def train(output_graphs, data=None, note=None): print("Training data, generating graphs: %r" % output_graphs) _run_trainer() _generate_parsed_logs() (training_details, validation_details) = _parse_logs() _move_trained_weight_file() if output_graphs: graph.plot_results(training_details, validation_details, note) predict.test_validation()
def train(output_graphs, data=None, weight_file=None, note=None): print("Training data, generating graphs: %r" % output_graphs) run_trainer() generate_parsed_logs() (training_details, validation_details) = parse_logs() # TODO(neuberg): This will need to be adapted against an already trained weight # file that we are fine-tuning. trained_weight_file = get_trained_weight_file() if output_graphs: graph.plot_results(training_details, validation_details, note) # If no weight file is provided by callers to this method, parse out the one we just # trained. if weight_file == None: weight_file = trained_weight_file predict.test_validation(data, weight_file)
def train(output_graphs, data=None, weight_file=None, note=None): print("Training data, generating graphs: %r" % output_graphs) run_trainer() copy_model_definitions() generate_parsed_logs() (training_details, validation_details) = parse_logs() trained_weight_file = get_trained_weight_file() if output_graphs: graph.plot_results(training_details, validation_details, note) # If no weight file is provided by callers to this method, parse out the one we just # trained. if weight_file == None: weight_file = trained_weight_file predict.test_clusters(data, weight_file) predict.test_validation_pairings(data, weight_file)
def train(output_graphs, data=None, weight_file=None, note=None): print("Training data, generating graphs: %r" % (output_graphs)) run() copy_model_definitions() generate_parsed_logs() (training_details, validation_details) = parse_logs() trained_weight_file = get_trained_weight_file() if output_graphs: graph.plot_results(training_details, validation_details, note) # If no weight file is provided by callers to this method, parse out the one we just # trained. #if weight_file == None: # weight_file = trained_weight_file #predict.test_clusters(data, weight_file) predict.test_pairings(data, trained_weight_file)