def train(restore=False):
config=Config()
data,vocab = utils.load_sentiment_treebank(DIR,config.fine_grained)
train_set, dev_set, test_set = data['train'], data['dev'], data['test']
print 'train', len(train_set)
print 'dev', len(dev_set)
print 'test', len(test_set)
num_emb = len(vocab)
num_labels = 5 if config.fine_grained else 3
for _, dataset in data.items():
labels = [label for _, label in dataset]
assert set(labels) <= set(xrange(num_labels)), set(labels)
print 'num emb', num_emb
print 'num labels', num_labels
config.num_emb=num_emb
config.output_dim = num_labels
config.maxseqlen=utils.get_max_len_data(data)
config.maxnodesize=utils.get_max_node_size(data)
print config.maxnodesize,config.maxseqlen ," maxsize"
#return
random.seed()
np.random.seed()
with tf.Graph().as_default():
#model = tf_seq_lstm.tf_seqLSTM(config)
model = tf_tree_lstm.tf_NarytreeLSTM(config)
init=tf.initialize_all_variables()
saver = tf.train.Saver()
best_valid_score=0.0
best_valid_epoch=0
dev_score=0.0
test_score=0.0
with tf.Session() as sess:
sess.run(init)
start_time=time.time()
if restore:saver.restore(sess,'./ckpt/tree_rnn_weights')
for epoch in range(config.num_epochs):
print 'epoch', epoch
avg_loss=0.0
avg_loss = train_epoch(model, train_set,sess)
print 'avg loss', avg_loss
dev_score=evaluate(model,dev_set,sess)
print 'dev-scoer', dev_score
if dev_score > best_valid_score:
best_valid_score=dev_score
best_valid_epoch=epoch
saver.save(sess,'./ckpt/tree_rnn_weights')
if epoch -best_valid_epoch > config.early_stopping:
break
print "time per epochis {0}".format(
time.time()-start_time)
test_score = evaluate(model,test_set,sess)
print test_score,'test_score'
def train2():
config = Config()
config.batch_size = 25
config.lr = 0.05
config.dropout = 0.5
config.reg = 0.0001
config.emb_lr = 0.02
import collections
import numpy as np
from sklearn import metrics
def test(model, data, session):
relevant_labels = [0, 2]
ys_true = collections.deque([])
ys_pred = collections.deque([])
for batch in data:
y_pred = model.get_output()
y_true = batch[0].root_labels/2
feed_dict = {model.labels: batch[0].root_labels}
feed_dict.update(model.tree_lstm.get_feed_dict(batch[0]))
y_pred_ = session.run([y_pred], feed_dict=feed_dict)
y_pred_ = np.argmax(y_pred_[0][:,relevant_labels], axis=1)
ys_true += y_true.tolist()
ys_pred += y_pred_.tolist()
ys_true = list(ys_true)
ys_pred = list(ys_pred)
score = metrics.accuracy_score(ys_true, ys_pred)
print "Accuracy", score
#print "Recall", metrics.recall_score(ys_true, ys_pred)
#print "f1_score", metrics.f1_score(ys_true, ys_pred)
print "confusion_matrix"
print metrics.confusion_matrix(ys_true, ys_pred)
return score
data, vocab = utils.load_sentiment_treebank(DIR, GLOVE_DIR, config.fine_grained)
# data, vocab = utils.load_sentiment_treebank(DIR, None, config.fine_grained)
config.embeddings = vocab.embed_matrix
train_set, dev_set, test_set = data['train'], data['dev'], data['test']
print 'train', len(train_set)
print 'dev', len(dev_set)
print 'test', len(test_set)
num_emb = len(vocab)
num_labels = 5 if config.fine_grained else 3
for _, dataset in data.items():
labels = [label for _, label in dataset]
assert set(labels) <= set(xrange(num_labels)), set(labels)
print 'num emb', num_emb
print 'num labels', num_labels
config.num_emb = num_emb
config.output_dim = num_labels
# return
random.seed()
np.random.seed()
from random import shuffle
shuffle(train_set)
train_set = utils.build_labelized_batch_trees(train_set, config.batch_size)
dev_set = utils.build_labelized_batch_trees(dev_set, 500)
test_set = utils.build_labelized_batch_trees(test_set, 500)
with tf.Graph().as_default():
#model = tf_seq_lstm.tf_seqLSTM(config)
model = nary_tree_lstm.SoftMaxNarytreeLSTM(config, train_set + dev_set + test_set)
init=tf.global_variables_initializer()
best_valid_score=0.0
best_valid_epoch=0
dev_score=0.0
test_score=0.0
with tf.Session() as sess:
sess.run(init)
for epoch in range(config.num_epochs):
start_time = time.time()
print 'epoch', epoch
avg_loss=0.0
model.train_epoch(train_set[:],sess)
print "Training time per epoch is {0}".format(
time.time() - start_time)
print 'validation score'
score = test(model,dev_set,sess)
#print 'train score'
#test(model, train_set[:40], sess)
if score >= best_valid_score:
best_valid_score = score
best_valid_epoch = epoch
test_score = test(model,test_set,sess)
print 'test score :', test_score, 'updated', epoch - best_valid_epoch, 'epochs ago with validation score', best_valid_score
def train(restore=False):
config = Config()
data, vocab = utils.load_sentiment_treebank(DIR, config.fine_grained)
train_set, dev_set, test_set = data['train'], data['dev'], data['test']
print 'train', len(train_set)
print 'dev', len(dev_set)
print 'test', len(test_set)
num_emb = len(vocab)
num_labels = 5 if config.fine_grained else 3
for _, dataset in data.items():
labels = [label for _, label in dataset]
assert set(labels) <= set(xrange(num_labels)), set(labels)
print 'num emb', num_emb
print 'num labels', num_labels
config.num_emb = num_emb
config.output_dim = num_labels
config.maxseqlen = utils.get_max_len_data(data)
config.maxnodesize = utils.get_max_node_size(data)
print config.maxnodesize, config.maxseqlen, " maxsize"
#return
random.seed()
np.random.seed()
with tf.Graph().as_default():
#model = tf_seq_lstm.tf_seqLSTM(config)
model = tf_tree_lstm.tf_NarytreeLSTM(config)
init = tf.initialize_all_variables()
saver = tf.train.Saver()
best_valid_score = 0.0
best_valid_epoch = 0
dev_score = 0.0
test_score = 0.0
with tf.Session() as sess:
sess.run(init)
start_time = time.time()
if restore: saver.restore(sess, './ckpt/tree_rnn_weights')
for epoch in range(config.num_epochs):
print 'epoch', epoch
avg_loss = 0.0
avg_loss = train_epoch(model, train_set, sess)
print 'avg loss', avg_loss
dev_score = evaluate(model, dev_set, sess)
print 'dev-scoer', dev_score
if dev_score > best_valid_score:
best_valid_score = dev_score
best_valid_epoch = epoch
saver.save(sess, './ckpt/tree_rnn_weights')
if epoch - best_valid_epoch > config.early_stopping:
break
print "time per epochis {0}".format(time.time() - start_time)
test_score = evaluate(model, test_set, sess)
print test_score, 'test_score'
def train(restore=False):
config=Config()
config.batch_size = 5
config.lr = 0.05
data,vocab = utils.load_sentiment_treebank(DIR,GLOVE_DIR,config.fine_grained)
config.embeddings = vocab.embed_matrix
config.early_stopping = 2
config.reg = 0.0001
config.dropout = 1.0
config.emb_lr = 0.1
train_set, dev_set, test_set = data['train'], data['dev'], data['test']
print 'train', len(train_set)
print 'dev', len(dev_set)
print 'test', len(test_set)
num_emb = len(vocab)
num_labels = 5 if config.fine_grained else 3
for _, dataset in data.items():
labels = [label for _, label in dataset]
assert set(labels) <= set(xrange(num_labels)), set(labels)
print 'num emb', num_emb
print 'num labels', num_labels
config.num_emb=num_emb
config.output_dim = num_labels
config.maxseqlen=utils.get_max_len_data(data)
config.maxnodesize=utils.get_max_node_size(data)
print config.maxnodesize,config.maxseqlen ," maxsize"
#return
random.seed()
np.random.seed()
with tf.Graph().as_default():
#model = tf_seq_lstm.tf_seqLSTM(config)
model = tf_tree_lstm.tf_NarytreeLSTM(config)
init=tf.global_variables_initializer()
saver = tf.train.Saver()
best_valid_score=0.0
best_valid_epoch=0
dev_score=0.0
test_score=0.0
with tf.Session() as sess:
sess.run(init)
if restore:saver.restore(sess,'./ckpt/tree_rnn_weights')
for epoch in range(config.num_epochs):
start_time = time.time()
print 'epoch', epoch
avg_loss=0.0
avg_loss = train_epoch(model, train_set,sess)
print 'avg loss', avg_loss
print "Training time per epoch is {0}".format(
time.time() - start_time)
dev_score=evaluate(model,dev_set,sess)
print 'dev-score', dev_score
if dev_score >= best_valid_score:
best_valid_score=dev_score
best_valid_epoch=epoch
#saver.save(sess,'./ckpt/tree_rnn_weights')
test_score = evaluate(model, test_set, sess)
print 'test score :', test_score, 'updated', epoch - best_valid_epoch, 'epochs ago with validation score', best_valid_score