public static void Main(string[] args)
{
// CreateHostBuilder(args).Build().Run();
//CreateWebHostBuilder(args).Build().Run();
Sequential Seq = new Sequential();
Seq.Add(new Dense(32, activation: "relu", input_shape: new Shape(250, 250, 3)));
Seq.Add(new Dense(64, activation: "relu"));
Seq.Add(new Dense(1, activation: "sigmoid"));
Console.WriteLine(Backend.GetBackend());
var function = Backend.Function(Seq.Layers(0), Seq.Layers(1));
Console.WriteLine(function);
NDarray x = np.array(new float[, ] {
{ 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 }
});
NDarray y = np.array(new float[] { 0, 1, 1, 0 });
var val = (42, 2);
// var data = tf.ones(new TensorShape(new int[] {1, 259, 259, 3}));
var data = Backend.ones();
KerasIterator iter = new KerasIterator(data);
var z = new PyIter(iter.PyObject);
z.MoveNext();
var output = z.Current;
var res = function(data);
Console.WriteLine("function Results:");
Console.WriteLine(res);
}
private Sequential FitAndEvaluate(Sequential newModel, string bestWeightsFile)
{
int imgHeight = 75;
int trainSamples = 31980;
int testSamples = 4420;
int epochs = 20;
int batchSize = 26;
string trainDirectory = "E:\\Projects\\MLProject1\\MLProject1\\bin\\Debug\\data\\Train";
string testDirectory = "E:\\Projects\\MLProject1\\MLProject1\\bin\\Debug\\data\\Test";
string validationDirectory = "E:\\Projects\\MLProject1\\MLProject1\\bin\\Debug\\data\\Valid";
ImageDataGenerator generator = new ImageDataGenerator(rescale: (float)(1.00 / 255.00));
//load and iterate training dataset
KerasIterator trainIterator = generator.FlowFromDirectory(trainDirectory,
class_mode: "categorical",
target_size: new Tuple <int, int>(imgHeight, imgHeight),
batch_size: batchSize);
//color_mode:"grayscale");
// load and iterate testing dataset
KerasIterator testIterator = generator.FlowFromDirectory(testDirectory,
class_mode: "categorical",
target_size: new Tuple <int, int>(imgHeight, imgHeight),
batch_size: batchSize);
//color_mode: "grayscale");
KerasIterator validationIterator = generator.FlowFromDirectory(validationDirectory,
class_mode: "categorical",
target_size: new Tuple <int, int>(imgHeight, imgHeight),
batch_size: batchSize);
ModelCheckpoint checkpoint = new ModelCheckpoint(filepath: bestWeightsFile,
monitor: "val_accuracy",
verbose: 1,
save_best_only: true);
newModel.FitGenerator(trainIterator, steps_per_epoch: trainSamples / batchSize, epochs: epochs, verbose: 1,
validation_data: validationIterator, callbacks: new Callback[] { checkpoint });
//evaluate model
double[] loss = newModel.EvaluateGenerator(testIterator, steps: testSamples / batchSize);
foreach (double d in loss)
{
Console.Write(d + " ");
}
return(newModel);
}