Man pages for stschn/deepANN
Neural Network Toolbox
| accuracy | Classification accuracy |
| alexnet | AlexNet model |
| append_rows | Append dummy rows |
| as_ANN_matrix | Convert data into an ANN compatible matrix with only numbers |
| as_CNN_image_X | Create a 4-dimensional array for image features (input) |
| as_CNN_image_Y | Create a one-hot vector for image labels (output) |
| as_CNN_temp_X | Features (X) data format for a temporal CNN |
| as_CNN_temp_Y | Outcomes (Y) data format for a temporal CNN |
| as_images_array | Convert (resized) images to 3D arrays |
| as_images_tensor | Convert list of image arrays to a tensor |
| as_lag | Get ANN lag from ARIMA(X) lag |
| as_LSTM_data_frame | Recreation of a data frame based on preformatted X and Y data... |
| as_LSTM_period_outcome | Rebuild data frame |
| as_LSTM_X | Features (X) data format for LSTM |
| as_LSTM_Y | Outcomes (Y) data format for LSTM |
| as_MLP_X | Features (X) data format for SLP/MLP |
| as_MLP_Y | Outcomes (Y) data format for SLP/MLP |
| as_tensor_1D | Transform data into a 1D tensor |
| as_tensor_2D | Transform data into a 2D tensor. |
| as_tensor_3D | Transform data into a 3D tensor. |
| as_timesteps | Get ANN timesteps from ANN lag |
| backend | Backend |
| build_LSTM | Build LSTM architecture |
| build_MLP | Build SLP/MLP architecture |
| coerce_dimension | Coerce data to an array with no trailing dimension of 1 or to... |
| concatenate.factor | Concatenate two or more objects into a factor object |
| cross_entropy | Cross entropy |
| cross_validation_split | K-fold cross validation |
| data_split | Data split |
| day.name | Built-in Constants |
| decision_tree | Decision Tree |
| degree | Radian to degree |
| dice | Dice coefficient |
| diffinv_log | Invert a log-differenced vector |
| diffinv_percentage | Invert a percentage-differenced vector |
| diffinv_simple | Invert a simple-differenced vector |
| diff_log | Log-differencing of a numeric vector |
| diff_percentage | Percentage-differencing of a numeric vector |
| distance | Distance |
| dummify | Create dummy variables for categorical (nominal or ordinal)... |
| dummify_multilabel | Create dummy variables for multi-label columns |
| effectcoding | Effectcoding |
| Encoder-class | Class Encoder |
| entropy | Shannon entropy |
| erf | Error function (from MATLAB) |
| erfc | Complementary error function (from MATLAB) |
| erfcinv | Inverse complementary error function (from MATLAB) |
| erfinv | Inverse error function (from MATLAB) |
| fit_LSTM | Fit LSTM model |
| fit_MLP | Fit SLP/MLP model |
| get_LSTM_XY | Extract features (X) and outcome (Y) vector or matrix from... |
| get_period_shift | Period shift |
| get_season | Get season from given dates |
| gini_impurity | Gini impurity |
| huber_loss | Huber loss |
| images_load | Load images from different sources like from files or web |
| images_resize | Resize loaded images |
| inception_resnet_v2 | Inception-ResNet v2 model |
| inception_v3 | Inception v3 model |
| invert_differencing | Invert a differenced data series |
| iou | Intersection-over-Union (IoU, Jaccard Index) |
| k_nearest_neighbors | K-nearest neighbors |
| LabelBinarizer-class | class LabelBinarizer |
| LabelEncoder-class | class LabelEncoder |
| lags | Build a lagged data set or series |
| lenet5 | LeNet-5 model |
| list_as_numeric | Recursively transform all objects within a list to numeric... |
| load_weights_ANN | Load model weights from file |
| log_cosh_loss | Log-Cosh loss |
| mae | Mean absolute error (MAE) |
| mape | Mean absolute percentage error (MAPE) |
| MinMaxScaler-class | class MinMaxScaler |
| mobilenet | MobileNet model |
| mobilenet_v2 | MobileNetV2 model |
| mobilenet_v3 | MobileNetV3 model |
| moving_average | Weighted moving average |
| mse | Mean squared error (MSE) |
| msle | Mean squared logarithmic error (MSLE) |
| multi-assign | Multi-assign operator |
| MultiLabelBinarizer-class | class MultiLabelBinarizer |
| naive_bayes | Naive Bayes |
| naive_forecast | Naive forecasting |
| nasnet | NASNet-A model |
| nsamples | Number of samples within an array |
| nsubsequences | Number of subsequences within an array |
| ntimesteps | Number of timesteps within an array |
| nunits | Number of units within an array |
| one_hot_decode | One-hot decoding |
| one_hot_encode | One-hot encoding |
| OneHotEncoder-class | class OneHotEncoder |
| outlier | Definition and detection of outliers |
| outlier_dataset | Replace outliers in columns of a data set with 'NA' |
| Oversampler-class | OverSampler class |
| partition | Subset data set/time series into several slices |
| period | Subset data set/time series to specific periodically data |
| pipe | Pipe operator |
| predict_ANN | Predict with ANN model |
| predict.decisiontree | Prediction for Decision Tree |
| predict.kmeans | Prediction for kmeans |
| predict.naivebayes | Prediction for Naive Bayes |
| probability | Probability |
| quantile_loss | Quantile loss |
| radian | Degree to radian |
| RandomOversampler-class | RandomOverSampler class |
| random_seed | Random Number Generation with Tensorflow |
| RandomUndersampler-class | RadomUnderSampler class |
| re.factor | Renew an (ordered) factor object |
| remove_columns | Remove columns with only one specific value |
| resample_imbalanced | Resampling imbalanced data for classification problems |
| resnet | ResNet models |
| rmse | Root mean square error (RMSE) |
| rmsle | Root mean square logarithmic error (RMSLE) |
| rmspe | Root mean square percentage error (RMSPE) |
| sampler-class | Base Sampler class |
| sampler_Wrapper | Wrapper function for class Oversampler. |
| save_weights_ANN | Save model weights to file |
| scale_center | (Mean) Centering |
| scale_dataset | Scaling of a data set |
| scale_log | Log Transformation |
| scale_minmax | Min-Max Scaling |
| Scaler-class | Class Scaler |
| scale_train_test | Scaling of a train and test data set |
| scale_zscore | Z-Score Scaling |
| scaling | Scaling of a numeric object |
| sd_pop | Population standard deviation |
| similarity | Similarity |
| SMOTE-class | SMOTE class |
| sparse_encode | Sparse encoding |
| sse | Sum of squared errors (SSE) |
| StandardScaler-class | class StandardScaler |
| start_invert_differencing | Start row index/period for invert differencing |
| stationary | Build a stationary data series by differencing |
| stderror | Standard error |
| train_test_split | Data split |
| Undersampler-class | UnderSampler class |
| unet | U-Net model |
| unet3d | 3D U-Net model |
| var_pop | Population variance |
| vc | Variance coefficient (VC) |
| vector_as_ANN_matrix | Transform a vector into a ANN compatible matrix |
| vector_as_numeric | Transform a vector to a numeric vector |
| vgg | VGG models |
| wape | Weighted average percentage error (WAPE) |
| winsorize | Winsorize outliers |
| wmape | Weighted mean absolute percentage error (WMAPE) |
| xception | Xception model |
| zfnet | ZFNet model |
