public static void FitMnist()
{
var model = new Sequential();
model.Add(new Conv2D(32, kernelSize: new int[] { 3, 3 }, inputShape: new int[] { 28, 28, 1 }, activation: "relu"));
model.Add(new Conv2D(64, kernelSize: new int[] { 3, 3 }, activation: "relu"));
// model.Add(new MaxPooling1D(poolSize: 2));
model.Add(new MaxPooling2D(poolSize: new int[] { 2, 2 }));
model.Add(new Dropout(0.25));
model.Add(new Flatten());
model.Add(new Dense(128, activation: "relu"));
model.Add(new Dropout(0.5));
model.Add(new Dense(10, activation: "softmax"));
var optimizer = new SGD(lr: 0.01);
model.Compile("categorical_crossentropy", optimizer, new string[] { "accuracy" });
var xtrain = TensorUtils.Deserialize(new FileStream(GetDataPath("datasets/nda_mnist/mnist_xtrain.nda"), FileMode.Open));
var ytrain = TensorUtils.Deserialize(new FileStream(GetDataPath("datasets/nda_mnist/mnist_ytrain.nda"), FileMode.Open));
xtrain = xtrain.Cast(DType.Float32);
xtrain = Ops.Div(null, xtrain, 255f);
ytrain = ytrain.Cast(DType.Float32);
model.Fit(xtrain, ytrain, batchSize: 128, epochs: 12);
var stream = new FileStream("c:/ttt/mnist.model", FileMode.OpenOrCreate, FileAccess.Write);
stream.SetLength(0);
model.Save(stream);
}
public static void Train()
{
var model = new Sequential();
// embedding layer
model.Add(new Embedding(output, 100, input_length: 32));
model.Add(new Conv1D(64, 3, padding: "causal", activation: "tanh"));
model.Add(new Dropout(0.2));
model.Add(new MaxPooling1D(2));
model.Add(new Conv1D(128, 3, activation: "relu", dilation_rate: 2, padding: "causal"));
model.Add(new Dropout(0.2));
model.Add(new MaxPooling1D(2));
model.Add(new Conv1D(256, 3, activation: "relu", dilation_rate: 4, padding: "causal"));
model.Add(new Dropout(0.2));
model.Add(new MaxPooling1D(2));
//model.Add(new Conv1D(256, 5, activation: "relu"));
model.Add(new GlobalMaxPooling1D());
model.Add(new Dense(256, activation: "relu"));
model.Add(new Dense(output, activation: "softmax"));
model.Compile(loss: "sparse_categorical_crossentropy", optimizer: new Adam());
model.Summary();
var mc = new ModelCheckpoint("best_model.h5", monitor: "val_loss", mode: "min", save_best_only: true, verbose: 1);
var history = model.Fit(train_x, train_y, batch_size: 32, epochs: 100, validation_split: 0.25f, verbose: 1, callbacks: new Callback[] { mc });
model.Save("last_epoch.h5");
}
public static void FitMnistSimple()
{
var model = new Sequential();
model.Add(new Dense(512, activation: "relu", inputShape: new int[] { 784 }));
model.Add(new Dropout(0.2));
model.Add(new Dense(512, activation: "relu"));
model.Add(new Dropout(0.2));
model.Add(new Dense(10, activation: "softmax"));
var optimizer = new SGD(lr: 0.01);
model.Compile("categorical_crossentropy", optimizer, new string[] { "accuracy" });
var xtrain = TensorUtils.Deserialize(new FileStream(GetDataPath("datasets/nda_mnist/mnist_xtrain.nda"), FileMode.Open));
var ytrain = TensorUtils.Deserialize(new FileStream(GetDataPath("datasets/nda_mnist/mnist_ytrain.nda"), FileMode.Open));
xtrain = xtrain.Cast(DType.Float32);
xtrain = Ops.Div(null, xtrain, 255f);
ytrain = ytrain.Cast(DType.Float32);
model.Fit(xtrain, ytrain, batchSize: 128, epochs: 20);
var stream = new FileStream("c:/ttt/mnist-simple.model", FileMode.OpenOrCreate, FileAccess.Write);
stream.SetLength(0);
model.Save(stream);
}
private void PatchInstructorsNotes(EdxCourse edxCourse, Course ulearnCourse, string olxPath)
{
var ulearnUnits = ulearnCourse.Units;
foreach (var chapter in edxCourse.CourseWithChapters.Chapters)
{
var chapterUnit = ulearnCourse.Units.FirstOrDefault(u => u.Title == chapter.DisplayName);
var chapterNote = chapterUnit?.InstructorNote;
if (chapterUnit == null || chapterNote == null)
{
continue;
}
var unitIndex = ulearnUnits.IndexOf(chapterUnit);
var displayName = "Заметки преподавателю";
var sequentialId = $"{ulearnCourse.Id}-{unitIndex}-note-seq";
var verticalId = $"{ulearnCourse.Id}-{unitIndex}-note-vert";
var mdBlockId = $"{ulearnCourse.Id}-{unitIndex}-note-md";
var sequentialNote = new Sequential(sequentialId, displayName,
new[]
{
new Vertical(verticalId, displayName, new[] { new MdBlock(chapterNote.Markdown).ToEdxComponent(mdBlockId, displayName, chapterUnit.Directory.FullName) })
})
{
VisibleToStaffOnly = true
};
if (!File.Exists($"{olxPath}/sequential/{sequentialNote.UrlName}.xml"))
{
var sequentials = chapter.Sequentials.ToList();
sequentials.Add(sequentialNote);
new Chapter(chapter.UrlName, chapter.DisplayName, chapter.Start, sequentials.ToArray()).Save(olxPath);
}
sequentialNote.Save(olxPath);
}
}
public static void Run()
{
//var ((x_train, y_train), (x_test, y_test)) = IMDB.LoadData(num_words: top_words);
var((x_train, y_train), (x_test, y_test)) = LoadDataRussianLanguageToxicComments(
trainCount: train_count, testCount: test_count, numWords: top_words, maxWords: max_words);
//Не нужно массивы дополнять до 500 элементов, т.к. они уже размером в 500 элементов
//x_train = SequenceUtil.PadSequences(x_train, maxlen: max_words);
//x_test = SequenceUtil.PadSequences(x_test, maxlen: max_words);
//Create model
Sequential model = new Sequential();
model.Add(new Embedding(top_words, 32, input_length: max_words));
model.Add(new Conv1D(filters: 32, kernel_size: 3, padding: "same", activation: "relu"));
model.Add(new MaxPooling1D(pool_size: 2));
model.Add(new Flatten());
model.Add(new Dense(250, activation: "relu"));
model.Add(new Dense(1, activation: "sigmoid"));
model.Compile(loss: "binary_crossentropy", optimizer: "adam", metrics: new string[] { "accuracy" });
model.Summary();
// Fit the model
model.Fit(x_train, y_train, validation_data: new NDarray[] { x_test, y_test },
epochs: 2 /*10*/, batch_size: 128, verbose: 2);
// Final evaluation of the model
var scores = model.Evaluate(x_test, y_test, verbose: 0);
Console.WriteLine("Accuracy: " + (scores[1] * 100));
model.Save("model.h5");
File.WriteAllText("model.json", model.ToJson()); //save model
//model.SaveTensorflowJSFormat("./"); //error - Cannot perform runtime binding on a null reference
}
private void PatchInstructorsNotes(EdxCourse edxCourse, Course ulearnCourse, string olxPath)
{
var ulearnUnits = ulearnCourse.GetUnits().ToList();
foreach (var chapter in edxCourse.CourseWithChapters.Chapters)
{
var chapterNote = ulearnCourse.InstructorNotes.FirstOrDefault(x => x.UnitName == chapter.DisplayName);
if (chapterNote == null)
{
continue;
}
var unitIndex = ulearnUnits.IndexOf(chapterNote.UnitName);
var displayName = "Заметки преподавателю";
var sequentialId = string.Format("{0}-{1}-{2}", ulearnCourse.Id, unitIndex, "note-seq");
var verticalId = string.Format("{0}-{1}-{2}", ulearnCourse.Id, unitIndex, "note-vert");
var mdBlockId = string.Format("{0}-{1}-{2}", ulearnCourse.Id, unitIndex, "note-md");
var sequentialNote = new Sequential(sequentialId, displayName,
new[]
{
new Vertical(verticalId, displayName, new[] { new MdBlock(chapterNote.Markdown).ToEdxComponent(mdBlockId, displayName, ulearnCourse.GetDirectoryByUnitName(chapterNote.UnitName)) })
})
{
VisibleToStaffOnly = true
};
if (!File.Exists(string.Format("{0}/sequential/{1}.xml", olxPath, sequentialNote.UrlName)))
{
var sequentials = chapter.Sequentials.ToList();
sequentials.Add(sequentialNote);
new Chapter(chapter.UrlName, chapter.DisplayName, chapter.Start, sequentials.ToArray()).Save(olxPath);
}
sequentialNote.Save(olxPath);
}
}
public static void Run()
{
//Load IMDb dataset
var((x_train, y_train), (x_test, y_test)) = IMDB.LoadData();
var X = np.concatenate(new NDarray[] { x_train, x_test }, axis: 0);
var Y = np.concatenate(new NDarray[] { y_train, y_test }, axis: 0);
Console.WriteLine("Shape of X: " + X.shape);
Console.WriteLine("Shape of Y: " + Y.shape);
//We can get an idea of the total number of unique words in the dataset.
Console.WriteLine("Number of words: ");
var hstack = np.hstack(new NDarray[] { X });
//var unique = hstack.unique();
//Console.WriteLine(np.unique(np.hstack(new NDarray[] { X })).Item1);
// Load the dataset but only keep the top n words, zero the rest
int top_words = 1000;// 5000;
((x_train, y_train), (x_test, y_test)) = IMDB.LoadData(num_words: top_words);
int max_words = 500;
x_train = SequenceUtil.PadSequences(x_train, maxlen: max_words);
x_test = SequenceUtil.PadSequences(x_test, maxlen: max_words);
//Create model
Sequential model = new Sequential();
model.Add(new Embedding(top_words, 32, input_length: max_words));
model.Add(new Conv1D(filters: 32, kernel_size: 3, padding: "same", activation: "relu"));
model.Add(new MaxPooling1D(pool_size: 2));
model.Add(new Flatten());
model.Add(new Dense(250, activation: "relu"));
model.Add(new Dense(1, activation: "sigmoid"));
model.Compile(loss: "binary_crossentropy", optimizer: "adam", metrics: new string[] { "accuracy" });
model.Summary();
// Fit the model
model.Fit(x_train, y_train, validation_data: new NDarray[] { x_test, y_test },
epochs: 1 /*10*/, batch_size: 128, verbose: 2);
// Final evaluation of the model
var scores = model.Evaluate(x_test, y_test, verbose: 0);
Console.WriteLine("Accuracy: " + (scores[1] * 100));
model.Save("model.h5");
File.WriteAllText("model.json", model.ToJson()); //save model
//model.SaveTensorflowJSFormat("./"); //error - Cannot perform runtime binding on a null reference
}
static void SummarizePerformance(int epoch, Sequential generator, Sequential discriminator, NDarray dataset, int latentDim, int sampleCount = 50)
{
var real = GenerateRealSamples(dataset, sampleCount);
var realAcc = discriminator.Evaluate(real.Item1, real.Item2, verbose: 0);
var fake = GenerateFakeGeneratorSamples(generator, latentDim, sampleCount);
var fakeAcc = discriminator.Evaluate(fake.Item1, fake.Item2, verbose: 0);
Console.WriteLine("Accuracy real: \t " + realAcc.Last() * 100 + "% \t fake:" + fakeAcc.Last() * 100 + "%");
var fakes = fake.Item1;
fakes = (fakes + 1) / 2;
for (int i = 0; i < sampleCount; i++)
{
SaveArrayAsImage(fakes[i], "output/gantest_" + epoch + "_" + i + ".png");
}
generator.Save("output/generator" + epoch + ".h5");
}
public static void BuildAndTrain()
{
//Model to hold the neural network architecture which in this case is WaveNet
var model = new Sequential();
// Starts with embedding layer
model.Add(new Embedding(output, 100, input_length: 32));
model.Add(new Conv1D(64, 3, padding: "causal", activation: "tanh"));
model.Add(new Dropout(0.2));
model.Add(new MaxPooling1D(2));
model.Add(new Conv1D(128, 3, activation: "relu", dilation_rate: 2, padding: "causal"));
model.Add(new Dropout(0.2));
model.Add(new MaxPooling1D(2));
model.Add(new Conv1D(256, 3, activation: "relu", dilation_rate: 4, padding: "causal"));
model.Add(new Dropout(0.2));
model.Add(new MaxPooling1D(2));
//model.Add(new Conv1D(256, 5, activation: "relu"));
model.Add(new GlobalMaxPooling1D());
model.Add(new Dense(256, activation: "relu"));
model.Add(new Dense(output, activation: "softmax"));
// Compile with Adam optimizer
model.Compile(loss: "sparse_categorical_crossentropy", optimizer: new Adam());
model.Summary();
// Callback to store the best trained model
var mc = new ModelCheckpoint("best_model.h5", monitor: "val_loss", mode: "min", save_best_only: true, verbose: 1);
//Method to actually train the model for 100 iteration
var history = model.Fit(train_x, train_y, batch_size: 32, epochs: 100, validation_split: 0.25f, verbose: 1, callbacks: new Callback[] { mc });
// Save the final trained model which we are going to use for prediction
model.Save("last_epoch.h5");
}
static void Main(string[] args)
{
//Mock data fix the file address
NDarray dataset = Numpy.np.loadtxt(fname: "C:\\Natan\\csharp\\trial11\\pima-indians-diabetes.csv", delimiter: ",");
var X = dataset[":,0: 8"];
var Y = dataset[":, 8"];
var model = new Sequential();
model.Add(new Dense(12, input_dim: 8, kernel_initializer: "uniform", activation: "relu"));
model.Add(new Dense(8, kernel_initializer: "uniform", activation: "relu"));
model.Add(new Dense(1, activation: "sigmoid"));
model.Compile(optimizer: "adam", loss: "binary_crossentropy", metrics: new string[] { "accuracy" });
model.Fit(X, Y, batch_size: 10, epochs: 150, verbose: 1);
model.Save("modelAA.h5");
double[] scores = model.Evaluate(X, Y);
foreach (double sc in scores)
{
Console.WriteLine(sc);
//Console.WriteLine(scmodel..metrics_names[1], scores[1] * 100))
}
Console.WriteLine("Hello World! we learned");
Console.ReadKey();
}
static void Main(string[] args)
{
int batch_size = 128; //Training batch size
int num_classes = 10; //No. of classes
int epochs = 12; //No. of epoches we will train
// input image dimensions
int img_rows = 28, img_cols = 28;
// Declare the input shape for the network
Shape input_shape = null;
// Load the MNIST dataset into Numpy array
var((x_train, y_train), (x_test, y_test)) = MNIST.LoadData();
//Check if its channel fist or last and rearrange the dataset accordingly
if (K.ImageDataFormat() == "channels_first")
{
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols);
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols);
input_shape = (1, img_rows, img_cols);
}
else
{
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1);
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1);
input_shape = (img_rows, img_cols, 1);
}
//Normalize the input data
x_train = x_train.astype(np.float32);
x_test = x_test.astype(np.float32);
x_train /= 255;
x_test /= 255;
Console.WriteLine("x_train shape: " + x_train.shape);
Console.WriteLine(x_train.shape[0] + " train samples");
Console.WriteLine(x_test.shape[0] + " test samples");
// Convert class vectors to binary class matrices
y_train = Util.ToCategorical(y_train, num_classes);
y_test = Util.ToCategorical(y_test, num_classes);
// Build CNN model
var model = new Sequential();
model.Add(new Conv2D(32, kernel_size: (3, 3).ToTuple(),
activation: "relu",
input_shape: input_shape));
model.Add(new Conv2D(64, (3, 3).ToTuple(), activation: "relu"));
model.Add(new MaxPooling2D(pool_size: (2, 2).ToTuple()));
model.Add(new Dropout(0.25));
model.Add(new Flatten());
model.Add(new Dense(128, activation: "relu"));
model.Add(new Dropout(0.5));
model.Add(new Dense(num_classes, activation: "softmax"));
//Compile with loss, metrics and optimizer
model.Compile(loss: "categorical_crossentropy",
optimizer: new Adadelta(), metrics: new string[] { "accuracy" });
//Train the model
model.Fit(x_train, y_train,
batch_size: batch_size,
epochs: epochs,
verbose: 1,
validation_data: new NDarray[] { x_test, y_test });
//Score the model for performance
var score = model.Evaluate(x_test, y_test, verbose: 0);
Console.WriteLine("Test loss:" + score[0]);
Console.WriteLine("Test accuracy:" + score[1]);
// Save the model to HDF5 format which can be loaded later or ported to other application
model.Save("model.h5");
// Save it to Tensorflow JS format and we will test it in browser.
var v = K.Instance;
//model.SaveTensorflowJSFormat(@"C:\_temp\");
//model.SaveOnnx(@"C:\_temp\");
Console.ReadLine();
}
public static void Run()
{
//Console.WriteLine(modelPath);
//Console.WriteLine(h5Path);
int batch_size = 200;
int num_classes = 10;
int epochs = 10;
// input image dimensions
int img_rows = 28, img_cols = 28;
Shape input_shape = null;
// the data, split between train and test sets
var((x_train, y_train), (x_test, y_test)) = MNIST.LoadData();
if (K.ImageDataFormat() == "channels_first")
{
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols);
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols);
input_shape = (1, img_rows, img_cols);
}
else
{
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1);
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1);
input_shape = (img_rows, img_cols, 1);
}
x_train = x_train.astype(np.float32);
x_test = x_test.astype(np.float32);
x_train /= 255;
x_test /= 255;
Console.WriteLine("x_train shape: " + x_train.shape);
Console.WriteLine(x_train.shape[0] + " train samples");
Console.WriteLine(x_test.shape[0] + " test samples");
// convert class vectors to binary class matrices
y_train = Util.ToCategorical(y_train, num_classes);
y_test = Util.ToCategorical(y_test, num_classes);
// Build CNN model
var model = new Sequential();
model.Add(new Conv2D(32, kernel_size: (3, 3).ToTuple(),
activation: "relu",
input_shape: input_shape));
model.Add(new Conv2D(64, (3, 3).ToTuple(), activation: "relu"));
model.Add(new MaxPooling2D(pool_size: (2, 2).ToTuple()));
model.Add(new Dropout(0.25));
model.Add(new Flatten());
model.Add(new Dense(128, activation: "relu"));
model.Add(new Dropout(0.5));
model.Add(new Dense(num_classes, activation: "softmax"));
model.Compile(loss: "categorical_crossentropy",
optimizer: new Adadelta(), metrics: new string[] { "accuracy" });
model.Fit(x_train, y_train,
batch_size: batch_size,
epochs: epochs,
verbose: 1,
validation_data: new NDarray[] { x_test, y_test });
model.Save(h5Path);
model.SaveTensorflowJSFormat(modelPath);
//model.SaveOnnx("model.onnx");
var score = model.Evaluate(x_test, y_test, verbose: 0);
Console.WriteLine("Test loss:" + score[0]);
Console.WriteLine("Test accuracy:" + score[1]);
}
public static void Run()
{
int batch_size = 128;
int num_classes = 10;
int epochs = 100;
// the data, split between train and test sets
var((x_train, y_train), (x_test, y_test)) = Cifar10.LoadData();
Console.WriteLine("x_train shape: " + x_train.shape);
Console.WriteLine(x_train.shape[0] + " train samples");
Console.WriteLine(x_test.shape[0] + " test samples");
// convert class vectors to binary class matrices
y_train = Util.ToCategorical(y_train, num_classes);
y_test = Util.ToCategorical(y_test, num_classes);
// Build CNN model
var model = new Sequential();
model.Add(new Conv2D(32, kernel_size: (3, 3).ToTuple(),
padding: "same",
input_shape: new Shape(32, 32, 3)));
model.Add(new Activation("relu"));
model.Add(new Conv2D(32, (3, 3).ToTuple()));
model.Add(new Activation("relu"));
model.Add(new MaxPooling2D(pool_size: (2, 2).ToTuple()));
model.Add(new Dropout(0.25));
model.Add(new Conv2D(64, kernel_size: (3, 3).ToTuple(),
padding: "same"));
model.Add(new Activation("relu"));
model.Add(new Conv2D(64, (3, 3).ToTuple()));
model.Add(new Activation("relu"));
model.Add(new MaxPooling2D(pool_size: (2, 2).ToTuple()));
model.Add(new Dropout(0.25));
model.Add(new Flatten());
model.Add(new Dense(512));
model.Add(new Activation("relu"));
model.Add(new Dropout(0.5));
model.Add(new Dense(num_classes));
model.Add(new Activation("softmax"));
model.Compile(loss: "categorical_crossentropy",
optimizer: new RMSprop(lr: 0.0001f, decay: 1e-6f), metrics: new string[] { "accuracy" });
x_train = x_train.astype(np.float32);
x_test = x_test.astype(np.float32);
x_train /= 255;
x_test /= 255;
model.Fit(x_train, y_train,
batch_size: batch_size,
epochs: epochs,
verbose: 1,
validation_data: new NDarray[] { x_test, y_test },
shuffle: true);
//Save model and weights
//string model_path = "./model.json";
//string weight_path = "./weights.h5";
//string json = model.ToJson();
//File.WriteAllText(model_path, json);
//model.SaveWeight(weight_path);
model.Save("model.h5");
model.SaveTensorflowJSFormat("./");
//Score trained model.
var score = model.Evaluate(x_test, y_test, verbose: 0);
Console.WriteLine("Test loss:" + score[0]);
Console.WriteLine("Test accuracy:" + score[1]);
}