def train():
# Training procedure
# ======================================================
# 设定最小显存使用量
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
config = CNNConfig('CHAR-RANDOM')
cnn = TextCNN(config)
cnn.prepare_data()
cnn.setCNN()
print('Setting Tensorboard and Saver...')
# 设置Saver和checkpoint来保存模型
# ===================================================
checkpoint_dir = os.path.join(os.path.abspath("checkpoints"),
"textcnn")
checkpoint_prefix = os.path.join(checkpoint_dir, cnn.train_mode)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.global_variables())
# =====================================================
# 配置Tensorboard，重新训练时，请将tensorboard文件夹删除，不然图会覆盖
# ====================================================================
train_tensorboard_dir = 'tensorboard/textcnn/train/' + config.train_mode
valid_tensorboard_dir = 'tensorboard/textcnn/valid/' + config.train_mode
if not os.path.exists(train_tensorboard_dir):
os.makedirs(train_tensorboard_dir)
if not os.path.exists(valid_tensorboard_dir):
os.makedirs(valid_tensorboard_dir)
# 训练结果记录
log_file = open(valid_tensorboard_dir + '/log.txt', mode='w')
merged_summary = tf.summary.merge([
tf.summary.scalar('Trainloss', cnn.loss),
tf.summary.scalar('Trainaccuracy', cnn.accuracy)
])
merged_summary_t = tf.summary.merge([
tf.summary.scalar('Testloss', cnn.loss),
tf.summary.scalar('Testaccuracy', cnn.accuracy)
])
train_summary_writer = tf.summary.FileWriter(train_tensorboard_dir,
sess.graph)
# =========================================================================
global_step = tf.Variable(0, trainable=False)
# 保证Batch normalization的执行
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(
update_ops): # 保证train_op在update_ops执行之后再执行。
train_op = tf.train.AdamOptimizer(config.learning_rate).minimize(
cnn.loss, global_step)
# 训练步骤
def train_step(batch_x, batch_y, keep_prob=config.dropout_keep_prob):
feed_dict = {
cnn.input_x: batch_x,
cnn.labels: batch_y,
cnn.dropout_keep_prob: keep_prob,
cnn.training: True
}
sess.run(train_op, feed_dict=feed_dict)
step, loss, accuracy, summery = sess.run(
[global_step, cnn.loss, cnn.accuracy, merged_summary],
feed_dict={
cnn.input_x: batch_x,
cnn.labels: batch_y,
cnn.dropout_keep_prob: 1.0,
cnn.training: False
})
t = datetime.datetime.now().strftime('%m-%d %H:%M')
print('TRAIN %s: epoch: %d, step: %d, loss: %f, accuracy: %f' %
(t, epoch, step, loss, accuracy))
# 把结果写入Tensorboard中
train_summary_writer.add_summary(summery, step)
def test_step(batch_x, batch_y):
step, loss, accuracy, summery = sess.run(
[global_step, cnn.loss, cnn.accuracy, merged_summary_t],
feed_dict={
cnn.input_x: batch_x,
cnn.labels: batch_y,
cnn.dropout_keep_prob: 1.0,
cnn.training: False
})
t = datetime.datetime.now().strftime('%m-%d %H:%M')
print('TEST %s: epoch: %d, step: %d, loss: %f, accuracy: %f' %
(t, epoch, step, loss, accuracy))
# 把结果写入Tensorboard中
train_summary_writer.add_summary(summery, step)
return accuracy
print('Start training TextCNN, training mode=' + cnn.train_mode)
sess.run(tf.global_variables_initializer())
last = 0
# Training loop
for epoch in range(1000000):
batch_x, batch_y = train_dataset.next_batch(128)
train_step(batch_x, batch_y, config.dropout_keep_prob)
if epoch % 10 == 0:
batch_x, batch_y = test_dataset.next_batch(128)
accuracy = test_step(batch_x, batch_y)
if accuracy > last:
path = saver.save(sess,
checkpoint_prefix,
global_step=global_step)
print("Saved model checkpoint to {}\n".format(path))
last = accuracy
train_summary_writer.close()
log_file.close()
def train():
# Training procedure
# ======================================================
# 设定最小显存使用量
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
config = CNNConfig('MULTI')
cnn = TextCNN(config)
cnn.prepare_data()
cnn.setCNN()
print('Setting Tensorboard and Saver...')
# 设置Saver和checkpoint来保存模型
# ===================================================
checkpoint_dir = os.path.join(os.path.abspath("checkpoints"),
"textcnn")
checkpoint_prefix = os.path.join(checkpoint_dir, cnn.train_mode)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
saver = tf.train.Saver(tf.global_variables())
# =====================================================
# 配置Tensorboard，重新训练时，请将tensorboard文件夹删除，不然图会覆盖
# ====================================================================
train_tensorboard_dir = 'tensorboard/textcnn/train/' + config.train_mode
valid_tensorboard_dir = 'tensorboard/textcnn/valid/' + config.train_mode
if not os.path.exists(train_tensorboard_dir):
os.makedirs(train_tensorboard_dir)
if not os.path.exists(valid_tensorboard_dir):
os.makedirs(valid_tensorboard_dir)
# 训练结果记录
log_file = open(valid_tensorboard_dir + '/log.txt', mode='w')
merged_summary = tf.summary.merge([
tf.summary.scalar('loss', cnn.loss),
tf.summary.scalar('accuracy', cnn.accuracy)
])
train_summary_writer = tf.summary.FileWriter(train_tensorboard_dir,
sess.graph)
# =========================================================================
global_step = tf.Variable(0, trainable=False)
# 保证Batch normalization的执行
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(
update_ops): # 保证train_op在update_ops执行之后再执行。
train_op = tf.train.AdamOptimizer(config.learning_rate).minimize(
cnn.loss, global_step)
# 训练步骤
def train_step(batch_x, batch_y, keep_prob=config.dropout_keep_prob):
feed_dict = {
cnn.input_x: batch_x,
cnn.labels: batch_y,
cnn.dropout_keep_prob: keep_prob,
cnn.training: True
}
sess.run(train_op, feed_dict=feed_dict)
step, loss, accuracy, summery = sess.run(
[global_step, cnn.loss, cnn.accuracy, merged_summary],
feed_dict={
cnn.input_x: batch_x,
cnn.labels: batch_y,
cnn.dropout_keep_prob: 1.0,
cnn.training: False
})
t = datetime.datetime.now().strftime('%m-%d %H:%M')
print('%s: epoch: %d, step: %d, loss: %f, accuracy: %f' %
(t, epoch, step, loss, accuracy))
# 把结果写入Tensorboard中
train_summary_writer.add_summary(summery, step)
# 验证步骤
def valid_step(next_valid_element):
# 把valid_loss和valid_accuracy归0
valid_loss = 0.0
valid_accuracy = 0.0
valid_precision = 0.0
valid_recall = 0.0
valid_f1_score = 0.0
i = 0
while True:
try:
lines = sess.run(next_valid_element)
batch_x, batch_y = cnn.convert_input(lines)
feed_dict = {
cnn.input_x: batch_x,
cnn.labels: batch_y,
cnn.dropout_keep_prob: 1.0,
cnn.training: False
}
loss, accuracy, prediction, y_true = sess.run(
[cnn.loss, cnn.accuracy, cnn.prediction, cnn.labels],
feed_dict)
precision = sk.metrics.precision_score(y_true=y_true,
y_pred=prediction,
average='weighted')
recall = sk.metrics.recall_score(y_true=y_true,
y_pred=prediction,
average='weighted')
f1_score = sk.metrics.f1_score(y_true=y_true,
y_pred=prediction,
average='weighted')
valid_loss += loss
valid_accuracy += accuracy
valid_precision += precision
valid_recall += recall
valid_f1_score += f1_score
i += 1
except tf.errors.OutOfRangeError:
# 遍历完验证集，然后对loss和accuracy求平均值
valid_loss /= i
valid_accuracy /= i
valid_precision /= i
valid_recall /= i
valid_f1_score /= i
t = datetime.datetime.now().strftime('%m-%d %H:%M')
log = '%s: epoch %d, validation loss: %0.6f, accuracy: %0.6f' % (
t, epoch, valid_loss, valid_accuracy)
log = log + '\n' + (
'precision: %0.6f, recall: %0.6f, f1_score: %0.6f' %
(valid_precision, valid_recall, valid_f1_score))
print(log)
log_file.write(log + '\n')
time.sleep(3)
return
print('Start training TextCNN, training mode=' + cnn.train_mode)
sess.run(tf.global_variables_initializer())
# Training loop
for epoch in range(config.epoch_num):
train_init_op, valid_init_op, next_train_element, next_valid_element = cnn.shuffle_datset(
)
sess.run(train_init_op)
while True:
try:
lines = sess.run(next_train_element)
batch_x, batch_y = cnn.convert_input(lines)
train_step(batch_x, batch_y, config.dropout_keep_prob)
except tf.errors.OutOfRangeError:
# 初始化验证集迭代器
sess.run(valid_init_op)
# 计算验证集准确率
valid_step(next_valid_element)
break
train_summary_writer.close()
log_file.close()
# 训练完成后保存参数
path = saver.save(sess, checkpoint_prefix, global_step=global_step)
print("Saved model checkpoint to {}\n".format(path))
