datasets.py 文件源码

def __init__(self, X_train, y_train, X_test, y_test, categorical=True):
        self._x_train = X_train
        self._x_test = X_test

        # are the targets to be made one hot vectors
        if categorical:
            self._y_train = np_utils.to_categorical(y_train)
            self._y_test = np_utils.to_categorical(y_test)
            self._output_size = self._y_train.shape[1]

        # handle sparse output classification
        elif issubclass(y_train.dtype.type, np.integer):
            self._y_train = y_train
            self._y_test = y_test
            self._output_size = self._y_train.max() + 1  # assume 0 based indexes

        # not classification, just copy them
        else:
            self._y_train = y_train
            self._y_test = y_test
            self._output_size = self._y_train.shape[1]