dlpy.model.Model

class dlpy.model.Model(conn, inputs=None, outputs=None, model_table=None, model_weights=None)

__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. One use case is the model creates weights table given a pre-trained weights and a pre-trained model. Example: mapper = model.create_layer_id_name_mapping() pretrained_weights_table = WeightsTable(conn, weights_tbl_name=’my_pretrained_weights_table’, model_tbl_name=’my_pretrained_model_table’) pretrained_weights_table.remap_layer_ids(mapper, casout=’new_weights).
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. *.prototxt), and convert the weights (e.g. *.caffemodel) to a SAS capable file (e.g. *.caffemodel.h5).
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.

Attributes

can_be_last_layer
feature_maps
layer_id
model_ever_trained
model_explain_table
n_epochs
name
number_of_instances
rnn_summary	Return a DataFrame containing the layer information for rnn models
score_message_level
src_layers
summary	Return a DataFrame containing the layer information
train_tbl
training_history
type
type_desc
type_label
valid_conf_mat
valid_res
valid_res_tbl
valid_score
valid_tbl