void generate_text(TextGeneratingTrainingEngine engine, DataInfo di)
{
var random = new Random(2018);
var start_index = (int)(random.NextDouble() * (di.text.Length - maxlen - 1));
var seed_generated_text = di.text.Substring(start_index, maxlen).Replace('\n', ' ');
Console.WriteLine($"\nSeed: {seed_generated_text}");
var temperatures = new double[] { 0.2, 0.5, 1.0, 1.2 };
foreach (var temperature in temperatures)
{
var generated_text = seed_generated_text;
for (int i = 0; i < 400; i++)
{
var sampled = generated_text.Select(v => (float)(di.char_indices[v])).ToArray();
var preds = engine.evaluate(new float[][] { sampled }, engine.softmaxOutput)[0].Take(di.chars.Length).ToArray();
var next_index = sample(random, preds, temperature);
var next_char = di.chars[next_index];
if (next_char == '\n')
{
next_char = ' ';
}
generated_text = generated_text.Substring(1) + next_char;
}
Console.WriteLine($"Randomly generated with temperature {temperature:F1}: {generated_text}");
}
}
void run()
{
var di = new DataInfo();
var engine = new TextGeneratingTrainingEngine()
{
num_epochs = 32,
batch_size = 128,
sequence_length = maxlen,
lossFunctionType = TrainingEngine.LossFunctionType.Custom,
accuracyFunctionType = TrainingEngine.AccuracyFunctionType.SameAsLoss,
metricType = TrainingEngine.MetricType.Loss
};
engine.setData(di.x, di.y, null, null);
engine.train();
generate_text(engine, di);
}
