| @@ -31,7 +31,7 @@ namespace Tensorflow.Keras.Datasets | |||||
| /// <param name="oov_char"></param> | /// <param name="oov_char"></param> | ||||
| /// <param name="index_from"></param> | /// <param name="index_from"></param> | ||||
| /// <returns></returns> | /// <returns></returns> | ||||
| public DatasetPass load_data(string path = "imdb.npz", | |||||
| public DatasetPass load_data(string? path = "imdb.npz", | |||||
| int num_words = -1, | int num_words = -1, | ||||
| int skip_top = 0, | int skip_top = 0, | ||||
| int maxlen = -1, | int maxlen = -1, | ||||
| @@ -42,7 +42,7 @@ namespace Tensorflow.Keras.Datasets | |||||
| { | { | ||||
| if (maxlen == -1) throw new InvalidArgumentError("maxlen must be assigned."); | if (maxlen == -1) throw new InvalidArgumentError("maxlen must be assigned."); | ||||
| var dst = Download(); | |||||
| var dst = path ?? Download(); | |||||
| var lines = File.ReadAllLines(Path.Combine(dst, "imdb_train.txt")); | var lines = File.ReadAllLines(Path.Combine(dst, "imdb_train.txt")); | ||||
| var x_train_string = new string[lines.Length]; | var x_train_string = new string[lines.Length]; | ||||
| @@ -55,7 +55,7 @@ namespace Tensorflow.Keras.Datasets | |||||
| var x_train = keras.preprocessing.sequence.pad_sequences(PraseData(x_train_string), maxlen: maxlen); | var x_train = keras.preprocessing.sequence.pad_sequences(PraseData(x_train_string), maxlen: maxlen); | ||||
| File.ReadAllLines(Path.Combine(dst, "imdb_test.txt")); | |||||
| lines = File.ReadAllLines(Path.Combine(dst, "imdb_test.txt")); | |||||
| var x_test_string = new string[lines.Length]; | var x_test_string = new string[lines.Length]; | ||||
| var y_test = np.zeros(new int[] { lines.Length }, np.int64); | var y_test = np.zeros(new int[] { lines.Length }, np.int64); | ||||
| for (int i = 0; i < lines.Length; i++) | for (int i = 0; i < lines.Length; i++) | ||||