Python plot_results示例

示例#1

文件： train.py 项目： jhauswald/cloudless

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()

示例#2

文件： train.py 项目： robinkraft/cloudless

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()

示例#3

文件： train.py 项目： max-nova/cloudless

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)

示例#4

文件： train.py 项目： max-nova/cloudless

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)

示例#5

文件： train.py 项目： DIVYANI-DROID/Computer-Vision---face-detection-in-photos

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)

示例#6

文件： train.py 项目： SundayDX/LFW-adventure

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)