/// <summary>
/// Decodes the predictions.
/// </summary>
/// <param name="preds">The preds.</param>
/// <param name="top">The top.</param>
/// <returns></returns>
public ImageNetPrediction[] DecodePredictions(NDarray preds, int top = 3)
{
Dictionary <string, object> parameters = new Dictionary <string, object>();
parameters["preds"] = preds;
parameters["top"] = top;
var predobj = (PyObject)InvokeStaticMethod(caller, "decode_predictions", parameters);
var d = predobj.ToString();
var list = TupleSolver.TupleToList <object>(predobj[0]);
List <ImageNetPrediction> predictions = new List <ImageNetPrediction>();
for (int i = 0; i < list.Length; i = i++)
{
ImageNetPrediction pred = new ImageNetPrediction()
{
WordID = list[i].ToString(),
Word = list[i + 1].ToString(),
PredictedValue = Convert.ToSingle(list[i + 2].ToString(), CultureInfo.InvariantCulture),
};
i = i + 3;
predictions.Add(pred);
}
return(predictions.ToArray());
}
/// <summary>
/// Loads the data.
/// </summary>
/// <param name="path">The path. to load text</param>
/// <param name="num_words">The number words.</param>
/// <param name="skip_top">Skip top number of records.</param>
/// <param name="maxlen">The max length.</param>
/// <param name="test_split">The test split.</param>
/// <param name="seed">The seed.</param>
/// <param name="start_char">The start character.</param>
/// <param name="oov_char">The oov character.</param>
/// <param name="index_from">The index from.</param>
/// <returns></returns>
public static ((NDarray, NDarray), (NDarray, NDarray)) LoadData(string path = "reuters.npz", int?num_words = null, int skip_top = 0, int?maxlen = null, float test_split = 0.2f,
int seed = 113, int start_char = 1, int oov_char = 2, int index_from = 3)
{
var dlist = TupleSolver.TupleToList(Instance.keras.datasets.reuters.load_data(path: path, num_words: num_words, skip_top: skip_top, maxlen: maxlen, test_split: test_split, seed: seed, start_char: start_char,
oov_char: oov_char, index_from: index_from));
return((dlist[0], dlist[1]), (dlist[2], dlist[3]));
}
public static (PyObject, string) NPRandom(int?seed = null)
{
Dictionary <string, object> parameters = new Dictionary <string, object>();
parameters["seed"] = seed;
var list = TupleSolver.TupleToList <object>((PyObject)InvokeStaticMethod(caller.seeding, "np_random", parameters));
return((PyObject)list[0], list[1].ToString());
}
/// <summary>
/// Decodes the predictions.
/// </summary>
/// <param name="preds">The preds.</param>
/// <param name="top">The top.</param>
/// <returns></returns>
public static ImageNetPrediction[] DecodePredictions(NDarray preds, int top = 3)
{
Dictionary <string, object> parameters = new Dictionary <string, object>();
parameters["preds"] = preds;
parameters["top"] = top;
var predobj = (PyObject)InvokeStaticMethod(Instance.keras.applications.resnet50, "decode_predictions", parameters);
var d = predobj.ToString();
var list = TupleSolver.TupleToList <object>(predobj);
return(null);
}
public virtual (float, object) ComputeReward(object achieved_goal, object desired_goal, Dictionary <string, object> info)
{
Dictionary <string, object> parameters = new Dictionary <string, object>();
parameters["achieved_goal"] = achieved_goal;
parameters["desired_goal"] = desired_goal;
parameters["info"] = info;
var py = InvokeMethod("compute_reward", parameters);
var list = TupleSolver.TupleToList <object>(py);
return((float)list[0], list[1]);
}
public virtual EnvResult Step(object action)
{
Dictionary <string, object> parameters = new Dictionary <string, object>();
parameters["action"] = action;
PyObject py = InvokeMethod("step", parameters);
var list = TupleSolver.TupleToList <object>(py);
EnvResult result = new EnvResult()
{
Observation = list[0],
Reward = Convert.ToSingle(list[1].ToString()),
Done = list[2].ToString() == "True" ? true : false,
Info = DictSolver.ToClr <object>(new PyDict((PyObject)list[3]))
};
return(result);
}
/// <summary>
/// Loads the data.
/// </summary>
/// <param name="path">The path.</param>
/// <returns></returns>
public static ((NDarray, NDarray), (NDarray, NDarray)) LoadData(string path = "mnist.npz")
{
var dlist = TupleSolver.TupleToList(Instance.keras.datasets.mnist.load_data(path: path));
return((dlist[0], dlist[1]), (dlist[2], dlist[3]));
}
/// <summary>
/// Loads the data.
/// </summary>
/// <returns></returns>
public static ((NDarray, NDarray), (NDarray, NDarray)) LoadData()
{
var dlist = TupleSolver.TupleToList(Instance.keras.datasets.fashion_mnist.load_data());
return((dlist[0], dlist[1]), (dlist[2], dlist[3]));
}
/// <summary>
/// Loads the data.
/// </summary>
/// <param name="label_mode">The label mode.</param>
/// <returns></returns>
public static ((NDarray, NDarray), (NDarray, NDarray)) LoadData(string label_mode = "fine")
{
var dlist = TupleSolver.TupleToList(Instance.keras.datasets.cifar10.load_data(label_mode: label_mode));
return((dlist[0], dlist[1]), (dlist[2], dlist[3]));
}
/// <summary>
/// Loads the data.
/// </summary>
/// <param name="path">The path.</param>
/// <param name="test_split">The test split.</param>
/// <param name="seed">The seed.</param>
/// <returns></returns>
public static ((NDarray, NDarray), (NDarray, NDarray)) LoadData(string path = "boston_housing.npz", float test_split = 0.2f, int seed = 113)
{
var dlist = TupleSolver.TupleToList(Instance.keras.datasets.boston_housing.load_data(path: path, test_split: test_split, seed: seed));
return((dlist[0], dlist[1]), (dlist[2], dlist[3]));
}
