def main():
data_loader = InputHelper(log=log)
data_loader.load_embedding(FLAGS.embedding_file, FLAGS.embedding_size)
data_loader.load_label_dictionary(FLAGS.label_dic)
x, y, x_w_p, x_s_p = data_loader.load_valid(FLAGS.valid_file,
FLAGS.interaction_rounds,
FLAGS.sequence_length)
FLAGS.embeddings = data_loader.embeddings
FLAGS.vocab_size = len(data_loader.word2idx)
FLAGS.n_classes = len(data_loader.label_dictionary)
model = BiRNN(embedding_size=FLAGS.embedding_size,
rnn_size=FLAGS.rnn_size,
layer_size=FLAGS.layer_size,
vocab_size=FLAGS.vocab_size,
attn_size=FLAGS.attn_size,
sequence_length=FLAGS.sequence_length,
n_classes=FLAGS.n_classes,
interaction_rounds=FLAGS.interaction_rounds,
batch_size=FLAGS.batch_size,
embeddings=FLAGS.embeddings,
grad_clip=FLAGS.grad_clip,
learning_rate=FLAGS.learning_rate)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(FLAGS.save_dir)
model_path = FLAGS.save_dir + '/model.ckpt-45'
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, model_path)
labels = model.inference(sess, y, x, x_w_p, x_s_p)
corrcet_num = 0
for i in range(len(labels)):
if labels[i] == y[i]:
corrcet_num += 1
print('eval_acc = {:.3f}'.format(corrcet_num * 1.0 / len(labels)))
data_loader.output_result(labels, FLAGS.valid_file, FLAGS.result_file)
def train():
#train data load
data_loader = InputHelper(log=log)
data_loader.load_embedding(FLAGS.embedding_file,FLAGS.embedding_size)
train_data = data_loader.load_data(FLAGS.data_dir+'/'+FLAGS.train_file, FLAGS.data_dir+'/',FLAGS.interaction_rounds,FLAGS.sequence_length)
x_batch,y_batch,train_interaction_point, train_word_point = data_loader.generate_batches(train_data,FLAGS.batch_size,FLAGS.interaction_rounds)
FLAGS.vocab_size = len(data_loader.word2idx)
FLAGS.n_classes = len(data_loader.label_dictionary)
print FLAGS.n_classes
FLAGS.num_batches = data_loader.num_batches
FLAGS.embeddings = data_loader.embeddings
# test data load
test_data_loader = InputHelper(log=log)
test_data_loader.load_info(embeddings=FLAGS.embeddings,word2idx=data_loader.word2idx,idx2word=data_loader.idx2word,
label_dictionary=data_loader.label_dictionary)
test_data = test_data_loader.load_data(FLAGS.data_dir + '/' + FLAGS.test_file, FLAGS.data_dir + '/',
FLAGS.interaction_rounds, FLAGS.sequence_length)
test_x_batch, test_y_batch, test_interaction_point,test_word_point = test_data_loader.generate_batches(test_data, FLAGS.batch_size, FLAGS.interaction_rounds)
# Define specified Model
model = BiRNN(embedding_size=FLAGS.embedding_size, rnn_size=FLAGS.rnn_size, layer_size=FLAGS.layer_size,
vocab_size=FLAGS.vocab_size, attn_size=FLAGS.attn_size, sequence_length=FLAGS.sequence_length,
n_classes=FLAGS.n_classes, interaction_rounds=FLAGS.interaction_rounds, batch_size=FLAGS.batch_size,
embeddings=FLAGS.embeddings,grad_clip=FLAGS.grad_clip, learning_rate=FLAGS.learning_rate)
# define value for tensorboard
tf.summary.scalar('train_loss', model.cost)
tf.summary.scalar('accuracy', model.accuracy)
merged = tf.summary.merge_all()
# 调整GPU内存分配方案
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
train_writer = tf.summary.FileWriter(FLAGS.log_dir, sess.graph)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=1000)
total_steps = FLAGS.num_epochs * FLAGS.num_batches
for e in xrange(FLAGS.num_epochs):
data_loader.reset_batch()
e_avg_loss = []
t_acc = []
start = time.time()
num_tt = []
# w=open('temp/pre'+str(e)+'.txt','w')
for b in xrange(FLAGS.num_batches):
x, y, z,m = data_loader.next_batch(x_batch,y_batch,train_interaction_point,train_word_point)
feed = {model.input_data:x, model.targets:y, model.output_keep_prob:FLAGS.dropout_keep_prob, model.word_point:m, model.sentence_point:z}
train_loss,t_accs,yy,yyy, summary, _ = sess.run([model.cost, model.accuracy,model.y_results,
model.y_tr, merged, model.train_op], feed_dict=feed)
e_avg_loss.append(train_loss)
t_acc.append(t_accs)
global_step = e * FLAGS.num_batches + b
if global_step % 20 == 0:
train_writer.add_summary(summary, e * FLAGS.num_batches + b)
num_t = 0
for i in range(len(yy)):
if yy[i] == yyy[i] and yy[i] != 4:
num_t+=1
num_tt.append(num_t*1.0/len(yy))
# w.write('predict '+str(len(yy))+'\n')
# for y in yy:
# w.write(str(y)+'\t')
# w.write('\ntrue '+str(len(yyy))+'\n')
# for ys in yyy:
# w.write(str(ys)+'\t')
# w.write('\n')
# w.close()
# model test
test_data_loader.reset_batch()
test_accuracy = []
test_a = []
for i in xrange(test_data_loader.num_batches):
test_x, test_y, test_z, test_m = test_data_loader.next_batch(test_x_batch,test_y_batch,test_interaction_point,test_word_point)
feed = {model.input_data:test_x, model.targets:test_y, model.output_keep_prob:1.0,model.word_point:test_m, model.sentence_point:test_z}
accuracy,y_p,y_r = sess.run([model.accuracy,model.y_results,model.y_tr],feed_dict=feed)
test_accuracy.append(accuracy)
num_test = 0
for j in range(len(y_p)):
if y_p[j] == y_r[j] and y_p[j] != 4:
num_test+=1
test_a.append(num_test*1.0/len(y_p))
end = time.time()
num_tt_acc = np.average(num_tt)
num_test_acc = np.average(test_a)
avg_loss = np.average(e_avg_loss)
print('e{},loss = {:.3f}, train_acc = {:.3f}, test_acc = {:.3f}, time/epoch'.format(e,avg_loss,num_tt_acc,num_test_acc,end - start ))
#print and save
# avg_loss = np.average(e_avg_loss)
# t_avg_acc = np.average(t_acc)
# log.info('epoch {}, train_loss = {:.3f},train_acc = {:.3f} test_accuracy:{:.3f}, time/epoch = {:.3f}'.format(e, avg_loss,t_avg_acc,np.average(test_accuracy), end - start))
checkpoint_path = os.path.join(FLAGS.save_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=e)