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);
}