def dev_point_wise():
if FLAGS.data == 'TREC' or FLAGS.data == 'sst2':
train, dev, test = load_trec_sst2(FLAGS.data)
else:
train, dev = load(FLAGS.data)
q_max_sent_length = max(
map(lambda x: len(x), train['question'].str.split()))
print(q_max_sent_length)
print(len(train))
print('train question unique:{}'.format(len(train['question'].unique())))
print('train length', len(train))
print('dev length', len(dev))
if FLAGS.data == 'TREC' or FLAGS.data == 'sst2':
alphabet, embeddings = prepare([train, dev, test],
max_sent_length=q_max_sent_length,
dim=FLAGS.embedding_dim,
is_embedding_needed=True,
fresh=True)
else:
alphabet, embeddings = prepare([train, dev],
max_sent_length=q_max_sent_length,
dim=FLAGS.embedding_dim,
is_embedding_needed=True,
fresh=True)
print('alphabet:', len(alphabet))
with tf.Graph().as_default():
with tf.device("/gpu:0"):
session_conf = tf.ConfigProto()
session_conf.allow_soft_placement = FLAGS.allow_soft_placement
session_conf.log_device_placement = FLAGS.log_device_placement
session_conf.gpu_options.allow_growth = True
sess = tf.Session(config=session_conf)
now = int(time.time())
timeArray = time.localtime(now)
timeStamp1 = time.strftime("%Y%m%d%H%M%S", timeArray)
timeDay = time.strftime("%Y%m%d", timeArray)
print(timeStamp1)
with sess.as_default(), open(precision, "w") as log:
log.write(str(FLAGS.__flags) + '\n')
cnn = CNN(max_input_left=q_max_sent_length,
vocab_size=len(alphabet),
embeddings=embeddings,
embedding_size=FLAGS.embedding_dim,
batch_size=FLAGS.batch_size,
filter_sizes=list(map(int,
FLAGS.filter_sizes.split(","))),
num_filters=FLAGS.num_filters,
l2_reg_lambda=FLAGS.l2_reg_lambda,
is_Embedding_Needed=True,
trainable=FLAGS.trainable,
dataset=FLAGS.data,
extend_feature_dim=FLAGS.extend_feature_dim)
cnn.build_graph()
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads_and_vars = optimizer.compute_gradients(cnn.loss)
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
sess.run(tf.global_variables_initializer())
acc_max = 0.0000
for i in range(FLAGS.num_epochs):
datas = batch_gen_with_point_wise(train,
alphabet,
FLAGS.batch_size,
q_len=q_max_sent_length)
for data in datas:
feed_dict = {
cnn.question: data[0],
cnn.input_y: data[1],
cnn.q_position: data[2],
cnn.dropout_keep_prob: FLAGS.dropout_keep_prob
}
_, step, loss, accuracy = sess.run(
[train_op, global_step, cnn.loss, cnn.accuracy],
feed_dict)
time_str = datetime.datetime.now().isoformat()
print("{}: step {}, loss {:g}, acc {:g} ".format(
time_str, step, loss, accuracy))
predicted = predict(sess, cnn, train, alphabet,
FLAGS.batch_size, q_max_sent_length)
predicted_label = np.argmax(predicted, 1)
acc_train = accuracy_score(predicted_label, train['flag'])
predicted_dev = predict(sess, cnn, dev, alphabet,
FLAGS.batch_size, q_max_sent_length)
predicted_label = np.argmax(predicted_dev, 1)
acc_dev = accuracy_score(predicted_label, dev['flag'])
if acc_dev > acc_max:
tf.train.Saver().save(sess,
"model_save/model",
write_meta_graph=True)
acc_max = acc_dev
print("{}:train epoch:acc {}".format(i, acc_train))
print("{}:dev epoch:acc {}".format(i, acc_dev))
line2 = " {}:epoch: acc{}".format(i, acc_dev)
log.write(line2 + '\n')
log.flush()
acc_flod.append(acc_max)
log.close()
line2 = " {}:epoch: acc{}".format(i, acc_dev)
log.write(line2 + '\n')
log.flush()
acc_flod.append(acc_max)
log.close()
if __name__ == '__main__':
if FLAGS.data == 'TREC' or FLAGS.data == 'sst2':
for attr, value in sorted(FLAGS.__flags.items()):
print(("{}={}".format(attr.upper(), value)))
dev_point_wise()
ckpt = tf.train.get_checkpoint_state("model_save" + '/')
saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path +
'.meta')
train, dev, test = load_trec_sst2(FLAGS.data)
q_max_sent_length = max(
map(lambda x: len(x), train['question'].str.split()))
alphabet, embeddings = prepare([train, test, dev],
max_sent_length=q_max_sent_length,
dim=FLAGS.embedding_dim,
is_embedding_needed=True,
fresh=True)
with tf.Session() as sess:
saver.restore(sess, ckpt.model_checkpoint_path)
graph = tf.get_default_graph()
scores = []
question = graph.get_operation_by_name('input_question').outputs[0]
q_position = graph.get_operation_by_name('q_position').outputs[0]
dropout_keep_prob = graph.get_operation_by_name(
'dropout_keep_prob').outputs[0]