def train(self, sess, save_file, X_train, y_train, X_val, y_val):
count = 0
max_f = -1
writer = tf.summary.FileWriter('./graphs_ar', sess.graph)
trainloss = tf.summary.scalar("training_loss", self.ner_model.loss)
for epoch in range(self.num_epochs):
print "current epoch: %d" % (epoch)
for iteration in range(self.num_iterations):
print "this iteration is %d"%(iteration)
X_train_batch, y_train_batch = helper.nextRandomBatch(X_train, y_train, batch_size=self.batch_size)
transition_batch = helper.getTransition(y_train_batch)
_, train_loss, loss_train, max_scores, max_scores_pre, length =\
sess.run([
self.ner_model.optimizer,
trainloss,
self.ner_model.loss,
self.ner_model.max_scores,
self.ner_model.max_scores_pre,
self.ner_model.length,
],
feed_dict={
self.ner_model.input_data:X_train_batch,
self.ner_model.targets:y_train_batch,
self.ner_model.targets_transition:transition_batch
})
print "the loss : %f"%loss_train#, X_train_batch
writer.add_summary(train_loss, epoch*self.num_iterations+iteration)
if iteration % 100 == 0:
presicion_loc, recall_loc, f_loc, presicion_org, recall_org, f_org, presicion_per, recall_per, f_per = \
self.validationBatch(sess, X_val, y_val)
print "iteration: %5d, valid , valid precision: LOC %.5f, ORG %.5f, PER %.5f, valid recall: LOC %.5f, ORG %.5f, PER %.5f, valid f1: LOC %.5f, ORG %.5f, PER %.5f" %\
(iteration, presicion_loc, presicion_org, presicion_per, recall_loc, recall_org, recall_per, f_loc, f_org, f_per)
if f_loc + f_org + f_per >= max_f:
max_f = f_loc + f_org + f_per
saver = tf.train.Saver()
save_path = saver.save(sess, save_file)
print "saved the best model with f1: %.5f" % (max_f / 3.0)
self.last_f = f_loc + f_org + f_per
def train_an_iteration(self, sess, save_file, X_train, y_train, X_val, y_val, char2id, id2char, label2id, id2label, summary_writer_train_source, summary_writer_train_target, summary_writer_val_source, summary_writer_val_target, flag="source", is_summary=False, is_validation=False, X_test_source=None, y_test_source=None):
# num_iterations = int(math.ceil(1.0 * len(X_train) / self.batch_size))
num_iterations = 128
cnt = 0
for iteration in range(num_iterations):
print "this iteration is %d, the flag is %s"%(iteration, flag)
# train, the flag indicate the data source
X_train_batch, y_train_batch = helper.nextBatch(X_train, y_train, start_index=iteration * self.batch_size, batch_size=self.batch_size)
# y_train_weight_batch = 1 + np.array((y_train_batch == label2id['B']) | (y_train_batch == label2id['E']), float)
transition_batch = helper.getTransition(y_train_batch)
_, loss_train, max_scores, max_scores_pre, length, train_summary =\
sess.run([
self.optimizer_source if flag == "source" else self.optimizer_target,
self.loss_source if flag == "source" else self.loss_target,
self.max_scores_source if flag == "source" else self.max_scores_target,
self.max_scores_pre_source if flag == "source" else self.max_scores_pre_target,
self.length,
self.train_summary_source if flag == "source" else self.train_summary_target
],
feed_dict={
self.targets_transition:transition_batch,
self.inputs:X_train_batch,
self.targets:y_train_batch,
# self.targets_weight:y_train_weight_batch
})
print "the loss : %f"%loss_train
if is_summary:
presicion_loc, recall_loc, f_loc, presicion_org, recall_org, f_org, presicion_per, recall_per, f_per = self.predictBatch(sess, X_test_source, y_test_source, id2label, id2char, label2id, self.batch_size, "target")
print "iteration: %5d, %s valid , valid precision: LOC %.5f, ORG %.5f, PER %.5f, valid recall: LOC %.5f, ORG %.5f, PER %.5f, valid f1: LOC %.5f, ORG %.5f, PER %.5f" % (iteration, flag, presicion_loc, presicion_org, presicion_per, recall_loc, recall_org, recall_per, f_loc, f_org, f_per)
if f_loc + f_org + f_per >= self.max_f1:
self.max_f1 = f_loc + f_org + f_per
saver = tf.train.Saver()
save_path = saver.save(sess, save_file)
print "saved the best model with f1: %.5f" % (self.max_f1 / 3.0)
self.last_f = f_loc + f_org + f_per
def train(self, sess, save_file, X_train, y_train, X_val, y_val):
saver = tf.train.Saver()
char2id, id2char = helper.loadMap("char2id")
label2id, id2label = helper.loadMap("label2id")
merged = tf.contrib.deprecated.merge_all_summaries()
summary_writer_train = tf.contrib.summary.SummaryWriter(
'loss_log/train_loss', sess.graph)
summary_writer_val = tf.contrib.summary.SummaryWriter(
'loss_log/val_loss', sess.graph)
num_iterations = int(math.ceil(1.0 * len(X_train) / self.batch_size))
cnt = 0
for epoch in range(self.num_epochs):
# shuffle train in each epoch
sh_index = np.arange(len(X_train))
np.random.shuffle(sh_index)
X_train = X_train[sh_index]
y_train = y_train[sh_index]
print("current epoch: %d" % (epoch))
for iteration in range(num_iterations):
# train
X_train_batch, y_train_batch = helper.nextBatch(
X_train,
y_train,
start_index=iteration * self.batch_size,
batch_size=self.batch_size)
y_train_weight_batch = 1 + np.array(
(y_train_batch == label2id['B']) |
(y_train_batch == label2id['E']), float)
transition_batch = helper.getTransition(y_train_batch)
_, loss_train, max_scores, max_scores_pre, length, train_summary = \
sess.run([
self.optimizer,
self.loss,
self.max_scores,
self.max_scores_pre,
self.length,
self.train_summary
],
feed_dict={
self.targets_transition: transition_batch,
self.inputs: X_train_batch,
self.targets: y_train_batch,
self.targets_weight: y_train_weight_batch
})
predicts_train = self.viterbi(max_scores,
max_scores_pre,
length,
predict_size=self.batch_size)
if iteration % 10 == 0:
cnt += 1
precision_train, recall_train, f1_train = self.evaluate(
X_train_batch, y_train_batch, predicts_train, id2char,
id2label)
summary_writer_train.add_summary(train_summary, cnt)
print(
"iteration: %5d, train loss: %5d, train precision: %.5f, train recall: %.5f, train f1: %.5f"
% (iteration, loss_train, precision_train,
recall_train, f1_train))
# validation
if iteration % 100 == 0:
X_val_batch, y_val_batch = helper.nextRandomBatch(
X_val, y_val, batch_size=self.batch_size)
y_val_weight_batch = 1 + np.array(
(y_val_batch == label2id['B']) |
(y_val_batch == label2id['E']), float)
transition_batch = helper.getTransition(y_val_batch)
loss_val, max_scores, max_scores_pre, length, val_summary = \
sess.run([
self.loss,
self.max_scores,
self.max_scores_pre,
self.length,
self.val_summary
],
feed_dict={
self.targets_transition: transition_batch,
self.inputs: X_val_batch,
self.targets: y_val_batch,
self.targets_weight: y_val_weight_batch
})
predicts_val = self.viterbi(max_scores,
max_scores_pre,
length,
predict_size=self.batch_size)
precision_val, recall_val, f1_val = self.evaluate(
X_val_batch, y_val_batch, predicts_val, id2char,
id2label)
summary_writer_val.add_summary(val_summary, cnt)
print(
"iteration: %5d, valid loss: %5d, valid precision: %.5f, valid recall: %.5f, valid f1: %.5f"
% (iteration, loss_val, precision_val, recall_val,
f1_val))
if f1_val > self.max_f1:
self.max_f1 = f1_val
save_path = saver.save(sess, save_file)
print("saved the best model with f1: %.5f" %
(self.max_f1))
def train(self, sess, save_file, X_train, y_train, X_val, y_val):
saver = tf.train.Saver()
char2id, id2char = helper.loadMap("char2id")
label2id, id2label = helper.loadMap("label2id")
merged = tf.merge_all_summaries()
summary_writer_train = tf.train.SummaryWriter('loss_log/train_loss', sess.graph)
summary_writer_val = tf.train.SummaryWriter('loss_log/val_loss', sess.graph)
num_iterations = int(math.ceil(1.0 * len(X_train) / self.batch_size))
cnt = 0
for epoch in range(self.num_epochs):
# shuffle train in each epoch
sh_index = np.arange(len(X_train))
np.random.shuffle(sh_index)
X_train = X_train[sh_index]
y_train = y_train[sh_index]
print "current epoch: %d" % (epoch)
for iteration in range(num_iterations):
# train
X_train_batch, y_train_batch = helper.nextBatch(X_train, y_train, start_index=iteration * self.batch_size, batch_size=self.batch_size)
y_train_weight_batch = 1 + np.array((y_train_batch == label2id['B']) | (y_train_batch == label2id['E']), float)
transition_batch = helper.getTransition(y_train_batch)
_, loss_train, max_scores, max_scores_pre, length, train_summary =\
sess.run([
self.optimizer,
self.loss,
self.max_scores,
self.max_scores_pre,
self.length,
self.train_summary
],
feed_dict={
self.targets_transition:transition_batch,
self.inputs:X_train_batch,
self.targets:y_train_batch,
self.targets_weight:y_train_weight_batch
})
predicts_train = self.viterbi(max_scores, max_scores_pre, length, predict_size=self.batch_size)
if iteration % 10 == 0:
cnt += 1
precision_train, recall_train, f1_train = self.evaluate(X_train_batch, y_train_batch, predicts_train, id2char, id2label)
summary_writer_train.add_summary(train_summary, cnt)
print "iteration: %5d, train loss: %5d, train precision: %.5f, train recall: %.5f, train f1: %.5f" % (iteration, loss_train, precision_train, recall_train, f1_train)
# validation
if iteration % 100 == 0:
X_val_batch, y_val_batch = helper.nextRandomBatch(X_val, y_val, batch_size=self.batch_size)
y_val_weight_batch = 1 + np.array((y_val_batch == label2id['B']) | (y_val_batch == label2id['E']), float)
transition_batch = helper.getTransition(y_val_batch)
loss_val, max_scores, max_scores_pre, length, val_summary =\
sess.run([
self.loss,
self.max_scores,
self.max_scores_pre,
self.length,
self.val_summary
],
feed_dict={
self.targets_transition:transition_batch,
self.inputs:X_val_batch,
self.targets:y_val_batch,
self.targets_weight:y_val_weight_batch
})
predicts_val = self.viterbi(max_scores, max_scores_pre, length, predict_size=self.batch_size)
precision_val, recall_val, f1_val = self.evaluate(X_val_batch, y_val_batch, predicts_val, id2char, id2label)
summary_writer_val.add_summary(val_summary, cnt)
print "iteration: %5d, valid loss: %5d, valid precision: %.5f, valid recall: %.5f, valid f1: %.5f" % (iteration, loss_val, precision_val, recall_val, f1_val)
if f1_val > self.max_f1:
self.max_f1 = f1_val
save_path = saver.save(sess, save_file)
print "saved the best model with f1: %.5f" % (self.max_f1)
def train_an_iteration(self, sess, save_file, X_train, y_train, X_val, y_val, char2id, id2char, label2id, id2label, summary_writer_train_source, summary_writer_train_target, summary_writer_val_source, summary_writer_val_target, flag="source", is_summary=False, is_validation=False, X_test_source=None, y_test_source=None):
saver = tf.train.Saver()
# num_iterations = int(math.ceil(1.0 * len(X_train) / self.batch_size))
num_iterations = 1
cnt = 0
for iteration in range(num_iterations):
print "this iteration is %d, the flag is %s"%(iteration, flag)
# train, the flag indicate the data source
X_train_batch, y_train_batch = helper.nextBatch(X_train, y_train, start_index=iteration * self.batch_size, batch_size=self.batch_size)
# y_train_weight_batch = 1 + np.array((y_train_batch == label2id['B']) | (y_train_batch == label2id['E']), float)
transition_batch = helper.getTransition(y_train_batch)
_, loss_train, max_scores, max_scores_pre, length, train_summary =\
sess.run([
self.optimizer_source if flag == "source" else self.optimizer_target,
self.loss_source if flag == "source" else self.loss_target,
self.max_scores_source if flag == "source" else self.max_scores_target,
self.max_scores_pre_source if flag == "source" else self.max_scores_pre_target,
self.length,
self.train_summary_source if flag == "source" else self.train_summary_target
],
feed_dict={
self.targets_transition:transition_batch,
self.inputs:X_train_batch,
self.targets:y_train_batch,
# self.targets_weight:y_train_weight_batch
})
## predicts_train = self.viterbi(max_scores, max_scores_pre, length, predict_size=self.batch_size)
## if is_summary:
## cnt += 1
## presicion_loc, recall_loc, f_loc, presicion_org, recall_org, f_org, presicion_per, recall_per, f_per = self.evaluate(X_train_batch, y_train_batch, predicts_train, id2char, id2label)
# if flag == "source":
# summary_writer_train_source.add_summary(train_summary, cnt)
# else:
# summary_writer_train_target.add_summary(train_summary, cnt)
## print "iteration: %5d, %s train loss: %5d, train precision: LOC %.5f, ORG %.5f, PER %.5f, train recall: LOC %.5f, ORG %.5f, PER %.5f, train f1: LOC %.5f, ORG %.5f, PER %.5f" % (iteration, flag, loss_train, presicion_loc, presicion_org, presicion_per, recall_loc, recall_org, recall_per, f_loc, f_org, f_per)
# validation
## if is_validation:
## X_val_batch, y_val_batch = helper.nextRandomBatch(X_test_source, y_test_source, batch_size=self.batch_size)
# ## y_val_weight_batch = 1 + np.array((y_val_batch == label2id['B']) | (y_val_batch == label2id['E']), float)
## transition_batch = helper.getTransition(y_val_batch)
##
## loss_val, max_scores, max_scores_pre, length, val_summary =\
## sess.run([
## self.loss_source if flag == "source" else self.loss_target,
## self.max_scores_source if flag == "source" else self.max_scores_target,
## self.max_scores_pre_source if flag == "source" else self.max_scores_pre_target,
## self.length,
## self.train_summary_source if flag == "source" else self.train_summary_target
## ],
## feed_dict={
## self.targets_transition:transition_batch,
## self.inputs:X_val_batch,
## self.targets:y_val_batch,
# ## self.targets_weight:y_val_weight_batch
## })
##
## predicts_val = self.viterbi(max_scores, max_scores_pre, length, predict_size=self.batch_size)
## presicion_loc, recall_loc, f_loc, presicion_org, recall_org, f_org, presicion_per, recall_per, f_per = self.evaluate(X_val_batch, y_val_batch, predicts_val, id2char, id2label)
# if flag == "source":
# summary_writer_val_source.add_summary(val_summary, cnt)
# else:
# summary_writer_val_target.add_summary(val_summary, cnt)
if is_summary:
presicion_loc, recall_loc, f_loc, presicion_org, recall_org, f_org, presicion_per, recall_per, f_per = self.predictBatch(sess, X_test_source, y_test_source, id2label, id2char, label2id, self.batch_size, "target")
print "iteration: %5d, %s valid , valid precision: LOC %.5f, ORG %.5f, PER %.5f, valid recall: LOC %.5f, ORG %.5f, PER %.5f, valid f1: LOC %.5f, ORG %.5f, PER %.5f" % (iteration, flag, presicion_loc, presicion_org, presicion_per, recall_loc, recall_org, recall_per, f_loc, f_org, f_per)
if f_loc + f_org + f_per >= self.max_f1:
self.max_f1 = f_loc + f_org + f_per
save_path = saver.save(sess, save_file)
print "saved the best model with f1: %.5f" % (self.max_f1 / 3.0)
# print "********************************************************"
# print "********************************************************"
# X = helper.getPredict("./a", char2id)
# saver = tf.train.Saver()
# saver.restore(sess, model_path)
# model.predictBatch(sess, X, y_true, id2label, id2char, label2id, 128, "source")
# print "********************************************************"
# print "********************************************************"
self.last_f = f_loc + f_org + f_per