Python Plot示例

示例#1

文件： deep_l0.py 项目： donghyunlee/play

train_monitor = TrainingDataMonitoring(variables=[cost],
                                       every_n_batches=n_batches,
                                       prefix="train")

valid_monitor = DataStreamMonitoring(
    [cost],
    valid_stream,
    after_epoch=True,
    #before_first_epoch = False,
    prefix="valid")

extensions = extensions = [
    Timing(every_n_batches=n_batches), train_monitor, valid_monitor,
    TrackTheBest('valid_nll', after_epoch=True),
    Plot(save_dir + experiment_name + ".png", [['train_nll', 'valid_nll']],
         every_n_batches=4 * n_batches,
         email=True),
    Checkpoint(save_dir + experiment_name + ".pkl",
               use_cpickle=True,
               every_n_batches=n_batches * 8,
               after_epoch=True),
    Checkpoint(save_dir + "best_" + experiment_name + ".pkl",
               after_epoch=True,
               use_cpickle=True).add_condition(
                   ['after_epoch'],
                   predicate=OnLogRecord('valid_nll_best_so_far')),
    Printing(every_n_batches=n_batches, after_epoch=True),
    FinishAfter(after_n_epochs=2),
    SaveComputationGraph(emit)
]

示例#2

文件： sp_only.py 项目： anirudh9119/play

train_monitor = TrainingDataMonitoring(variables=monitoring_variables +
                                       data_monitoring,
                                       every_n_batches=n_batches,
                                       prefix="train")

valid_monitor = DataStreamMonitoring(monitoring_variables,
                                     valid_stream,
                                     every_n_batches=n_batches,
                                     prefix="valid")

extensions = [
    ProgressBar(),
    Timing(every_n_batches=n_batches), train_monitor, valid_monitor,
    TrackTheBest('valid_nll', every_n_batches=n_batches),
    Plot(save_dir + "progress/" + experiment_name + ".png",
         [['train_nll', 'valid_nll'], ['valid_learning_rate']],
         every_n_batches=n_batches,
         email=False),
    Checkpoint(save_dir + "pkl/best_" + experiment_name + ".pkl",
               use_cpickle=True).add_condition(
                   ['after_batch'],
                   predicate=OnLogRecord('valid_nll_best_so_far')),
    Printing(every_n_batches=n_batches),
    Flush(every_n_batches=n_batches, before_first_epoch=True),
    LearningRateSchedule(lr,
                         'valid_nll',
                         path=save_dir + "pkl/best_" + experiment_name +
                         ".pkl",
                         states=states.values(),
                         every_n_batches=n_batches,
                         before_first_epoch=True)
]

示例#3

文件： conditional_scribe.py 项目： soroushmehr/scribe

    try:
        result = math.isnan(log.current_row['train_total_gradient_norm'])
        return result
    except:
        return False


extensions = extensions = [
    train_monitor, valid_monitor,
    Timing(every_n_batches=n_batches),
    Printing(every_n_batches=n_batches),
    WriteConditional(emit,
                     every_n_batches=n_batches,
                     save_name=save_dir + "samples/" + exp_name + ".png"),
    FinishAfter().add_condition(["after_batch"], _is_nan),
    ProgressBar(),
    Checkpoint(save_dir + "pkl/" + exp_name + ".pkl", after_epoch=True),
    SaveComputationGraph(emit),
    Plot(save_dir + exp_name + ".png", [['train_nll', 'valid_nll']],
         every_n_batches=5 * n_batches,
         email=False)
]

main_loop = MainLoop(model=model,
                     data_stream=data_stream,
                     algorithm=algorithm,
                     extensions=extensions)

main_loop.run()

ipdb.set_trace()

示例#4

valid_monitor = DataStreamMonitoring(
     [cost],
     valid_stream,
     after_epoch = True,
     #before_first_epoch = False,
     prefix="valid")

extensions = extensions=[
    Timing(every_n_batches = n_batches),
    train_monitor,
    valid_monitor,
    TrackTheBest('valid_sequence_log_likelihood', after_epoch = True),
    Plot(save_dir+experiment_name+".png",
         [['train_sequence_log_likelihood',
           'valid_sequence_log_likelihood']],
         every_n_batches = 4*n_batches,
         email=False),
    Checkpoint(save_dir+experiment_name+".pkl",
               use_cpickle = True,
               every_n_batches = n_batches*8,
               after_epoch = True),
    Checkpoint(save_dir+"best_"+experiment_name+".pkl",
     after_epoch = True,
     use_cpickle = True
     ).add_condition(['after_epoch'],
          predicate=OnLogRecord('valid_sequence_log_likelihood_best_so_far')),
    Printing(every_n_batches = n_batches, after_epoch = True),
    FinishAfter(after_n_epochs=2),
    SaveComputationGraph(emit)
    ]

示例#5

文件： l0.py 项目： donghyunlee/play

train_monitor = TrainingDataMonitoring(variables=[cost],
                                       every_n_batches=n_batches,
                                       prefix="train")

valid_monitor = DataStreamMonitoring(
    [cost],
    valid_stream,
    after_epoch=True,
    #before_first_epoch = False,
    prefix="valid")

extensions = extensions = [
    Timing(every_n_batches=n_batches), train_monitor, valid_monitor,
    TrackTheBest('valid_sequence_log_likelihood', after_epoch=True),
    Plot('Blizzard_l0.png',
         [['train_sequence_log_likelihood', 'valid_sequence_log_likelihood']],
         every_n_batches=4 * n_batches),
    Checkpoint(save_dir + experiment_name + ".pkl",
               use_cpickle=True,
               every_n_batches=n_batches * 8),
    Checkpoint(save_dir + "best_" + experiment_name + ".pkl",
               after_epoch=True,
               use_cpickle=True).add_condition(
                   ['after_epoch'],
                   predicate=OnLogRecord(
                       'valid_sequence_log_likelihood_best_so_far')),
    Printing(every_n_batches=n_batches)
]

main_loop = MainLoop(model=model,
                     data_stream=train_stream,