def test(self, sess, test_xy, load_init_model=False):
if load_init_model:
ckpt = tf.train.get_checkpoint_state(self.model_dir + '/{}'.format(self.model_name))
print('model file: ', ckpt.model_checkpoint_path)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
self.saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise RuntimeError('not exist model ...')
acc_sum = 0
sample_sum = 0
for batch in batch_iter(test_xy[0], test_xy[1], test_xy[2], batch_size=100):
acc_num = self.model_test(sess, batch)
acc_sum += acc_num
sample_sum += len(batch[1])
return acc_sum/sample_sum
def get_represent(self, sess, train_xy):
ckpt = tf.train.get_checkpoint_state(self.model_dir +
'/{}'.format(self.model_name))
print('model file: ', ckpt.model_checkpoint_path)
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
self.saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise RuntimeError('not exist model ...')
represent = []
for batch in batch_iter(train_xy[0],
train_xy[1],
train_xy[2],
batch_size=100):
repre = self.model_get_represent(sess, batch)
represent += repre.tolist()
return represent
def Textcnn_test():
if FLAGS.vocab_dir is None:
words = tools.build_vocab(train_data=FLAGS.train_data,
vocab_dir=FLAGS.vocab_dir) ### 制作词汇表
else:
words = tools.read_file(FLAGS.vocab_dir)
vocab_size = len(words)
print("Test words : ", vocab_size)
test_X, test_Y = tools.create_voabulary(train_data=FLAGS.test_data,
vocab_data=FLAGS.vocab_dir,
max_length=config.seq_length)
input_x = tf.placeholder(tf.int32, [None, config.seq_length],
name='input_x')
input_y = tf.placeholder(tf.float32, [None, config.num_classes],
name='input_y')
model_path = 'checkpoints/TextCNNnet_2019-11-01-15-31-50.ckpt-4000'
save_path = model_path
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
textcnn = TextCNN(config, vocab_size, keep_prob=1.0)
logits = textcnn.cnn(input_x) ### (?,10)
loss = textcnn_loss(logits=logits, label=input_y)
acc = textcnn_acc(logits=logits, labels=input_y)
saver = tf.train.Saver()
saver.restore(sess=sess, save_path=save_path)
batch_test = tools.batch_iter(test_X, test_Y,
config.batch_size) ### 生成批次数据
i = 0
all_acc = 0
for x_batch, y_batch in batch_test:
test_loss, test_acc = sess.run([loss, acc],
feed_dict={
input_x: x_batch,
input_y: y_batch
})
all_acc = all_acc + test_acc
i += 1
print("Average acc : ", (all_acc / i))
def train(self, sess, train_xy, batch_size=32, summary_writer=None):
windows = 1000
cost_sum = 0.0
acc_sum = 0
sample_cnt = 0
for batch in batch_iter(train_xy[0], train_xy[1], train_xy[2], batch_size=batch_size, shuffle=True):
_, summary, step, cost, acc_num = self.model_train(sess, batch)
summary_writer.add_summary(summary, step)
sample_cnt += len(batch[1])
if sample_cnt % windows:
cost_sum += cost
acc_sum += acc_num
else:
cost_sum = 0.0
acc_sum = 0
sample_cnt = 0
# print ("cost: {}".format(cost))
return cost_sum/sample_cnt, acc_sum/sample_cnt
def Textcnn_train():
########### load data ###################
if not os.path.exists(FLAGS.vocab_dir):
words = tools.build_vocab(train_data=FLAGS.train_data,
vocab_dir=FLAGS.vocab_dir) ### 制作词汇表
else:
words = tools.read_file(FLAGS.vocab_dir)
vocab_size = len(words)
train_X, train_Y = tools.create_voabulary(train_data=FLAGS.train_data,
vocab_data=FLAGS.vocab_dir,
max_length=config.seq_length)
val_X, val_Y = tools.create_voabulary(train_data=FLAGS.val_data,
vocab_data=FLAGS.vocab_dir,
max_length=config.seq_length)
#trainX = pad_sequences(train_X, maxlen=200, value=0.) # padding to max length
#test_X = pad_sequences(test_X, maxlen=200, value=0.) # padding to max length
print("Data deal down!")
###############################################################################
input_x = tf.placeholder(tf.int32, [None, config.seq_length],
name='input_x')
input_y = tf.placeholder(tf.float32, [None, config.num_classes],
name='input_y')
textcnn = TextCNN(config, vocab_size, keep_prob=config.dropout_keep_prob)
logits = textcnn.cnn(input_x) ### (?,10)
loss = textcnn_loss(logits=logits, label=input_y)
############# 计算 acc ######################################
acc = textcnn_acc(logits=logits, labels=input_y)
######################################################
global_step = tf.Variable(0, name='global_step', trainable=False)
learning_rate = tf.train.exponential_decay(
learning_rate=FLAGS.learning_rate,
global_step=global_step,
decay_steps=2000,
decay_rate=0.1,
staircase=True)
optim = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(
loss=loss, global_step=global_step)
tensorboard_dir = 'tensorboard/textcnn'
tf.summary.scalar("loss", loss)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(tensorboard_dir)
saver = tf.train.Saver(max_to_keep=3) ### 保存模型
model_save_dir = 'checkpoints/'
train_start_time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
model_name = 'TextCNNnet_{:s}.ckpt'.format(str(train_start_time))
model_save_path = os.path.join(model_save_dir, model_name)
model_restore_path = './checkpoints/TextCNNnet_2019-11-01-15-31-50.ckpt-4000'
##### 创建日志
logging.basicConfig(
filename='./checkpoints/' + model_name + '.log',
format='%(asctime)s - %(pathname)s - %(levelname)s: %(message)s',
level=logging.DEBUG,
filemode='a',
datefmt='%Y-%m-%d%I:%M:%S %p')
logging.info('###### Next Is Training Infomation ###################')
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
sess = tf.Session(config=sess_config)
with sess.as_default():
if not FLAGS.model_store:
step = 0
init = tf.global_variables_initializer()
sess.run(init)
writer.add_graph(sess.graph)
else:
saver.restore(sess=sess, save_path=model_restore_path)
step = sess.run(tf.train.get_global_step())
writer.add_graph(sess.graph)
print('First step is:', step)
num_batch = int(
(len(train_X) - 1) / config.batch_size) + 1 ### 总batch数
acc_begain = 0
for epoch in range(config.epochs):
batch_train = tools.batch_iter(train_X, train_Y,
config.batch_size) ### 生成批次数据
Begain_learn_rate = FLAGS.learning_rate
for x_batch, y_batch in batch_train:
step += 1
_, learn_rate, train_loss_value, train_pred, train_acc, merge_summary_value = sess.run(
[optim, learning_rate, loss, logits, acc, merged_summary],
feed_dict={
input_x: x_batch,
input_y: y_batch
})
if Begain_learn_rate != learn_rate:
information = '############ New Learning_Rate {:6f} in step {:d} ###########'.format(
learn_rate, step)
logging.info(information)
print(information)
Begain_learn_rate = learn_rate
if step % 10 == 0:
information = '## Epoch {:d} Step_Train / Total_Batch: {:d} / {:d} train_loss= {:5f} train_acc={:5f}'.\
format(int(step/num_batch),step, num_batch, train_loss_value, train_acc)
logging.info(information)
print(information)
if step % 500 == 0: ### 每 500 步进行一次验证，并保存最优模型
val_acc_all = 0
val_loss_all = 0
val_step = 0
batch_val = tools.batch_iter(val_X, val_Y,
config.batch_size) ### 生成批次数据
for x_val, y_val in batch_val:
if x_val.shape[0] < config.batch_size:
pass
else:
_, val_loss_value, val_pred, val_acc, merge_summary_value = sess.run(
[optim, loss, logits, acc, merged_summary],
feed_dict={
input_x: x_val,
input_y: y_val
})
writer.add_summary(merge_summary_value, step)
val_acc_all = val_acc_all + val_acc
val_loss_all = val_loss_all + val_loss_value
val_step += 1
ave_acc = val_acc_all / val_step
ave_loss = val_loss_all / val_step
if (ave_acc - acc_begain) > 0.001:
acc_begain = ave_acc
saver.save(sess, model_save_path, global_step=step)
tf.train.write_graph(sess.graph_def, '',
'./checkpoints/textcnn_graph.pb')
information = '############ Val_loss = {:5f} Val_acc = {:5f} ##################'.format(
ave_loss, ave_acc)
logging.info(information)
print(information)
