dlpy.model.Model(conn, inputs=None, outputs=None, model_table=None, model_weights=None)¶Bases: dlpy.network.Network
__init__(conn, inputs=None, outputs=None, model_table=None, model_weights=None)¶Initialize self. See help(type(self)) for accurate signature.
Methods
| __init__(conn[, inputs, outputs, …]) | Initialize self. |
| change_labels(label_file, id_column, …) | Overrides the labels already in the model |
| compile() | parse the network nodes and process CAS Action |
| count_instances() | |
| count_params() | Count the total number of parameters in the model |
| create_layer_id_name_mapping() | Create a dictionary which maps layer id to layer name. |
| deploy(path[, output_format, model_weights, …]) | Deploy the deep learning model to a data file |
| evaluate(data[, text_parms, layer_out, …]) | Evaluate the deep learning model on a specified validation data set |
| evaluate_object_detection(ground_truth, …) | Evaluate the deep learning model on a specified validation data set. |
| fit(data[, inputs, target, data_specs, …]) | Fitting a deep learning model. |
| fit_and_visualize(data[, inputs, target, …]) | Fitting a deep learning model while visulizing the fit and loss at each iteration. |
| forecast([test_table, horizon, train_table, …]) | Make forecasts based on deep learning models trained on TimeseriesTable. |
| format_name([block_num, local_count]) | Format the name of the layer |
| from_caffe_model(conn, input_network_file[, …]) | Generate a model object from a Caffe model proto file (e.g. |
| from_keras_model(conn, keras_model[, …]) | Generate a model object from a Keras model object |
| from_onnx_model(conn, onnx_model[, …]) | Generate a Model object from ONNX model. |
| from_sashdat(conn, path[, output_model_table]) | Generate a model object using the model information in the sashdat file |
| from_table(input_model_table[, …]) | Create a Model object from CAS table that defines a deep learning model |
| get_feature_maps(data[, label, idx, image_id]) | Extract the feature maps for a single image |
| get_features(data, dense_layer[, target]) | Extract linear features for a data table from the layer specified by dense_layer |
| get_model_info() | Return the information about the model table |
| get_number_of_instances() | |
| heat_map_analysis([data, mask_width, …]) | Conduct a heat map analysis on table of images |
| load(path[, display_note]) | Load the deep learning model architecture from existing table |
| load_weights(path[, labels, data_spec, …]) | Load the weights from a data file specified by ‘path’ |
| load_weights_attr(path) | Load the weights attribute form a sashdat file |
| load_weights_from_caffe(path[, labels, …]) | Load the model weights from a HDF5 file |
| load_weights_from_file(path[, format_type, …]) | Load the model weights from a HDF5 file |
| load_weights_from_file_with_labels(path[, …]) | Load the model weights from a HDF5 file |
| load_weights_from_keras(path[, labels, …]) | Load the model weights from a HDF5 file |
| load_weights_from_table(path) | Load the weights from a file |
| plot_evaluate_res([cas_table, img_type, …]) | Plot the bar chart of the classification predictions |
| plot_heat_map([idx, alpha]) | Display the heat maps analysis results |
| plot_network() | Display a graph that summarizes the model architecture. |
| plot_training_history([items, fig_size, …]) | Display the training iteration history. |
| predict(data[, text_parms, layer_out, …]) | Evaluate the deep learning model on a specified validation data set |
| print_summary() | Display a table that summarizes the model architecture |
| save_to_astore([path, layers]) | Save the model to an astore object, and write it into a file. |
| save_to_onnx(path[, model_weights]) | Save to ONNX model |
| save_to_table(path) | Save the model as SAS dataset |
| save_to_table_with_caslibify(path) | Save the model as SAS dataset |
| save_weights_csv(path) | Save model weights table as csv |
| score(table[, model, init_weights, …]) | Inference of input data with the trained deep learning model |
| set_weights(weight_tbl) | Assign weights to the Model object |
| set_weights_attr(attr_tbl[, clear]) | Attach the weights attribute to the model weights |
| share_weights(layers) | Share weights between layers |
| to_functional_model([stop_layers]) | Convert a Sequential into a functional model and return the functional model. |
| to_model_params() | Convert the model configuration to CAS action parameters |
| train(table[, attributes, inputs, nominals, …]) | Trains a deep learning model |
| tune(data[, inputs, target]) | Tunes hyper parameters for the deep learning model. |