def main(_):
path = os.getcwd()
father_path = os.path.dirname(path)
checkpoint_dir = father_path + "/checkpoint/"
lstm_log_dir = father_path + "/log/evaluation_charge_log/"
attr_log_dir = father_path + "/log/evaluation_attr_log/"
val_lstm_log_dir = father_path + "/log/validation_charge_log/"
val_attr_log_dir = father_path + "/log/validation_attr_log/"
create_dir([
checkpoint_dir, lstm_log_dir, attr_log_dir, val_lstm_log_dir,
val_attr_log_dir
])
restore = True
skiptrain = False
valmatrix = False
val_case = False
mixandmatrix = False
single_attr_log = False
bs = 32
perstep = 500
eva_number = 0
val_number = 0
mixcouple = [69, 71]
mixattr = [2, 3, 7]
single_attr = [4, 9]
word2id, word_embeddings, attr_table, x_train, y_train, y_attr_train, x_test, y_test, y_attr_test, x_val, y_val, y_attr_val, namehash, length_train, length_test, length_val = load_data_and_labels_fewshot(
)
id2word = {}
for i in word2id:
id2word[word2id[i]] = i
batches = batch_iter(list(zip(x_train, y_train, y_attr_train)),
global_config.batch_size, global_config.num_epochs)
lstm_config = model.lstm_Config()
lstm_config.num_steps = len(x_train[0])
lstm_config.hidden_size = len(word_embeddings[0])
lstm_config.vocab_size = len(word_embeddings)
lstm_config.num_classes = len(y_train[0])
lstm_config.num_epochs = 20
lstm_config.batch_size = bs
lstm_config.num_epochs = 20
lstm_eval_config = model.lstm_Config()
lstm_eval_config.keep_prob = 1.0
lstm_eval_config.num_steps = len(x_train[0])
lstm_eval_config.hidden_size = len(word_embeddings[0])
lstm_eval_config.vocab_size = len(word_embeddings)
lstm_eval_config.num_classes = len(y_train[0])
lstm_eval_config.batch_size = bs
lstm_eval_config.num_epochs = 20
zero_x = [0 for i in range(lstm_config.num_steps)]
zero_y = [0 for i in range(lstm_config.num_classes)]
lstm_count_tab = np.array([[0.0 for i in range(lstm_config.num_classes)]
for j in range(lstm_config.num_classes)])
total_tab = np.array([0.0 for i in range(lstm_config.num_classes)])
with tf.Graph().as_default():
tf.set_random_seed(6324)
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
sess = tf.Session(config=tf_config)
with sess.as_default():
lstm_initializer = tf.contrib.layers.xavier_initializer()
with tf.variable_scope("lstm_model",
reuse=None,
initializer=lstm_initializer):
print('lstm step1')
lstm_model = model.LSTM_MODEL(word_embeddings=word_embeddings,
attr_table=attr_table,
config=lstm_config)
print('lstm step2')
lstm_optimizer = tf.train.AdamOptimizer(lstm_config.lr)
print('lstm step3')
lstm_global_step = tf.Variable(0,
name="lstm_global_step",
trainable=False)
lstm_train_op = lstm_optimizer.minimize(
lstm_model.total_loss, global_step=lstm_global_step)
print('lstm step4')
saver = tf.train.Saver()
init_op = tf.initialize_all_variables()
sess.run(init_op)
best_macro_f1 = 0.0
if restore:
f_f1 = open(val_lstm_log_dir + 'best_macro_f1', 'r')
f1s = f_f1.readlines()
best_macro_f1 = float(
f1s[-1].strip().split(' ')[-1].strip('[').strip(']'))
f_f1.close()
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
pass
def lstm_train_step(x_batch, y_batch, y_attr_batch, length_batch):
"""
A single training step
"""
feed_dict = {
lstm_model.input_x: x_batch,
lstm_model.input_length: length_batch,
lstm_model.input_y: y_batch,
lstm_model.unmapped_input_attr: y_attr_batch,
lstm_model.keep_prob: 0.5,
}
_, step, total_loss, lstm_loss, attr_loss = sess.run([
lstm_train_op, lstm_global_step, lstm_model.total_loss,
lstm_model.lstm_loss, lstm_model.total_attr_loss
], feed_dict)
time_str = datetime.datetime.now().isoformat()
if step % 50 == 0:
#print sc
print((
"{}: step {}, total loss {:g}, lstm_loss {:g}, attr_loss {:g}"
.format(time_str, step, total_loss, lstm_loss,
attr_loss)))
return step
def lstm_dev_step(x_batch,
y_batch,
y_attr_batch,
length_batch,
writer=None):
"""
Evaluates model on a dev set
"""
feed_dict = {
lstm_model.input_x: x_batch,
lstm_model.input_length: length_batch,
lstm_model.input_y: y_batch,
lstm_model.unmapped_input_attr: y_attr_batch,
lstm_model.keep_prob: 1.0,
}
runlist = [lstm_model.predictions, lstm_model.ans]
lstm_p, lstm_l = sess.run(runlist, feed_dict=feed_dict)
return lstm_p, lstm_l
# batches = batch_iter(list(zip(x_train,y_train,y_attr_train,length_train)),lstm_config.batch_size, lstm_config.num_epochs)
print('Evaluation')
if mixandmatrix:
f_mix = open(lstm_log_dir + str(eva_number) + 'mixed.html',
'w')
f_mix.write(
'<head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"/></head>\n'
)
if single_attr_log:
f_single_attr = open(
lstm_log_dir + str(eva_number) + 'attr.html', 'w')
f_single_attr.write(
'<head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"/></head>\n'
)
all_count = 0.0
total_losses, lstm_losses, attr_losses = 0.0, 0.0, 0.0
lstm_prc = PrecRecallCounter(lstm_config.num_classes, lstm_log_dir,
'lstm', eva_number)
attr_prc = PrecRecallCounter(
[2 for temp in range(global_config.num_of_attr)], attr_log_dir,
'attr', eva_number)
lstm_matrix = [[0 for j in range(lstm_config.num_classes)]
for i in range(lstm_config.num_classes)]
num = int(len(y_test) / float(lstm_eval_config.batch_size))
print(num)
picked_x_index, picked_out, picked_label = [], [], []
for i in range(num):
if i % 100 == 0:
print(i)
begin = i * lstm_eval_config.batch_size
end = (i + 1) * lstm_eval_config.batch_size
y_batch_t = y_test[begin:end]
x_batch_t = x_test[begin:end]
y_attr_batch_t = y_attr_test[begin:end]
length_batch = length_test[begin:end]
lstm_p, lstm_l = lstm_dev_step(x_batch_t, y_batch_t,
y_attr_batch_t, length_batch)
diff = np.not_equal(lstm_p, lstm_l)
picked_out += list(lstm_p[diff])
picked_x_index += list(
np.where(diff)[0] + i * lstm_config.batch_size)
picked_label += list(lstm_l[diff])
print(picked_x_index, picked_out, picked_label)
print(picked_x_index, picked_out, picked_label)
with open('wrong_predictions.txt', 'w', encoding='utf-8') as f:
for i in list(zip(picked_x_index, picked_out, picked_label)):
f.write(str(i) + '\n')
def main(_):
path = os.getcwd()
father_path = os.path.dirname(path)
checkpoint_dir = father_path + "/checkpoint/"
lstm_log_dir = father_path + "/log/evaluation_charge_log/"
attr_log_dir = father_path + "/log/evaluation_attr_log/"
val_lstm_log_dir = father_path + "/log/validation_charge_log/"
val_attr_log_dir = father_path + "/log/validation_attr_log/"
create_dir([
checkpoint_dir, lstm_log_dir, attr_log_dir, val_lstm_log_dir,
val_attr_log_dir
])
restore = False
skiptrain = False
valmatrix = False
val_case = False
mixandmatrix = False
single_attr_log = False
bs = 32
perstep = 500
eva_number = 0
val_number = 0
mixcouple = [69, 71]
mixattr = [2, 3, 7]
single_attr = [4, 9]
word2id, word_embeddings, attr_table, x_train, y_train, y_attr_train, x_test, y_test, y_attr_test, x_val, y_val, y_attr_val, namehash, length_train, length_test, length_val = load_data_and_labels_fewshot(
)
id2word = {}
for i in word2id:
id2word[word2id[i]] = i
batches = batch_iter(list(zip(x_train, y_train, y_attr_train)),
global_config.batch_size, global_config.num_epochs)
lstm_config = model.lstm_Config()
lstm_config.num_steps = len(x_train[0])
lstm_config.hidden_size = len(word_embeddings[0])
lstm_config.vocab_size = len(word_embeddings)
lstm_config.num_classes = len(y_train[0])
lstm_config.num_epochs = 20
lstm_config.batch_size = bs
lstm_config.num_epochs = 20
lstm_eval_config = model.lstm_Config()
lstm_eval_config.keep_prob = 1.0
lstm_eval_config.num_steps = len(x_train[0])
lstm_eval_config.hidden_size = len(word_embeddings[0])
lstm_eval_config.vocab_size = len(word_embeddings)
lstm_eval_config.num_classes = len(y_train[0])
lstm_eval_config.batch_size = bs
lstm_eval_config.num_epochs = 20
zero_x = [0 for i in range(lstm_config.num_steps)]
zero_y = [0 for i in range(lstm_config.num_classes)]
lstm_count_tab = np.array([[0.0 for i in range(lstm_config.num_classes)]
for j in range(lstm_config.num_classes)])
total_tab = np.array([0.0 for i in range(lstm_config.num_classes)])
with tf.Graph().as_default():
tf.set_random_seed(6324)
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
sess = tf.Session(config=tf_config)
with sess.as_default():
lstm_initializer = tf.contrib.layers.xavier_initializer()
with tf.variable_scope("lstm_model",
reuse=None,
initializer=lstm_initializer):
print('lstm step1')
lstm_model = model.LSTM_MODEL(word_embeddings=word_embeddings,
attr_table=attr_table,
config=lstm_config)
print('lstm step2')
lstm_optimizer = tf.train.AdamOptimizer(lstm_config.lr)
print('lstm step3')
lstm_global_step = tf.Variable(0,
name="lstm_global_step",
trainable=False)
lstm_train_op = lstm_optimizer.minimize(
lstm_model.total_loss, global_step=lstm_global_step)
print('lstm step4')
saver = tf.train.Saver()
init_op = tf.initialize_all_variables()
sess.run(init_op)
best_macro_f1 = 0.0
if restore:
f_f1 = open(val_lstm_log_dir + 'best_macro_f1', 'r')
f1s = f_f1.readlines()
best_macro_f1 = float(
f1s[-1].strip().split(' ')[-1].strip('[').strip(']'))
f_f1.close()
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
pass
def lstm_train_step(x_batch, y_batch, y_attr_batch, length_batch):
"""
A single training step
"""
feed_dict = {
lstm_model.input_x: x_batch,
lstm_model.input_length: length_batch,
lstm_model.input_y: y_batch,
lstm_model.unmapped_input_attr: y_attr_batch,
lstm_model.keep_prob: 0.5,
}
_, step, total_loss, lstm_loss, attr_loss = sess.run([
lstm_train_op, lstm_global_step, lstm_model.total_loss,
lstm_model.lstm_loss, lstm_model.total_attr_loss
], feed_dict)
time_str = datetime.datetime.now().isoformat()
if step % 50 == 0:
#print sc
print(
"{}: step {}, total loss {:g}, lstm_loss {:g}, attr_loss {:g}"
.format(time_str, step, total_loss, lstm_loss,
attr_loss))
return step
def lstm_dev_step(x_batch,
y_batch,
y_attr_batch,
length_batch,
writer=None):
"""
Evaluates model on a dev set
"""
feed_dict = {
lstm_model.input_x: x_batch,
lstm_model.input_length: length_batch,
lstm_model.input_y: y_batch,
lstm_model.unmapped_input_attr: y_attr_batch,
lstm_model.keep_prob: 1.0,
}
runlist = [
lstm_model.predictions, lstm_model.attr_preds,
lstm_model.total_loss, lstm_model.lstm_loss,
lstm_model.total_attr_loss, lstm_model.attn_weights
]
lstm_p, attr_p, t_loss, l_loss, a_loss, attn_weights = sess.run(
runlist, feed_dict=feed_dict)
return lstm_p, attr_p, t_loss, l_loss, a_loss, attn_weights
batches = batch_iter(
list(zip(x_train, y_train, y_attr_train, length_train)),
lstm_config.batch_size, lstm_config.num_epochs)
for batch in batches:
x_batch, y_batch, y_attr_batch, length_batch = zip(*batch)
step = lstm_train_step(x_batch, y_batch, y_attr_batch,
length_batch)
if ((step % perstep) == 0) or (skiptrain):
print('Evaluation')
if mixandmatrix:
f_mix = open(
lstm_log_dir + str(eva_number) + 'mixed.html', 'w')
f_mix.write(
'<head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"/></head>\n'
)
if single_attr_log:
f_single_attr = open(
lstm_log_dir + str(eva_number) + 'attr.html', 'w')
f_single_attr.write(
'<head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"/></head>\n'
)
all_count = 0.0
total_losses, lstm_losses, attr_losses = 0.0, 0.0, 0.0
lstm_prc = PrecRecallCounter(lstm_config.num_classes,
lstm_log_dir, 'lstm',
eva_number)
attr_prc = PrecRecallCounter(
[2 for temp in range(global_config.num_of_attr)],
attr_log_dir, 'attr', eva_number)
lstm_matrix = [[0 for j in range(lstm_config.num_classes)]
for i in range(lstm_config.num_classes)]
num = int(len(y_test) / float(lstm_eval_config.batch_size))
print(num)
for i in range(num):
if i % 100 == 0:
print(i)
begin = i * lstm_eval_config.batch_size
end = (i + 1) * lstm_eval_config.batch_size
y_batch_t = y_test[begin:end]
x_batch_t = x_test[begin:end]
y_attr_batch_t = y_attr_test[begin:end]
length_batch = length_test[begin:end]
lstm_p, attr_p, t_loss, l_loss, a_loss, attn_weights = lstm_dev_step(
x_batch_t, y_batch_t, y_attr_batch_t, length_batch)
total_losses += t_loss
lstm_losses += l_loss
attr_losses += a_loss
for j in range(lstm_eval_config.batch_size):
indexes = np.flatnonzero(y_batch_t[j])
lstm_prc.multicount(lstm_p[j], indexes)
for index in indexes:
lstm_matrix[index][lstm_p[j]] += 1
for k in range(global_config.num_of_attr):
attr_prc.count(attr_p[j][k],
y_attr_batch_t[j][k], k)
if mixandmatrix:
mixed = ismixed(mixcouple, lstm_p[j], indexes)
if mixed:
wordcolor = '<font style="background: rgba(255, 255, 0, %f)">%s</font>\n'
f_mix.write('<p>' + str(lstm_p[j]) + ' ' +
str(indexes) + '</p>\n')
towrite = ''
for k in range(global_config.num_of_attr):
towrite = towrite + str(
attr_p[j][k]) + ' '
f_mix.write('<p>' + towrite + '</p>\n')
towrite = ''
for k in range(global_config.num_of_attr):
towrite = towrite + str(
y_attr_batch_t[j][k]) + ' '
f_mix.write('<p>' + towrite + '</p>\n')
for c in mixattr:
f_mix.write(wordcolor % (0, str(c)))
for w in range(len(x_batch_t[j])):
if w == length_batch[j]:
break
f_mix.write(
wordcolor %
(attn_weights[j][c][w] /
np.max(attn_weights[j][c]),
id2word[x_batch_t[j][w]]))
f_mix.write('<p>---</p>\n')
if single_attr_log:
for attr_index in single_attr:
if (attr_p[j][attr_index] !=
y_attr_batch_t[j][attr_index]) & (
y_attr_batch_t[j][attr_index]
!= 2):
wordcolor = '<font style="background: rgba(255, 255, 0, %f)">%s</font>\n'
f_single_attr.write(
'<p>' + str(indexes) +
str(attr_index) + ' ' +
str(attr_p[j][attr_index]) + ' ' +
str(y_attr_batch_t[j]
[attr_index]) + '</p>\n')
for w in range(len(x_batch_t[j])):
if w == length_batch[j]:
break
f_single_attr.write(
wordcolor %
(attn_weights[j][attr_index][w]
/ np.max(attn_weights[j]
[attr_index]),
id2word[x_batch_t[j][w]]))
f_single_attr.write('<p>---</p>\n')
begin = num * lstm_eval_config.batch_size
y_batch_t = y_test[begin:]
x_batch_t = x_test[begin:]
y_attr_batch_t = y_attr_test[begin:]
length_batch = length_test[begin:]
cl = len(y_batch_t)
for itemp in range(lstm_eval_config.batch_size - cl):
y_batch_t = np.append(y_batch_t, [y_batch_t[0]],
axis=0)
x_batch_t = np.append(x_batch_t, [x_batch_t[0]],
axis=0)
y_attr_batch_t = np.append(y_attr_batch_t,
[y_attr_batch_t[0]],
axis=0)
length_batch = np.append(length_batch,
[length_batch[0]],
axis=0)
lstm_p, attr_p, t_loss, l_loss, a_loss, attn_weights = lstm_dev_step(
x_batch_t, y_batch_t, y_attr_batch_t, length_batch)
total_losses += t_loss
lstm_losses += l_loss
attr_losses += a_loss
for jtemp in range(cl):
indexes = np.flatnonzero(y_batch_t[jtemp])
lstm_prc.multicount(lstm_p[jtemp], indexes)
for index in indexes:
lstm_matrix[index][lstm_p[jtemp]] += 1
for k in range(global_config.num_of_attr):
attr_prc.count(attr_p[jtemp][k],
y_attr_batch_t[jtemp][k], k)
lstm_prc.compute()
attr_prc.compute()
lstm_prc.output()
attr_prc.output()
if (lstm_prc.macro_f1[0] > best_macro_f1) or skiptrain:
best_macro_f1 = lstm_prc.macro_f1[0]
f_f1 = open(val_lstm_log_dir + 'best_macro_f1', 'a+')
f_f1.write('eva:' + str(eva_number) + ' ' +
str(best_macro_f1) + '\n')
f_f1.close()
print('Validation')
if not skiptrain:
saver.save(sess,
checkpoint_dir + 'model.ckpt',
global_step=step)
all_count = 0.0
total_losses, lstm_losses, attr_losses = 0.0, 0.0, 0.0
val_lstm_prc = PrecRecallCounter(
lstm_config.num_classes, val_lstm_log_dir, 'lstm',
val_number)
val_attr_prc = PrecRecallCounter(
[2 for temp in range(global_config.num_of_attr)],
val_attr_log_dir, 'attr', val_number)
val_lstm_matrix = [[
0 for j in range(lstm_config.num_classes)
] for i in range(lstm_config.num_classes)]
num = int(
len(y_val) / float(lstm_eval_config.batch_size))
if val_case:
f_case = open(
val_lstm_log_dir + 'case' + str(val_number),
'w')
print(num)
for i in range(num):
if i % 100 == 0:
print(i)
begin = i * lstm_eval_config.batch_size
end = (i + 1) * lstm_eval_config.batch_size
y_batch_t = y_val[begin:end]
x_batch_t = x_val[begin:end]
y_attr_batch_t = y_attr_val[begin:end]
length_batch = length_val[begin:end]
lstm_p, attr_p, t_loss, l_loss, a_loss, attn_weights = lstm_dev_step(
x_batch_t, y_batch_t, y_attr_batch_t,
length_batch)
total_losses += t_loss
lstm_losses += l_loss
attr_losses += a_loss
for j in range(lstm_eval_config.batch_size):
indexes = np.flatnonzero(y_batch_t[j])
val_lstm_prc.multicount(lstm_p[j], indexes)
for index in indexes:
val_lstm_matrix[index][lstm_p[j]] += 1
for k in range(global_config.num_of_attr):
val_attr_prc.count(attr_p[j][k],
y_attr_batch_t[j][k], k)
if val_case:
towrite = str(lstm_p[j]) + '\t' + str(
indexes[0]) + '\t' + str(
attr_p[j]) + '\t' + str(
y_attr_batch_t[j]) + '\t'
for w in range(len(x_batch_t[j])):
if w == length_batch[j]:
break
towrite = towrite + id2word[
x_batch_t[j][w]] + ' '
for temp_attr in range(
global_config.num_of_attr):
towrite = towrite + '\t'
for w in range(len(x_batch_t[j])):
if w == length_batch[j]:
break
towrite = towrite + str(
attn_weights[j][temp_attr][w] /
np.max(attn_weights[j]
[temp_attr])) + ' '
towrite = towrite + '\n'
f_case.write(towrite)
begin = num * lstm_eval_config.batch_size
y_batch_t = y_val[begin:]
x_batch_t = x_val[begin:]
y_attr_batch_t = y_attr_val[begin:]
length_batch = length_val[begin:]
cl = len(y_batch_t)
for itemp in range(lstm_eval_config.batch_size - cl):
y_batch_t = np.append(y_batch_t, [y_batch_t[0]],
axis=0)
x_batch_t = np.append(x_batch_t, [x_batch_t[0]],
axis=0)
y_attr_batch_t = np.append(y_attr_batch_t,
[y_attr_batch_t[0]],
axis=0)
length_batch = np.append(length_batch,
[length_batch[0]],
axis=0)
lstm_p, attr_p, t_loss, l_loss, a_loss, attn_weights = lstm_dev_step(
x_batch_t, y_batch_t, y_attr_batch_t, length_batch)
total_losses += t_loss
lstm_losses += l_loss
attr_losses += a_loss
for jtemp in range(cl):
indexes = np.flatnonzero(y_batch_t[jtemp])
val_lstm_prc.multicount(lstm_p[jtemp], indexes)
for index in indexes:
val_lstm_matrix[index][lstm_p[jtemp]] += 1
for k in range(global_config.num_of_attr):
val_attr_prc.count(attr_p[jtemp][k],
y_attr_batch_t[jtemp][k], k)
if val_case:
towrite = str(lstm_p[jtemp]) + '\t' + str(
indexes[0]) + '\t' + str(
attr_p[jtemp]) + '\t' + str(
y_attr_batch_t[jtemp]) + '\t'
for w in range(len(x_batch_t[jtemp])):
if w == length_batch[jtemp]:
break
towrite = towrite + id2word[
x_batch_t[jtemp][w]] + ' '
for temp_attr in range(
global_config.num_of_attr):
towrite = towrite + '\t'
for w in range(len(x_batch_t[jtemp])):
if w == length_batch[jtemp]:
break
towrite = towrite + str(
attn_weights[jtemp][temp_attr][w] /
np.max(attn_weights[jtemp]
[temp_attr])) + ' '
towrite = towrite + '\n'
f_case.write(towrite)
val_lstm_prc.compute()
val_attr_prc.compute()
val_lstm_prc.output()
val_attr_prc.output()
if valmatrix:
fm = open(
val_lstm_log_dir + str(val_number) + 'matrix',
'w')
for i in range(lstm_config.num_classes):
towrite = ""
for j in range(lstm_config.num_classes):
towrite = towrite + str(
val_lstm_matrix[i][j]) + ' '
towrite = towrite + '\n'
fm.write(towrite)
fm.close()
val_number += 1
if mixandmatrix:
fm = open(lstm_log_dir + str(eva_number) + 'matrix',
'w')
for i in range(lstm_config.num_classes):
towrite = ""
for j in range(lstm_config.num_classes):
towrite = towrite + str(
lstm_matrix[i][j]) + ' '
towrite = towrite + '\n'
fm.write(towrite)
fm.close()
num = float(num)
tn = datetime.datetime.now()
print(tn.isoformat())
print('loss total:{:g}, lstm:{:g}, attr:{:g}'.format(
total_losses / num, lstm_losses / num,
attr_losses / num))
if skiptrain:
break
eva_number += 1
def main(_):
path = os.getcwd()
father_path = os.path.dirname(path)
checkpoint_dir = father_path + "/checkpoint/"
lstm_log_dir = father_path + "/log/evaluation_charge_log/"
attr_log_dir = father_path + "/log/evaluation_attr_log/"
val_lstm_log_dir = father_path + "/log/validation_charge_log/"
val_attr_log_dir = father_path + "/log/validation_attr_log/"
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
if not os.path.exists(lstm_log_dir):
os.makedirs(lstm_log_dir)
if not os.path.exists(attr_log_dir):
os.makedirs(attr_log_dir)
if not os.path.exists(val_lstm_log_dir):
os.makedirs(val_lstm_log_dir)
if not os.path.exists(val_attr_log_dir):
os.makedirs(val_attr_log_dir)
restore = False
skiptrain = False
bs = 32 #batch size
perstep = 500
eva_number = 0
val_number = 0
#init
print "loading word embedding and data..."
word2id, word_embeddings, attr_table, x_train, y_train, y_attr_train, x_test, y_test, y_attr_test, x_val, y_val, y_attr_val, namehash, length_train, length_test, length_val = load_data_and_labels_fewshot(
)
id2word = {}
for i in word2id:
id2word[word2id[i]] = i
batches = batch_iter(list(zip(x_train, y_train, y_attr_train)),
global_config.batch_size, global_config.num_epochs)
lstm_config = model.lstm_Config()
lstm_config.num_steps = len(x_train[0])
lstm_config.hidden_size = len(word_embeddings[0])
lstm_config.vocab_size = len(word_embeddings)
lstm_config.num_classes = len(y_train[0])
lstm_config.num_epochs = 20
lstm_config.batch_size = bs
with tf.Graph().as_default():
tf.set_random_seed(6324)
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
sess = tf.Session(config=tf_config)
print "initializing model"
with sess.as_default():
lstm_initializer = tf.contrib.layers.xavier_initializer()
with tf.variable_scope("lstm_model",
reuse=None,
initializer=lstm_initializer):
lstm_model = model.LSTM_MODEL(word_embeddings=word_embeddings,
attr_table=attr_table,
config=lstm_config)
lstm_optimizer = tf.train.AdamOptimizer(lstm_config.lr)
lstm_global_step = tf.Variable(0,
name="lstm_global_step",
trainable=False)
lstm_train_op = lstm_optimizer.minimize(
lstm_model.total_loss, global_step=lstm_global_step)
saver = tf.train.Saver()
init_op = tf.initialize_all_variables()
sess.run(init_op)
best_macro_f1 = 0.0
if restore:
f_f1 = open(val_lstm_log_dir + 'best_macro_f1', 'r')
f1s = f_f1.readlines()
best_macro_f1 = float(
f1s[-1].strip().split(' ')[-1].strip('[').strip(']'))
f_f1.close()
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
pass
print "initialized"
batches = batch_iter(
list(zip(x_train, y_train, y_attr_train, length_train)),
lstm_config.batch_size, lstm_config.num_epochs)
for batch in batches:
x_batch, y_batch, y_attr_batch, length_batch = zip(*batch)
step = lstm_train_step(lstm_train_op, lstm_global_step,
lstm_model, sess, x_batch, y_batch,
y_attr_batch, length_batch)
if ((step % perstep) == 0) or (skiptrain):
new_marco_f1 = evaluation(eva_number, lstm_model, sess,
lstm_config, lstm_log_dir,
attr_log_dir, y_test, x_test,
y_attr_test, length_test)
eva_number += 1
#when model get the best performance on test set, validate it on the validation set
if (new_marco_f1 > best_macro_f1) or skiptrain:
if not skiptrain:
saver.save(sess,
checkpoint_dir + 'model.ckpt',
global_step=step)
best_macro_f1 = new_marco_f1
validation(val_number, lstm_model, sess, lstm_config,
val_lstm_log_dir, val_attr_log_dir, y_val,
x_val, y_attr_val, length_val)
val_number += 1