Python load_results 예제들

예제 #1

파일: extract_output.py 프로젝트: Qi0116/deepthought

def extract_output(experiment_root):
    train, model = load_results(experiment_root);
        
    # get the datasets with their names from the monitor
    for key, dataset in train.algorithm.monitoring_dataset.items():
        # process each dataset 
        with log_timing(log, 'processing dataset \'{}\''.format(key)): 
            y_real, y_pred, output = process_dataset(model, dataset)
            
            save(os.path.join(experiment_root, 'cache', key+'_output.pklz'), (y_real, y_pred, output));    

예제 #2

파일: extract_output.py 프로젝트: vishwajit123/deepthought

def extract_output(experiment_root):
    train, model = load_results(experiment_root)

    # get the datasets with their names from the monitor
    for key, dataset in train.algorithm.monitoring_dataset.items():
        # process each dataset
        with log_timing(log, 'processing dataset \'{}\''.format(key)):
            y_real, y_pred, output = process_dataset(model, dataset)

            save(os.path.join(experiment_root, 'cache', key + '_output.pklz'),
                 (y_real, y_pred, output))

예제 #3

파일: test.py 프로젝트: vishwajit123/deepthought

import logging
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)

import os
import deepthought
DATA_PATH = os.path.join(deepthought.DATA_PATH, 'rwanda2013rhythms')
MODEL_PATH = os.path.join(deepthought.OUTPUT_PATH, 'nips2014', 'models', 'h0')
OUTPUT_PATH = os.path.join(deepthought.OUTPUT_PATH, 'nips2014', 'figures',
                           'h0')
print 'data path  : {}'.format(DATA_PATH)
print 'model path : {}'.format(MODEL_PATH)
print 'output path: {}'.format(OUTPUT_PATH)

# test with subject 4
# WARNING: code seems to be broken due to library update!
from deepthought.experiments.nips2014.scripts.generate_plots import load_results
from deepthought.pylearn2ext.util import process_dataset
path4 = os.path.join(MODEL_PATH, '4', 'best')
train, model = load_results(path4)
dataset = train.algorithm.monitoring_dataset['test']
y_real, y_pred, output = process_dataset(model, dataset)

# subject 4 analysis
from deepthought.experiments.nips2014.scripts.generate_plots import multi_level_accuracy_analysis
multi_level_accuracy_analysis(y_real, y_pred)

from deepthought.pylearn2ext.util import aggregate_classification
t_real, t_pred, t_predf, t_predp = aggregate_classification(
    dataset.trial_partitions, y_real, y_pred, output)
multi_level_accuracy_analysis(t_real, t_pred)

예제 #4

파일: test.py 프로젝트: Qi0116/deepthought

logger.setLevel(logging.DEBUG)

import os;
import deepthought;
DATA_PATH = os.path.join(deepthought.DATA_PATH, 'rwanda2013rhythms');
MODEL_PATH = os.path.join(deepthought.OUTPUT_PATH, 'nips2014', 'models', 'h0');
OUTPUT_PATH = os.path.join(deepthought.OUTPUT_PATH, 'nips2014', 'figures', 'h0');
print 'data path  : {}'.format(DATA_PATH);
print 'model path : {}'.format(MODEL_PATH);
print 'output path: {}'.format(OUTPUT_PATH);



# test with subject 4
# WARNING: code seems to be broken due to library update!
from deepthought.experiments.nips2014.scripts.generate_plots import load_results;
from deepthought.pylearn2ext.util import process_dataset;
path4 = os.path.join(MODEL_PATH, '4', 'best');
train, model = load_results(path4);
dataset = train.algorithm.monitoring_dataset['test'];
y_real, y_pred, output = process_dataset(model, dataset);


# subject 4 analysis
from deepthought.experiments.nips2014.scripts.generate_plots import multi_level_accuracy_analysis;
multi_level_accuracy_analysis(y_real, y_pred);


from deepthought.pylearn2ext.util import aggregate_classification
t_real, t_pred, t_predf, t_predp = aggregate_classification(dataset.trial_partitions, y_real, y_pred, output);  
multi_level_accuracy_analysis(t_real, t_pred);