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January 24, 2017 15:04
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MongoDB tutorial + sacred examples
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# Author: Arthur Mensch | |
# License: BSD | |
import os | |
import time | |
import matplotlib.pyplot as plt | |
from modl.feature_extraction.image import LazyCleanPatchExtractor | |
from modl.image import ImageDictFact | |
from sacred import Experiment | |
from modl.datasets.image import load_image | |
from modl.plotting.image import plot_patches | |
from sacred.observers import MongoObserver | |
"""Install master of modl""" | |
decompose_ex = Experiment('decompose_images') | |
observer = MongoObserver.create(url='localhost', db_name='experiments', | |
port=27017) | |
decompose_ex.observers.append(observer) | |
@decompose_ex.config | |
def config(): | |
batch_size = 400 | |
learning_rate = 0.92 | |
reduction = 10 | |
alpha = 0.08 | |
n_epochs = 10 | |
n_components = 50 | |
test_size = 4000 | |
max_patches = 10000 | |
patch_size = (16, 16) | |
n_threads = 3 | |
verbose = 20 | |
method = 'gram' | |
setting = 'dictionary learning' | |
source = 'lisboa' | |
gray = False | |
scale = 1 | |
class DictionaryScorer: | |
def __init__(self, test_data): | |
self.start_time = time.clock() | |
self.test_data = test_data | |
self.test_time = 0 | |
self.time = [] | |
self.score = [] | |
self.iter = [] | |
@decompose_ex.capture | |
def __call__(self, dict_fact, _run): | |
test_time = time.clock() | |
score = dict_fact.score(self.test_data) | |
self.test_time += time.clock() - test_time | |
this_time = time.clock() - self.start_time - self.test_time | |
self.time.append(this_time) | |
self.score.append(score) | |
self.iter.append(dict_fact.n_iter_) | |
_run.info['score'] = self.score | |
_run.info['time'] = self.time | |
_run.info['iter'] = self.iter | |
@decompose_ex.automain | |
def decompose_run(batch_size, | |
learning_rate, | |
reduction, | |
n_components, | |
n_epochs, | |
patch_size, | |
test_size, | |
alpha, | |
setting, | |
n_threads, | |
verbose, | |
max_patches, | |
method, | |
source, | |
scale, gray, | |
_run, | |
_seed, | |
): | |
print('Loading data') | |
image = load_image(source, scale=scale, gray=gray) | |
print('Done') | |
width, height, n_channel = image.shape | |
patch_extractor = LazyCleanPatchExtractor(patch_size=patch_size, | |
max_patches=test_size, | |
random_state=_seed) | |
test_data = patch_extractor.transform(image[:, :height // 2, :]) | |
cb = DictionaryScorer(test_data) | |
dict_fact = ImageDictFact(method=method, | |
setting=setting, | |
alpha=alpha, | |
n_epochs=n_epochs, | |
random_state=_seed, | |
n_components=n_components, | |
learning_rate=learning_rate, | |
max_patches=max_patches, | |
batch_size=batch_size, | |
patch_size=patch_size, | |
reduction=reduction, | |
callback=cb, | |
verbose=verbose, | |
n_threads=n_threads, | |
) | |
dict_fact.fit(image[:, height // 2:, :]) | |
score = dict_fact.score(test_data) | |
_run.info['final_score'] = score | |
fig = plt.figure() | |
patches = dict_fact.components_ | |
plot_patches(fig, patches) | |
fig.suptitle('Dictionary components') | |
plt.savefig('components.png') | |
plt.close(fig) | |
_run.add_artifact('components.png') | |
os.unlink('components.png') | |
fig, ax = plt.subplots(1, 1) | |
ax.plot(cb.time, cb.score, marker='o') | |
ax.legend() | |
ax.set_xscale('log') | |
ax.set_xlabel('Time (s)') | |
ax.set_ylabel('Test objective value') | |
plt.savefig('training_curve.png') | |
plt.close(fig) | |
_run.add_artifact('training_curve.png') | |
os.unlink('training_curve.png') |
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