public static string Classification(string path)
{
string rv = "";
string imagePath = Path.GetFullPath(path);
string modelPath = Path.GetFullPath("Diyari_IQD.h5");
string weightsPath = Path.GetFullPath("Diyari_IQD_weights.h5");
if (File.Exists(imagePath))
{
var img = ImageUtil.LoadImg(path, target_size: new Shape(64, 64));
NDarray x = ImageUtil.ImageToArray(img);
x = x.reshape(1, x.shape[0], x.shape[1], x.shape[2]);
var model = Sequential.LoadModel(modelPath);
model.LoadWeight(weightsPath);
var y = model.Predict(x);
y = y.argmax();
int index = y.asscalar <int>();
rv = labels[index];
}
else
{
throw (new Exception("No Image found at: " + imagePath));
}
return(rv);
}
public static string Predict(string imagePath)
{
var img = ImageUtil.LoadImg(imagePath, target_size: new Shape(32, 32));
NDarray x = ImageUtil.ImageToArray(img);
x = x.reshape(1, x.shape[0], x.shape[1], x.shape[2]);
var model = Sequential.LoadModel("model.h5");
model.LoadWeight("weights.h5");
var y = model.Predict(x);
y = y.argmax();
int index = y.asscalar <int>();
return(labels[index]);
}
public void Predict(string text, Accord.MachineLearning.TFIDF codebook, int max_news_len)
{
var model = Sequential.LoadModel("best_model_gru.h5");
string result = "";
string[] words = TextUtil.TextToWordSequence(text);
double[] tokens = codebook.Transform(words);
var newItem = tokens.Where(value => value != 0).ToArray();
NDarray x = np.array(newItem);
x = x.reshape(1, x.shape[0]);
x = SequenceUtil.PadSequences(x, maxlen: max_news_len, dtype: "double");
var y = model.Predict(x);
Console.WriteLine(y.str);
}
public static void Predict(string text)
{
var model = Sequential.LoadModel("model.h5");
string result = "";
var indexes = IMDB.GetWordIndex();
string[] words = TextUtil.TextToWordSequence(text);
float[] tokens = words.Select(i => ((float)indexes[i])).ToArray();
NDarray x = np.array(tokens);
x = x.reshape(1, x.shape[0]);
x = SequenceUtil.PadSequences(x, maxlen: 500);
var y = model.Predict(x);
var binary = Math.Round(y[0].asscalar <float>());
result = binary == 0 ? "Negative" : "Positive";
Console.WriteLine("Sentiment for \"{0}\": {1}", text, result);
}
public static void Predict(string text)
{
var model = Sequential.LoadModel("model.h5");
string result = "";
float[,] tokens = new float[1, max_words];
string[] words = TextUtil.TextToWordSequence(text).Take(max_words).ToArray();
for (int i = 0; i < words.Length; i++)
{
tokens[0, i] = indexesByFrequency.ContainsKey(words[i])
? (float)indexesByFrequency[words[i]] : 0f;
}
NDarray x = np.array(tokens);
var y = model.Predict(x);
var binary = Math.Round(y[0].asscalar <float>());
result = binary == 0 ? "Норм, не токсично" : "ТОКСИЧНО";
Console.WriteLine($"Результат для \"{text}\": {result}, оценка: {y[0].asscalar<float>()}");
}