.ExecuteNetwork(data) as Data2D;
// Checking sizes
Dimension dim = output.GetDimension();
Assert.AreEqual(dim.b, 5);
Assert.AreEqual(dim.c, 2);
Assert.AreEqual(dim.h, 1);
Assert.AreEqual(dim.w, 1);
// Checking calculation
Assert.AreEqual(output[0, 0, 0, 0], 0.015777, 0.00001);
Assert.AreEqual(output[0, 0, 1, 0], 0.0, 0.00001);
Assert.AreEqual(output[0, 0, 0, 1], 0.01605, 0.00001);
Assert.AreEqual(output[0, 0, 1, 1], 0.0, 0.00001);
Assert.AreEqual(output[0, 0, 0, 2], 0.016398, 0.00001);
Assert.AreEqual(output[0, 0, 1, 2], 0.0, 0.00001);
Assert.AreEqual(output[0, 0, 0, 3], 0.006314, 0.00001);
Assert.AreEqual(output[0, 0, 1, 3], 0.0, 0.00001);
Assert.AreEqual(output[0, 0, 0, 4], 0.016303, 0.00001);
Assert.AreEqual(output[0, 0, 1, 4], 0.0, 0.00001);
}
public void Test_GRU_KerasModel()
{
string path = @"tests\test_gru_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
// Initialize data.
Data2D data = new Data2D(1, 3, 3, 5);
int l = 0;
for (int b = 0; b < 5; ++b)
{
for (int w = 0; w < 3; ++w)
{
for (int c = 0; c < 3; ++c)
{
l += 1;
data[0, w, c, b] = (l % 5 + 1) / 10.0;
}
}
}
Data2D output = model.ExecuteNetwork(data) as Data2D;
// Checking sizes
Dimension dim = output.GetDimension();
Assert.AreEqual(dim.b, 5);
Assert.AreEqual(dim.c, 2);
Assert.AreEqual(dim.h, 1);
Assert.AreEqual(dim.w, 1);
// Checking calculation
Assert.AreEqual(output[0, 0, 0, 0], 0.19632, 0.00001);
Assert.AreEqual(output[0, 0, 1, 0], 0.37259, 0.00001);
Assert.AreEqual(output[0, 0, 0, 1], 0.21991, 0.00001);
Assert.AreEqual(output[0, 0, 1, 1], 0.37473, 0.00001);
Assert.AreEqual(output[0, 0, 0, 2], 0.24834, 0.00001);
Assert.AreEqual(output[0, 0, 1, 2], 0.38176, 0.00001);
Assert.AreEqual(output[0, 0, 0, 3], 0.18727, 0.00001);
Assert.AreEqual(output[0, 0, 1, 3], 0.35267, 0.00001);
Assert.AreEqual(output[0, 0, 0, 4], 0.166619, 0.00001);
Assert.AreEqual(output[0, 0, 1, 4], 0.35275, 0.00001);
}
public static void Sample1(string filePath)
{
// Read the previously created json.
var reader = new ReaderKerasModel(filePath);
SequentialModel model = reader.GetSequentialExecutor();
// Then create the data to run the executer on.
// batch: should be set in the Keras model.
int height = 0;
int width = 0;
int channel = 0;
int batch = 0;
Data2D input = new Data2D(height, width, channel, batch);
// Calculate the network's output.
IData output = model.ExecuteNetwork(input);
}
static public void KerasModelTest(string pathIn, string pathModel, string pathOut, double accuracy = 0.00001)
{
var reader = new ReaderKerasModel(pathModel);
SequentialModel model = reader.GetSequentialExecutor();
// Initialize data.
Data2D data = Utils.ReadDataFromFile(pathIn);
// Load expected output and calculate the actual output.
Data2D expected = Utils.ReadDataFromFile(pathOut);
Data2D output = model.ExecuteNetwork(data) as Data2D;
// Checking sizes
Utils.CheckDimensions(output, expected);
// Checking calculation
Utils.CheckResults(output, expected, accuracy);
}
public ActionResult GetResult()
{
var img = Cv2.ImRead("");
CascadeClassifier face_classifier = new CascadeClassifier("");
img = img.CvtColor(ColorConversionCodes.BGR2GRAY);
var faces = face_classifier.DetectMultiScale(img, scaleFactor: 1.2, minNeighbors: 3, minSize: new OpenCvSharp.Size(140, 140));
//var gender_classifier =
var reder = new ReaderKerasModel("");
//SequentalModel model = reader.GetSequentialExecutor();
return(Ok());
}
public IData Run()
{
var reader = new ReaderKerasModel(xor_path);
var model = reader.GetSequentialExecutor();
var arr = new Data2D(1, 1, 2, 4);
for (int i = 0; i < 4; i++)
{
for (int j = 0; j < 2; j++)
{
arr[0, 0, j, i] = j == 0 ? i / 2 : i % 2;
}
}
Data2D result = model.ExecuteNetwork(arr) as Data2D;
return(result);
}
public void Test_GlobalMaxPool2D_KerasModel()
{
string path = @"tests\test_globalmaxpool_2D_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(3, 3, 2, 1);
inp[0, 0, 0, 0] = 1;
inp[1, 0, 0, 0] = 2;
inp[2, 0, 0, 0] = 0;
inp[0, 1, 0, 0] = 3;
inp[1, 1, 0, 0] = 4;
inp[2, 1, 0, 0] = 0;
inp[0, 2, 0, 0] = 2;
inp[1, 2, 0, 0] = 2;
inp[2, 2, 0, 0] = 0;
inp[0, 0, 1, 0] = 0;
inp[1, 0, 1, 0] = 3;
inp[2, 0, 1, 0] = 1;
inp[0, 1, 1, 0] = 1;
inp[1, 1, 1, 0] = 1;
inp[2, 1, 1, 0] = -1;
inp[0, 2, 1, 0] = -3;
inp[1, 2, 1, 0] = -1;
inp[2, 2, 1, 0] = 0;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 2);
Assert.AreEqual(ou.GetDimension().w, 1);
Assert.AreEqual(ou[0, 0, 0, 0], 4.0, 0.00001);
Assert.AreEqual(ou[0, 0, 1, 0], 3.0, 0.00001);
}
public void Test_GlobalAvgPool1D_KerasModel()
{
string path = @"tests\test_globalavgpool_1D_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(1, 3, 2, 1);
inp[0, 0, 0, 0] = 1;
inp[0, 1, 0, 0] = 2;
inp[0, 2, 0, 0] = 0;
inp[0, 0, 1, 0] = 3;
inp[0, 1, 1, 0] = 4;
inp[0, 2, 1, 0] = 0;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 2);
Assert.AreEqual(ou.GetDimension().w, 1);
Assert.AreEqual(ou[0, 0, 0, 0], 1.0, 0.00001);
Assert.AreEqual(ou[0, 0, 1, 0], 2.3333332538604736, 0.00001);
}
static void Main(string[] args)
{
var reader = new ReaderKerasModel(cnn_nn);
var model = reader.GetSequentialExecutor();
var array = new Data2D(28, 28, 1, 1);
Bitmap img = new Bitmap(zero_test_path);
for (int i = 0; i < img.Height; i++)
{
for (int j = 0; j < img.Width; j++)
{
Color pixel = img.GetPixel(j, i);
double value = (pixel.R + pixel.G + pixel.B) / 3;
value = value / 255;
array[i, j, 0, 0] = value;
}
}
var result = model.ExecuteNetwork(array);
int a = 5;
}
public void Test_Cropping2D_KerasModel()
{
string path = @"tests\test_crop_2D_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(4, 5, 2, 1);
int l = 0;
for (int h = 0; h < 4; ++h)
{
for (int w = 0; w < 5; ++w)
{
l += 1;
inp[h, w, 0, 0] = l + 1;
inp[h, w, 1, 0] = -(l + 1);
}
}
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 2);
Assert.AreEqual(ou.GetDimension().w, 2);
Assert.AreEqual(ou.GetDimension().h, 2);
Assert.AreEqual(ou[0, 0, 0, 0], 8.0, 0.00001);
Assert.AreEqual(ou[0, 0, 1, 0], -8.0, 0.00001);
Assert.AreEqual(ou[0, 1, 0, 0], 9.0, 0.00001);
Assert.AreEqual(ou[0, 1, 1, 0], -9.0, 0.00001);
Assert.AreEqual(ou[1, 0, 0, 0], 13.0, 0.00001);
Assert.AreEqual(ou[1, 0, 1, 0], -13.0, 0.00001);
Assert.AreEqual(ou[1, 1, 0, 0], 14.0, 0.00001);
Assert.AreEqual(ou[1, 1, 1, 0], -14.0, 0.00001);
}
public void Test_Cropping1D_KerasModel()
{
string path = @"tests\test_crop_1D_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(1, 5, 2, 1);
for (int l = 0; l < 5; ++l)
{
inp[0, l, 0, 0] = l + 1;
inp[0, l, 1, 0] = -(l + 1);
}
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 2);
Assert.AreEqual(ou.GetDimension().w, 2);
Assert.AreEqual(ou[0, 0, 0, 0], 2.0, 0.00001);
Assert.AreEqual(ou[0, 0, 1, 0], -2.0, 0.00001);
Assert.AreEqual(ou[0, 1, 0, 0], 3.0, 0.00001);
Assert.AreEqual(ou[0, 1, 1, 0], -3.0, 0.00001);
}
public void Test_Conv2D_2_KerasModel()
{
string path = @"tests\test_conv_2D_2_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(4, 5, 2, 1);
inp[0, 0, 0, 0] = 0;
inp[0, 0, 1, 0] = 1;
inp[0, 1, 0, 0] = 2;
inp[0, 1, 1, 0] = 1;
inp[0, 2, 0, 0] = 0;
inp[0, 2, 1, 0] = 0;
inp[0, 3, 0, 0] = 2;
inp[0, 3, 1, 0] = 1;
inp[0, 4, 0, 0] = 2;
inp[0, 4, 1, 0] = 1;
inp[1, 0, 0, 0] = 0;
inp[1, 0, 1, 0] = -1;
inp[1, 1, 0, 0] = 1;
inp[1, 1, 1, 0] = -2;
inp[1, 2, 0, 0] = 3;
inp[1, 2, 1, 0] = 1;
inp[1, 3, 0, 0] = 2;
inp[1, 3, 1, 0] = 0;
inp[1, 4, 0, 0] = 2;
inp[1, 4, 1, 0] = -3;
inp[2, 0, 0, 0] = 1;
inp[2, 0, 1, 0] = 2;
inp[2, 1, 0, 0] = -2;
inp[2, 1, 1, 0] = 0;
inp[2, 2, 0, 0] = 3;
inp[2, 2, 1, 0] = -3;
inp[2, 3, 0, 0] = 2;
inp[2, 3, 1, 0] = 1;
inp[2, 4, 0, 0] = 2;
inp[2, 4, 1, 0] = 0;
inp[3, 0, 0, 0] = 1;
inp[3, 0, 1, 0] = 2;
inp[3, 1, 0, 0] = 0;
inp[3, 1, 1, 0] = -2;
inp[3, 2, 0, 0] = 3;
inp[3, 2, 1, 0] = 1;
inp[3, 3, 0, 0] = 2;
inp[3, 3, 1, 0] = 3;
inp[3, 4, 0, 0] = -3;
inp[3, 4, 1, 0] = 1;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 2);
Assert.AreEqual(ou.GetDimension().w, 2);
Assert.AreEqual(ou.GetDimension().h, 2);
Assert.AreEqual(ou[0, 0, 0, 0], 2.0, 0.0001);
Assert.AreEqual(ou[0, 0, 1, 0], 1.0, 0.0001);
Assert.AreEqual(ou[0, 1, 0, 0], 7.5, 0.0001);
Assert.AreEqual(ou[0, 1, 1, 0], 4.0, 0.0001);
Assert.AreEqual(ou[1, 0, 0, 0], 19.5, 0.0001);
Assert.AreEqual(ou[1, 0, 1, 0], 20.5, 0.0001);
Assert.AreEqual(ou[1, 1, 0, 0], -19.5, 0.0001);
Assert.AreEqual(ou[1, 1, 1, 0], -0.5, 0.0001);
}
public void Test_AvgPool2D_1_KerasModel()
{
string path = @"tests\test_avgpool_2D_1_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D inp = new Data2D(4, 5, 2, 1);
inp[0, 0, 0, 0] = 0;
inp[0, 0, 1, 0] = 1;
inp[0, 1, 0, 0] = 2;
inp[0, 1, 1, 0] = 1;
inp[0, 2, 0, 0] = 0;
inp[0, 2, 1, 0] = 0;
inp[0, 3, 0, 0] = 2;
inp[0, 3, 1, 0] = 1;
inp[0, 4, 0, 0] = 2;
inp[0, 4, 1, 0] = 1;
inp[1, 0, 0, 0] = 0;
inp[1, 0, 1, 0] = -1;
inp[1, 1, 0, 0] = 1;
inp[1, 1, 1, 0] = -2;
inp[1, 2, 0, 0] = 3;
inp[1, 2, 1, 0] = 1;
inp[1, 3, 0, 0] = 2;
inp[1, 3, 1, 0] = 0;
inp[1, 4, 0, 0] = 2;
inp[1, 4, 1, 0] = -3;
inp[2, 0, 0, 0] = 1;
inp[2, 0, 1, 0] = 2;
inp[2, 1, 0, 0] = -2;
inp[2, 1, 1, 0] = 0;
inp[2, 2, 0, 0] = 3;
inp[2, 2, 1, 0] = -3;
inp[2, 3, 0, 0] = 2;
inp[2, 3, 1, 0] = 1;
inp[2, 4, 0, 0] = 2;
inp[2, 4, 1, 0] = 0;
inp[3, 0, 0, 0] = 1;
inp[3, 0, 1, 0] = 2;
inp[3, 1, 0, 0] = 0;
inp[3, 1, 1, 0] = -2;
inp[3, 2, 0, 0] = 3;
inp[3, 2, 1, 0] = 1;
inp[3, 3, 0, 0] = 2;
inp[3, 3, 1, 0] = 3;
inp[3, 4, 0, 0] = -3;
inp[3, 4, 1, 0] = 1;
Data2D ou = model.ExecuteNetwork(inp) as Data2D;
Assert.AreEqual(ou.GetDimension().c, 2);
Assert.AreEqual(ou.GetDimension().w, 2);
Assert.AreEqual(ou.GetDimension().h, 2);
Assert.AreEqual(ou[0, 0, 0, 0], 1.1666666269302368, 0.0001);
Assert.AreEqual(ou[0, 0, 1, 0], 0.0833333358168602, 0.0001);
Assert.AreEqual(ou[0, 1, 0, 0], 1.5833333730697632, 0.0001);
Assert.AreEqual(ou[0, 1, 1, 0], -0.25, 0.0001);
Assert.AreEqual(ou[1, 0, 0, 0], 1.3333333730697632, 0.0001);
Assert.AreEqual(ou[1, 0, 1, 0], 0.1666666716337204, 0.0001);
Assert.AreEqual(ou[1, 1, 0, 0], 1.25, 0.0001);
Assert.AreEqual(ou[1, 1, 1, 0], -0.25, 0.0001);
}
public void Test_Permute_KerasModel()
{
string path = @"tests\test_permute_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D data = new Data2D(2, 3, 4, 1);
int cntr = 0;
for (int h = 0; h < 2; ++h)
{
for (int w = 0; w < 3; ++w)
{
for (int c = 0; c < 4; ++c)
{
cntr += 1;
data[h, w, c, 0] = cntr + 1;
}
}
}
Data2D output = model.ExecuteNetwork(data) as Data2D;
// Checking sizes
Dimension dim = output.GetDimension();
Assert.AreEqual(dim.b, 1);
Assert.AreEqual(dim.c, 3);
Assert.AreEqual(dim.h, 4);
Assert.AreEqual(dim.w, 2);
// Checking calculation
Assert.AreEqual(output[0, 0, 0, 0], 2.0, 0.0000001);
Assert.AreEqual(output[0, 0, 1, 0], 6.0, 0.0000001);
Assert.AreEqual(output[0, 0, 2, 0], 10.0, 0.0000001);
Assert.AreEqual(output[0, 1, 0, 0], 14.0, 0.0000001);
Assert.AreEqual(output[0, 1, 1, 0], 18.0, 0.0000001);
Assert.AreEqual(output[0, 1, 2, 0], 22.0, 0.0000001);
Assert.AreEqual(output[1, 0, 0, 0], 3.0, 0.0000001);
Assert.AreEqual(output[1, 0, 1, 0], 7.0, 0.0000001);
Assert.AreEqual(output[1, 0, 2, 0], 11.0, 0.0000001);
Assert.AreEqual(output[1, 1, 0, 0], 15.0, 0.0000001);
Assert.AreEqual(output[1, 1, 1, 0], 19.0, 0.0000001);
Assert.AreEqual(output[1, 1, 2, 0], 23.0, 0.0000001);
Assert.AreEqual(output[2, 0, 0, 0], 4.0, 0.0000001);
Assert.AreEqual(output[2, 0, 1, 0], 8.0, 0.0000001);
Assert.AreEqual(output[2, 0, 2, 0], 12.0, 0.0000001);
Assert.AreEqual(output[2, 1, 0, 0], 16.0, 0.0000001);
Assert.AreEqual(output[2, 1, 1, 0], 20.0, 0.0000001);
Assert.AreEqual(output[2, 1, 2, 0], 24.0, 0.0000001);
Assert.AreEqual(output[3, 0, 0, 0], 5.0, 0.0000001);
Assert.AreEqual(output[3, 0, 1, 0], 9.0, 0.0000001);
Assert.AreEqual(output[3, 0, 2, 0], 13.0, 0.0000001);
Assert.AreEqual(output[3, 1, 0, 0], 17.0, 0.0000001);
Assert.AreEqual(output[3, 1, 1, 0], 21.0, 0.0000001);
Assert.AreEqual(output[3, 1, 2, 0], 25.0, 0.0000001);
}
public void Test_BatchNorm_KerasModel()
{
string path = @"tests\test_batchnorm_model.json";
var reader = new ReaderKerasModel(path);
SequentialModel model = reader.GetSequentialExecutor();
Data2D data = new Data2D(2, 1, 3, 4);
int l = 0;
for (int b = 0; b < 4; ++b)
{
for (int h = 0; h < 2; ++h)
{
for (int w = 0; w < 1; ++w)
{
for (int c = 0; c < 3; ++c)
{
l += 1;
data[h, w, c, b] = l % 7 - 3;
}
}
}
}
Data2D output = model.ExecuteNetwork(data) as Data2D;
Assert.AreEqual(output.GetDimension().b, 4);
Assert.AreEqual(output.GetDimension().c, 3);
Assert.AreEqual(output.GetDimension().w, 1);
Assert.AreEqual(output.GetDimension().h, 2);
// Checking calculation
Assert.AreEqual(output[0, 0, 0, 0], -4.3660264, 0.001);
Assert.AreEqual(output[0, 0, 1, 0], -2.02451992, 0.001);
Assert.AreEqual(output[0, 0, 2, 0], -3.6830132, 0.001);
Assert.AreEqual(output[1, 0, 0, 0], -0.3415066, 0.001);
Assert.AreEqual(output[1, 0, 1, 0], 2.0, 0.001);
Assert.AreEqual(output[1, 0, 2, 0], 0.34150672, 0.001);
Assert.AreEqual(output[0, 0, 0, 1], -5.70753288, 0.001);
Assert.AreEqual(output[0, 0, 1, 1], -3.3660264, 0.001);
Assert.AreEqual(output[0, 0, 2, 1], -5.02451992, 0.001);
Assert.AreEqual(output[1, 0, 0, 1], -1.6830132, 0.001);
Assert.AreEqual(output[1, 0, 1, 1], 0.6584934, 0.001);
Assert.AreEqual(output[1, 0, 2, 1], -1.0, 0.001);
Assert.AreEqual(output[0, 0, 0, 2], 2.34150672, 0.001);
Assert.AreEqual(output[0, 0, 1, 2], -4.70753288, 0.001);
Assert.AreEqual(output[0, 0, 2, 2], -6.3660264, 0.001);
Assert.AreEqual(output[1, 0, 0, 2], -3.02451992, 0.001);
Assert.AreEqual(output[1, 0, 1, 2], -0.6830132, 0.001);
Assert.AreEqual(output[1, 0, 2, 2], -2.34150648, 0.001);
Assert.AreEqual(output[0, 0, 0, 3], 1.0, 0.001);
Assert.AreEqual(output[0, 0, 1, 3], 3.34150672, 0.001);
Assert.AreEqual(output[0, 0, 2, 3], -7.70753288, 0.001);
Assert.AreEqual(output[1, 0, 0, 3], -4.3660264, 0.001);
Assert.AreEqual(output[1, 0, 1, 3], -2.02451992, 0.001);
Assert.AreEqual(output[1, 0, 2, 3], -3.6830132, 0.001);
}
