def build_train_dev_data(file_src, file_tgt, file_align, mode):
temp_train_file = tempfile.NamedTemporaryFile(delete=False)
temp_dev_file = tempfile.NamedTemporaryFile(delete=False)
train_file = open(temp_train_file.name, 'w')
dev_file = open(temp_dev_file.name, 'w')
dev_lines = 1000
n = 0
outfile = dev_file
with open(file_src) as srcfile, open(file_tgt) as tgtfile, open(file_align) as alignfile:
for x, y, a in zip(srcfile, tgtfile, alignfile):
if(n == dev_lines):
outfile = train_file
x = x.strip()
y = y.strip()
a = a.strip()
outfile.write("{0}\t{1}\t{2}\n".format(x, y, a))
n += 1
train_file.close()
dev_file.close()
temp_train_data_file = tempfile.NamedTemporaryFile(delete=False)
temp_dev_data_file = tempfile.NamedTemporaryFile(delete=False)
build_data(['', '-data', temp_train_file.name, '-mode', mode, '-output', temp_train_data_file.name])
build_data(['', '-data', temp_dev_file.name, '-mode', mode, '-output', temp_dev_data_file.name])
os.remove(temp_train_file.name)
os.remove(temp_dev_file.name)
return temp_train_data_file.name, temp_dev_data_file.name
def train(self, subset=[]):
print("\n### Train MLP - %s" % self.database)
Y = self.labels
X = self.images
if subset != []:
Y = Y[subset]
X = X[subset]
# X = X#build_data(images)
X = build_data(X)
self.mean = np.mean(X, axis=(0, 1, 2))
self.std = np.std(X, axis=(0, 1, 2))
X = self.normalize(X)
(n_samples, height, width, p) = X.shape
X = X.reshape(n_samples * height * width, p)
Y = Y.reshape(n_samples * height * width, 1)
# Train
Y_train_vector = (Y == np.arange(self.nb_labels)) * 1
mlp = MLPClassifier(hidden_layer_sizes=params.hidden_layer_sizes,
max_iter=params.max_iter,
alpha=params.alpha,
solver=params.solver,
activation=params.activation,
verbose=params.verbose,
tol=params.tol,
random_state=params.random_state,
learning_rate_init=params.learning_rate_init)
mlp.fit(X, Y_train_vector)
print("Done.")
self.model = mlp
index_files_dict['valid'] = [
PREFIX_INDEX_FOLDER + "debug_valid.txt",
# PREFIX_INDEX_FOLDER + "bouliane_valid.txt",
# PREFIX_INDEX_FOLDER + "hand_picked_Spotify_valid.txt",
# PREFIX_INDEX_FOLDER + "liszt_classical_archives_valid.txt"
]
index_files_dict['test'] = [
PREFIX_INDEX_FOLDER + "debug_test.txt",
# PREFIX_INDEX_FOLDER + "bouliane_test.txt",
# PREFIX_INDEX_FOLDER + "hand_picked_Spotify_test.txt",
# PREFIX_INDEX_FOLDER + "liszt_classical_archives_test.txt"
]
build_data(index_files_dict=index_files_dict,
meta_info_path=data_folder + '/temp.p',
quantization=script_param['quantization'],
temporal_granularity=script_param['temporal_granularity'],
store_folder=data_folder,
logging=logging)
############################################################
# Hyper parameter space
############################################################
model_space = Model_class.get_hp_space()
optim_space = Optimization_method.get_hp_space()
space = {'model': model_space, 'optim': optim_space, 'train': train_param, 'script': script_param}
############################################################
# MongoDB
############################################################
host = "localhost"
port = 27017
raise ValueError(
'The configuration file and the timestamp should be specified.')
es_file = sys.argv[3] + "/es_" + sys.argv[2] + ".txt"
es_epoch = sys.maxsize
if os.path.isfile(es_file) == True:
with open(es_file, 'r') as myfile:
es_epoch = int(myfile.read())
myfile.close()
return es_epoch
if __name__ == "__main__":
es_epoch = checkInputs()
config = build_data(sys.argv[1])
config.train_id_docs.extend(config.dev_id_docs)
train_data = utils.HeadData(config.train_id_docs, np.arange(len(config.train_id_docs)))
test_data = utils.HeadData(config.test_id_docs, np.arange(len(config.test_id_docs)))
tf.reset_default_graph()
tf.set_random_seed(1)
utils.printParameters(config)
# ---- Training ----
config1 = tf.ConfigProto()
config1.gpu_options.per_process_gpu_memory_fraction = 0.85
with tf.Session(config=config1) as sess:
# saver = tf.train.import_meta_graph('model.ckpt.meta')
# saver.restore(sess, 'model.ckpt')
embedding_matrix = tf.get_variable('embedding_matrix', shape=config.wordvectors.shape, dtype=tf.float32,
trainable=False).assign(config.wordvectors)
if (len(sys.argv) <= 3) or os.path.isfile(sys.argv[0]) == False:
raise ValueError(
'The configuration file and the timestamp should be specified.')
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
gpuConfig = tf.ConfigProto(allow_soft_placement=True)
gpuConfig.gpu_options.allow_growth = True
if __name__ == "__main__":
# checkInputs()
config = build_data("./configs/CoNLL04/bio_config")
train_data = utils.HeadData(config.train_id_docs,
np.arange(len(
config.train_id_docs))) ## build data
dev_data = utils.HeadData(config.dev_id_docs,
np.arange(len(config.dev_id_docs)))
test_data = utils.HeadData(config.test_id_docs,
np.arange(len(config.test_id_docs)))
tf.reset_default_graph()
tf.set_random_seed(1)
utils.printParameters(config)
with tf.Session(config=gpuConfig) as sess:
if (len(sys.argv) <= 3) or os.path.isfile(sys.argv[0]) == False:
raise ValueError(
'The configuration file and the timestamp should be specified.')
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
gpuConfig = tf.ConfigProto(allow_soft_placement=True)
gpuConfig.gpu_options.allow_growth = True
if __name__ == "__main__":
# checkInputs()
config = build_data("./configs/ADE/bio_config")
train_data = utils.HeadData(config.train_id_docs,
np.arange(len(
config.train_id_docs))) ## build data
# dev_data = utils.HeadData(config.dev_id_docs, np.arange(len(config.dev_id_docs)))
# test_data = utils.HeadData(config.test_id_docs, np.arange(len(config.test_id_docs)))
train_data, dev_data = train_data.split(0.2)
dev_data, test_data = dev_data.split(0.5)
tf.reset_default_graph()
tf.set_random_seed(1)
utils.printParameters(config)
with tf.Session(config=gpuConfig) as sess:
from build_data import build_data import numpy as np from sklearn.neural_network import MLPClassifier from skimage import color from matplotlib import pyplot as plt folder = '../Corel_Dataset/' images = np.load(folder + 'images_lab.npy') labels = np.load(folder + 'labels.npy') (n_samples, height, width, p) = images.shape Y = labels.reshape(n_samples, width * height) X = build_data(images, labels) (n_samples, height, width, size_input) = np.shape(X) # Shuffle the images np.random.seed(3) order = np.arange(n_samples) np.random.shuffle(order) # X = X[order] # Y = Y[order] # Build training data nb_labels = len(np.unique(Y)) train_size = 60
PREFIX_INDEX_FOLDER + "hand_picked_Spotify_valid.txt",
PREFIX_INDEX_FOLDER + "liszt_classical_archives_valid.txt"
]
index_files_dict['test'] = [
# PREFIX_INDEX_FOLDER + "debug_test.txt",
PREFIX_INDEX_FOLDER + "bouliane_test.txt",
PREFIX_INDEX_FOLDER + "hand_picked_Spotify_test.txt",
PREFIX_INDEX_FOLDER + "liszt_classical_archives_test.txt"
]
if not os.path.isdir(data_folder):
os.mkdir(data_folder)
build_data(index_files_dict=index_files_dict,
meta_info_path=LOCAL_SCRATCH + '/Data/temp.p',
quantization=quantization,
temporal_granularity=temporal_granularity,
store_folder=LOCAL_SCRATCH + '/Data',
logging=logging)
else:
logging.info('# ** Database NOT rebuilt ** ')
######################################
######################################
###### HOPT function
if not ONLY_BUILD_DB:
best = train_hopt(max_evals, result_file)
logging.info(best)
######################################
######################################
###### Or directly call the train function for one set of HPARAMS
nb_labels = len(np.unique(labels))
precision_acc = 3
np.random.seed(3)
(n_samples, height, width, p) = images.shape
n_steps = width * height
# Raw image ###############################
X = images.reshape(n_samples, height, width, 3)
X = X[image_num].reshape(1, height, width, 3)
image = color.lab2rgb(X.reshape(height, width, 3))
final_plot(image, 'original', 1)
# Feautre image ###########################
print "Compute Features..."
X = build_data(X)
(_, _, _, size_input) = X.shape
X = normalize(X, mean, std)
X_test = X[0].reshape(width * height, size_input)
# Ground truth labels #####################
Y = labels.reshape(n_samples, height * width)
Y_test = Y[image_num].reshape(width * height)
final_plot(colorize(Y_test.reshape(height, width)), 'ground truth', 2)
# Initial labeling (MLP) ##################
Y_proba = mlp_model.predict_proba(X_test)
Y_init = np.argmax(Y_proba, axis=1)
initial_accuracy = round(
np.sum(Y_init == Y_test) / float(width * height), precision_acc)
final_plot(colorize(Y_init.reshape(height, width)),
def config_from_args(args):
config = Config()
for key, value in vars(args).items():
config.__dict__[key] = value
config.auto_config()
logger = get_logger(config.log_path)
return config, logger
if __name__ == "__main__":
args = parse_parameters()
config, logger = config_from_args(args)
if args.do_preprocess:
build_data(config, logger)
# load vocabs
vocab_words = load_vocab(config.words_filename)
vocab_tags = load_vocab(config.tags_filename)
vocab_chars = load_vocab(config.chars_filename)
vocab_relations = load_vocab(config.relations_filename)
# get processing functions
processing_word = get_processing_word(vocab_words,
vocab_chars,
lowercase=config.lowercase,
chars=config.chars)
processing_tag = get_processing_word(vocab_tags, lowercase=False)
processing_relation = get_processing_relation(vocab_relations)
