Source code for

Build a deep neural network for regression predictions

import json
import spylunking.log.setup_logging as log_utils
import keras.models as keras_models
import keras.layers as keras_layers

log = log_utils.build_colorized_logger(

[docs]def build_regression_dnn( num_features, compile_config, model_json=None, model_config=None): """build_regression_dnn :param num_features: input_dim for the number of features in the data :param compile_config: dictionary of compile options :param model_json: keras model json to build the model :param model_config: optional dictionary for model """ model = None num_layers = 0 if model_json: f'loading from model_json={model_json}') model = keras_models.model_from_json( json.dumps(model_json)) elif model_config: model = keras_models.Sequential() f'building ' f'dnn num_features={num_features} ' f'model_config={model_config}') for idx, node in enumerate(model_config['layers']): layer_type = node.get( 'layer_type', 'dense').lower() if layer_type == 'dense': if num_layers == 0: model.add( keras_layers.Dense( int(node['num_neurons']), input_dim=num_features, kernel_initializer=node['init'], activation=node['activation'])) else: model.add( keras_layers.Dense( int(node['num_neurons']), kernel_initializer=node['init'], activation=node['activation'])) else: if layer_type == 'dropout': model.add( keras_layers.Dropout( float(node['rate']))) # end of supported model types num_layers += 1 # end of all layers else: # # noqa f'default dnn num_features={num_features}') model.add( keras_layers.Dense( 8, input_dim=num_features, kernel_initializer='normal', activation='relu')) model.add( keras_layers.Dense( 6, kernel_initializer='normal', activation='relu')) model.add( keras_layers.Dense( 1, kernel_initializer='normal')) # end of building a regression dnn # if model was defined if model: f'compiling={compile_config}') # compile the model loss = compile_config.get( 'loss', 'mse') optimizer = compile_config.get( 'optimizer', 'adam') metrics = compile_config.get( 'metrics', [ 'mse', 'mae', 'mape', 'cosine' ]) model.compile( loss=loss, optimizer=optimizer, metrics=metrics) else: log.error( f'failed building regression model={model}') # if could compile model return model
# end of build_regression_dnn