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 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
}
// Training method and helper for a selectable SNN or CNN type network from a given config
// Resulting model and scores are stored for further use
public void Process(Config _config, int num_classes)
{
if (x_train == null || y_train == null || x_test == null || y_test == null)
{
throw new Exception("Dataset was null!");
}
dtStart = DateTime.Now;
config = _config;
Shape input_shape = null;
if (config.isCNN)
{
input_shape = GetShape();
}
if (config.isNormalize)
{
x_train = x_train.astype(np.float32);
x_test = x_test.astype(np.float32);
x_train /= 255;
x_test /= 255;
}
Console.WriteLine("Test Started {0}", dtStart);
Console.WriteLine("Network Type: {0} Neural Network", (!config.isCNN) ? "Simple" : "Convolution");
Console.WriteLine("Width: {0} Height: {1} Size:{2}", width, height, width * height);
Console.WriteLine("x_train shape: {0} x_train samples: {1} x_test shape: {2} x_test samples: {3}",
x_train.shape, x_train.shape[0], x_test.shape, x_test.shape[0]);
y_train = Util.ToCategorical(y_train, num_classes);
y_test = Util.ToCategorical(y_test, num_classes);
starttime = Utils.GetDateTime_Formatted();
log_file = Utils.GetFileWithExtension(log_dir, starttime, log_ext);
if (!config.isCNN)
{
model = ProcessSnnModel(x_train, y_train, x_test, y_test, num_classes, log_file, config);
}
else
{
model = ProcessCnnModel(input_shape, x_train, y_train, x_test, y_test, num_classes, log_file, config);
}
dtEnd = DateTime.Now;
// Score the model for performance
score = model.Evaluate(x_test, y_test, verbose: 0);
TimeSpan ts = dtEnd - dtStart;
model.Summary();
Console.WriteLine("Test End: {0} Duration: {1}:{2}.{3}", dtEnd, ts.Hours, ts.Minutes, ts.Seconds);
Console.WriteLine("Loss: {0} Accuracy: {1}", score[0], score[1]);
}
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
}
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");
}
private (History, Sequential, Dictionary <string, int>) LearnNeuralNetwork(string trainCsvPath, int num_words, int max_news_len, int nb_classes)
{
NDarray x_train = null;
NDarray y_train = null;
var trainCSV = Frame.ReadCsv(trainCsvPath, false, separators: ";");
var trainYFloat = trainCSV.Rows.Select(kvp => { return(kvp.Value.GetAs <float>("Column1")); }).ValuesAll.ToList();
var trainXString = trainCSV.Rows.Select(kvp => { return(kvp.Value.GetAs <string>("Column2")); }).ValuesAll.ToList();
var trainXStringArray = trainXString.ToArray();
//x_train = np.array(new float[,] { { 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 } });
y_train = np.array(trainYFloat.ToArray());
y_train = Util.ToCategorical(y_train, nb_classes);
string[][] tokens = trainXStringArray.Tokenize();
var dictionaryLikeIMDB = FrequencyDictionary.Learn(tokens);
var bow = FrequencyDictionary.Transform(tokens, dictionaryLikeIMDB);
// Create a new TF-IDF with options:
/*var codebook = new Accord.MachineLearning.TFIDF()
* {
* Tf = TermFrequency.Log,
* Idf = InverseDocumentFrequency.Default
* };
*
* codebook.Learn(tokens);
*
* double[][] bow = codebook.Transform(tokens);*/
var list = new List <NDarray>();
foreach (var item in bow)
{
//var newItem = item.Take(max_news_len).ToArray();
//var ndarray = np.array(newItem);
var ndarray = np.array(item);
list.Add(ndarray);
}
var sequences = np.array(list);
//x_train = SequenceUtil.PadSequences(sequences, maxlen: max_news_len, dtype: "double");
x_train = SequenceUtil.PadSequences(sequences, maxlen: max_news_len);
var model = new Sequential();
model.Add(new Embedding(num_words, 32, null, null, null, null, false, max_news_len));
model.Add(new GRU(138));//16
model.Add(new Dense(12, activation: "softmax"));
model.Compile(optimizer: "adam", loss: "categorical_crossentropy", metrics: new string[] { "accuracy" });
model.Summary();
var model_gru_save_path = "best_model_gru.h5";
var checkpoint_callback_gru = new ModelCheckpoint(
model_gru_save_path,
"val_accuracy",
1,
true
);
var callbacks = new List <Callback>()
{
checkpoint_callback_gru
};
float validation_split = (float)0.1;
var history_gru = model.Fit(x_train,
y_train,
batch_size: 128,
epochs: 10,
validation_split: validation_split,
callbacks: callbacks.ToArray());
//Save model and weights
string json = model.ToJson();
File.WriteAllText("model.json", json);
return(history_gru, model, dictionaryLikeIMDB);
}