commit 996d461a6fb8dfe4da9c8298474ec5e87a144c7e
parent 748e9a2e558822a7ad9849dcef4b01df5eb77259
Author: Étienne Simon <esimon@esimon.eu>
Date: Fri, 18 Apr 2014 15:48:32 +0200
Rename config
Diffstat:
| M | main.py | | | 18 | +++++++++--------- |
| M | model.py | | | 50 | +++++++++++++++++++++++++------------------------- |
2 files changed, 34 insertions(+), 34 deletions(-)
diff --git a/main.py b/main.py
@@ -11,23 +11,23 @@ from relations import *
if __name__ == '__main__':
if len(sys.argv)<3:
- print('Usage: {0} data parameters [model]\n'.format(sys.argv[0]), file=sys.stderr)
+ print('Usage: {0} data parameters [model]'.format(sys.argv[0]), file=sys.stderr)
sys.exit(1)
data = sys.argv[1]
- config = sys.argv[2]
+ config_path = sys.argv[2]
model_path = None if len(sys.argv)<4 else sys.argv[3]
- with open(config, 'r') as config_file:
- hyperparameters = json.load(config_file)
- for k, v in hyperparameters.iteritems():
+ with open(config_path, 'r') as config_file:
+ config = json.load(config_file)
+ for k, v in config.iteritems():
if isinstance(v, basestring) and v.startswith('python:'):
- hyperparameters[k] = eval(v[7:])
- datalog_filepath = hyperparameters['datalog filepath']
+ config[k] = eval(v[7:])
+ datalog_filepath = config['datalog filepath']
data = Dataset(data)
if model_path is None:
- model = Model.initialise(hyperparameters['relations'], data, hyperparameters, 'TransE')
+ model = Model.initialise(config['relations'], data, config, config['model name'])
else:
- model = Model.load(model_path, data, hyperparameters, 'TransE')
+ model = Model.load(model_path, data, config, config['model name'])
model.train()
model.test()
diff --git a/model.py b/model.py
@@ -23,35 +23,35 @@ class Model(object):
"""
@classmethod
- def initialise(cls, Relations, dataset, hyperparameters, tag):
+ def initialise(cls, Relations, dataset, config, tag):
""" Initialise a model.
Keyword arguments:
Relations -- relations class
dataset -- dataset on which the model will be trained and tested
- hyperparameters -- hyperparameters dictionary
+ config -- config dictionary
tag -- name of the embeddings for parameter declaration
"""
log('# Initialising model "{0}"\n'.format(tag))
self = cls()
- self.embeddings = Embeddings(hyperparameters['rng'], dataset.number_embeddings, hyperparameters['dimension'], tag+'.embeddings')
- self.relations = Relations(hyperparameters['rng'], dataset.number_relations, hyperparameters['dimension'], tag+'.relations')
+ self.embeddings = Embeddings(config['rng'], dataset.number_embeddings, config['dimension'], tag+'.embeddings')
+ self.relations = Relations(config['rng'], dataset.number_relations, config['dimension'], tag+'.relations')
self.dataset = dataset
- self.hyperparameters = hyperparameters
+ self.config = config
self.tag = tag
self.build()
return self
@classmethod
- def load(cls, filepath, dataset, hyperparameters, tag):
+ def load(cls, filepath, dataset, config, tag):
""" Load a model from a file.
Keyword arguments:
filepath -- path to the Model file
dataset -- dataset on which the model will be trained and tested
- hyperparameters -- hyperparameters dictionary
+ config -- config dictionary
tag -- name of the embeddings for parameter declaration
"""
log('# Loading model from "{0}"\n'.format(filepath))
@@ -61,7 +61,7 @@ class Model(object):
self.embeddings = cPickle.load(file)
self.relations = cPickle.load(file)
self.dataset = dataset;
- self.hyperparameters = hyperparameters;
+ self.config = config;
self.tag = tag
self.build()
@@ -83,11 +83,11 @@ class Model(object):
left_negative, right_negative = self.embeddings.embed(inputs[3]), self.embeddings.embed(inputs[4])
relation = self.relations.lookup(inputs[0])
- score_positive = self.hyperparameters['similarity'](self.relations.apply(left_positive, relation), right_positive)
- score_left_negative = self.hyperparameters['similarity'](self.relations.apply(left_negative, relation), right_positive)
- score_right_negative = self.hyperparameters['similarity'](self.relations.apply(left_positive, relation), right_negative)
- score_left = self.hyperparameters['margin'] + score_positive - score_left_negative
- score_right = self.hyperparameters['margin'] + score_positive - score_right_negative
+ score_positive = self.config['similarity'](self.relations.apply(left_positive, relation), right_positive)
+ score_left_negative = self.config['similarity'](self.relations.apply(left_negative, relation), right_positive)
+ score_right_negative = self.config['similarity'](self.relations.apply(left_positive, relation), right_negative)
+ score_left = self.config['margin'] + score_positive - score_left_negative
+ score_right = self.config['margin'] + score_positive - score_right_negative
violating_margin_left = score_left>0
violating_margin_right = score_right>0
@@ -101,8 +101,8 @@ class Model(object):
relation = T.addbroadcast(relation, 0)
left_broadcasted = T.addbroadcast(left_positive, 0)
right_broadcasted = T.addbroadcast(right_positive, 0)
- left_score = self.hyperparameters['similarity'](self.relations.apply(left_broadcasted, relation), right_positive)
- right_score = self.hyperparameters['similarity'](self.relations.apply(left_positive, relation), right_broadcasted)
+ left_score = self.config['similarity'](self.relations.apply(left_broadcasted, relation), right_positive)
+ right_score = self.config['similarity'](self.relations.apply(left_positive, relation), right_broadcasted)
self.left_scoring_function = theano.function(inputs=list(inputs[0:3]), outputs=[left_score])
self.right_scoring_function = theano.function(inputs=list(inputs[0:3]), outputs=[right_score])
@@ -113,17 +113,17 @@ class Model(object):
Keyword arguments:
cost -- The cost to optimise.
"""
- lr_relations = self.hyperparameters['relation learning rate']
- lr_embeddings = self.hyperparameters['embeddings learning rate']
+ lr_relations = self.config['relation learning rate']
+ lr_embeddings = self.config['embeddings learning rate']
return self.relations.updates(cost, lr_relations) + self.embeddings.updates(cost, lr_embeddings)
def train(self):
""" Train the model. """
log('# Training the model "{0}"\n'.format(self.tag))
- batch_size = self.hyperparameters['train batch size']
- validation_frequency = self.hyperparameters['validation frequency']
- number_epoch = self.hyperparameters['number of epoch']
+ batch_size = self.config['train batch size']
+ validation_frequency = self.config['validation frequency']
+ number_epoch = self.config['number of epoch']
for epoch in xrange(number_epoch):
if epoch % validation_frequency == 0:
@@ -135,7 +135,7 @@ class Model(object):
def error(self, name):
""" Compute the mean rank and top 10 on a given data. """
- batch_size = self.hyperparameters['test batch size']
+ batch_size = self.config['test batch size']
count, mean, top10 = 0, 0, 0
for (relation, left, right) in self.dataset.iterate(name):
left_scores, right_scores = None, None
@@ -168,13 +168,13 @@ class Model(object):
if not hasattr(self, 'best_mean') or valid_mean < self.best_mean:
self.best_mean = valid_mean
log('(best so far')
- if self.hyperparameters['save best model']:
+ if self.config['save best model']:
log(', saving...')
- self.save(self.hyperparameters['best model save location'])
+ self.save(self.config['best model save location'])
log(' done')
log(')')
- if self.hyperparameters['validate on training data']:
+ if self.config['validate on training data']:
(train_mean, train_top10) = self.error('train')
log(' train mean: {0:<15} train top10: {1:<15}'.format(train_mean, train_top10))
log('\n')
@@ -184,5 +184,5 @@ class Model(object):
log('# Testing the model "{0}"'.format(self.tag))
(mean, top10) = self.error('test')
log(' mean: {0:<15} top10: {1:<15} (saving...'.format(mean, top10))
- self.save(self.hyperparameters['last model save location'])
+ self.save(self.config['last model save location'])
log(' done)\n')
