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train_GRU.py
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import tensorflow as tf
import numpy as np
import time
import datetime
import os
import network
from tensorflow.contrib.tensorboard.plugins import projector
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('summary_dir', '.','path to store summary')
#change the name to who you want to send
#tf.app.flags.DEFINE_string('wechat_name', 'Tang-24-0325','the user you want to send info to')
tf.app.flags.DEFINE_string('wechat_name', 'filehelper','the user you want to send info to')
#if you want to try itchat, please set it to True
itchat_run = False
if itchat_run:
import itchat
def main(_):
# the path to save models
save_path = './model/'
print 'reading wordembedding'
wordembedding = np.load('./data/vec.npy')
print 'reading training data'
train_y = np.load('./data/small_y.npy')
train_word = np.load('./data/small_word.npy')
train_pos1 = np.load('./data/small_pos1.npy')
train_pos2 = np.load('./data/small_pos2.npy')
settings = network.Settings()
settings.vocab_size = len(wordembedding)
settings.num_classes = len(train_y[0])
big_num = settings.big_num
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
initializer = tf.contrib.layers.xavier_initializer()
with tf.variable_scope("model", reuse=None, initializer = initializer):
m = network.GRU(is_training=True, word_embeddings = wordembedding, settings = settings)
global_step = tf.Variable(0, name="global_step", trainable=False)
optimizer = tf.train.AdamOptimizer(0.001)
#train_op=optimizer.minimize(m.total_loss,global_step=global_step)
train_op=optimizer.minimize(m.final_loss,global_step=global_step)
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver(max_to_keep=None)
#merged_summary = tf.summary.merge_all()
merged_summary = tf.merge_all_summaries()
summary_writer = tf.train.SummaryWriter(FLAGS.summary_dir+'/train_loss',sess.graph)
#summary for embedding
#it's not available in tf 0.11,(because there is no embedding panel in 0.11's tensorboard) so I delete it =.=
#you can try it on 0.12 or higher versions but maybe you should change some function name at first.
# summary_embed_writer = tf.train.SummaryWriter('./model',sess.graph)
# config = projector.ProjectorConfig()
# embedding_conf = config.embedding.add()
# embedding_conf.tensor_name = 'word_embedding'
# embedding_conf.metadata_path = './data/metadata.tsv'
# projector.visualize_embeddings(summary_embed_writer, config)
def train_step(word_batch, pos1_batch, pos2_batch, y_batch,big_num):
feed_dict = {}
total_shape = []
total_num = 0
total_word = []
total_pos1 = []
total_pos2 = []
for i in range(len(word_batch)):
total_shape.append(total_num)
total_num += len(word_batch[i])
for word in word_batch[i]:
total_word.append(word)
for pos1 in pos1_batch[i]:
total_pos1.append(pos1)
for pos2 in pos2_batch[i]:
total_pos2.append(pos2)
total_shape.append(total_num)
total_shape = np.array(total_shape)
total_word = np.array(total_word)
total_pos1 = np.array(total_pos1)
total_pos2 = np.array(total_pos2)
feed_dict[m.total_shape] = total_shape
feed_dict[m.input_word] = total_word
feed_dict[m.input_pos1] = total_pos1
feed_dict[m.input_pos2] = total_pos2
feed_dict[m.input_y] = y_batch
temp, step, loss, accuracy,summary,l2_loss,final_loss= sess.run([train_op, global_step, m.total_loss, m.accuracy,merged_summary,m.l2_loss,m.final_loss], feed_dict)
time_str = datetime.datetime.now().isoformat()
accuracy = np.reshape(np.array(accuracy),(big_num))
acc = np.mean(accuracy)
summary_writer.add_summary(summary,step)
if step % 50 == 0:
tempstr = "{}: step {}, softmax_loss {:g}, acc {:g}".format(time_str, step, loss, acc)
print(tempstr)
if itchat_run:
itchat.send(tempstr,FLAGS.wechat_name)
for one_epoch in range(settings.num_epochs):
if itchat_run:
itchat.send('epoch '+str(one_epoch)+' starts!',FLAGS.wechat_name)
temp_order = range(len(train_word))
np.random.shuffle(temp_order)
for i in range(int(len(temp_order)/float(settings.big_num))):
temp_word = []
temp_pos1 = []
temp_pos2 = []
temp_y = []
temp_input = temp_order[i*settings.big_num:(i+1)*settings.big_num]
for k in temp_input:
temp_word.append(train_word[k])
temp_pos1.append(train_pos1[k])
temp_pos2.append(train_pos2[k])
temp_y.append(train_y[k])
num = 0
for single_word in temp_word:
num += len(single_word)
if num > 1500:
print 'out of range'
continue
temp_word = np.array(temp_word)
temp_pos1 = np.array(temp_pos1)
temp_pos2 = np.array(temp_pos2)
temp_y = np.array(temp_y)
train_step(temp_word,temp_pos1,temp_pos2,temp_y,settings.big_num)
current_step = tf.train.global_step(sess, global_step)
if current_step > 9000 and current_step%500==0:
#if current_step == 50:
print 'saving model'
path = saver.save(sess,save_path +'ATT_GRU_model',global_step=current_step)
tempstr = 'have saved model to '+path
print tempstr
if itchat_run:
itchat.send('training has been finished!',FLAGS.wechat_name)
if __name__ == "__main__":
if itchat_run:
itchat.auto_login(hotReload=True,enableCmdQR=2)
tf.app.run()