def test():
logging.info('Loading test data...')
start_time = time.time()
x_test, y_test = process_file(test_set_file)
session = tf.Session()
session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess=session, save_path=save_path) # 读取保存的模型
logging.info('Testing...')
loss_test, acc_test = evaluate(session, x_test, y_test)
msg = 'Test Loss: {0:>6.2}, Test Acc: {1:>7.2%}'
logging.info(msg.format(loss_test, acc_test))
batch_size = 128
data_len = len(x_test)
num_batch = int((data_len - 1) / batch_size) + 1
y_test_cls = np.argmax(y_test, 1)
y_pred_cls = np.zeros(shape=len(x_test), dtype=np.int32) # 保存预测结果
for i in range(num_batch): # 逐批次处理
start_id = i * batch_size
end_id = min((i + 1) * batch_size, data_len)
feed_dict = {
model.input_x: x_test[start_id:end_id],
model.keep_prob: 1.0
}
y_pred_cls[start_id:end_id] = session.run(model.y_pred_cls,
feed_dict=feed_dict)
# 评估
logging.info("Precision, Recall and F1-Score...")
logging.info(metrics.classification_report(y_test_cls, y_pred_cls))
# 混淆矩阵
logging.info("Confusion Matrix...")
cm = metrics.confusion_matrix(y_test_cls, y_pred_cls)
logging.info(cm)
time_dif = get_time_dif(start_time)
logging.info("Time usage:", time_dif)
def train():
logging.info('Configuring TensorBoard and Saver...')
# 配置tensor board
tensorboard_dir = 'text_cnn/tmp'
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
tf.summary.scalar('loss', model.loss)
tf.summary.scalar('accuracy', model.acc)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(tensorboard_dir)
#配置Saver
saver = tf.train.Saver()
if not os.path.exists(save_dir):
os.makedirs(save_dir)
logging.info('Loading training and validation data...')
# 载入训练集与验证集
start_time = time.time()
train_set_data = process_file(train_set_file, config.num_classes,
config.seq_length)
x_train, y_train = train_set_data['train_set']
x_val, y_val = train_set_data['validate_set']
del train_set_data
logging.info('Time usage: {}'.format(get_time_dif(start_time, )))
# 创建session
init = tf.global_variables_initializer()
conf = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
session = tf.Session(config=conf)
session.run(init)
writer.add_graph(session.graph)
logging.info('Training and evaluating...')
start_time = time.time()
total_batch = 0
best_acc_val = 0.0
last_improved = 0
require_improvement = 1000
flag = False
wv_model = get_wordvec_model()
for epoch in range(config.num_epochs):
logging.info('Epoch: {}'.format(epoch + 1, ))
batch_train = batch_iter_jit(x_train, y_train, wv_model,
config.batch_size)
# batch_train = batch_iter(x_train, y_train, config.batch_size)
logging.info('batch data fetch success.')
# logging.info(type(batch_train))
for x_batch, y_batch in batch_train:
# logging.info('{}: x_batch length: {}'.format(total_batch, len(x_batch)))
feed_dict = feed_data(x_batch, y_batch, config.dropout_keep_prob)
if total_batch % config.save_per_batch == 0:
# 训练结果写入tensorboard轮数
s = session.run(merged_summary, feed_dict=feed_dict)
writer.add_summary(s, total_batch)
if total_batch % config.print_per_batch == 0:
# 输出训练集和验证集性能轮数
feed_dict[model.keep_prob] = 1.0
loss_train, acc_train = session.run([model.loss, model.acc],
feed_dict=feed_dict)
loss_val, acc_val = evaluate(session,
x_val,
y_val,
wv_model=wv_model)
if acc_val > best_acc_val:
best_acc_val = acc_val
last_improved = total_batch
saver.save(sess=session, save_path=save_path)
improved_str = '*'
else:
improved_str = ''
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>6.8}, Train Acc: {2:>7.8%},' \
'Val Loss: {3:>6.8}, Val Acc: {4:>7.8%}, Time: {5} {6}'
logging.info(
msg.format(total_batch, loss_train, acc_train, loss_val,
acc_val, time_dif, improved_str))
# 运行优化
session.run(model.optim, feed_dict=feed_dict)
total_batch += 1
if total_batch - last_improved > require_improvement:
# 验证集正确率长期不提升，提前结束训练
logging.info(
'No optimization for a long time, auto-stopping...')
flag = True
break
if flag:
break
def train2(restore=False):
logging.info('Configuring TensorBoard and Saver...')
# 配置tensor board
tensorboard_dir = 'text_cnn/tmp'
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
tf.summary.scalar('loss', model.loss)
tf.summary.scalar('accuracy', model.acc)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(tensorboard_dir)
#配置Saver
saver = tf.train.Saver()
if not os.path.exists(save_dir):
os.makedirs(save_dir)
logging.info('Loading training and validation data...')
# 载入训练集与验证集
start_time = time.time()
train_set_data = process_file(train_set_file, config.num_classes,
config.seq_length)
x_train, y_train = train_set_data['train_set']
x_val, y_val = train_set_data['validate_set']
del train_set_data
logging.info('Time usage: {}'.format(get_time_dif(start_time, )))
# 创建session
conf = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
session = tf.Session(config=conf)
if restore and os.path.exists(save_dir + "checkpoint"):
logging.info("Restoring Variables from Checkpoint for cnn model.")
saver.restore(session, tf.train.latest_checkpoint(save_dir))
else:
logging.info('first training cnn model, Initializing Variables')
session.run(tf.global_variables_initializer())
writer.add_graph(session.graph)
logging.info('Training and evaluating...')
start_time = time.time()
best_acc_val = 0.0
flag = False
# wv_model = get_wordvec_model()
for epoch in range(1, config.num_epochs + 1):
logging.info('Epoch: {}'.format(epoch))
batch_train = batch_iter_jit(x_train, y_train, config.batch_size)
# batch_train = batch_iter(x_train, y_train, config.batch_size)
# logging.info('batch data fetch success.')
# logging.info(type(batch_train))
total_batch = 0
for x_batch, y_batch in batch_train:
if x_batch is None:
continue
# logging.info('{}: x_batch length: {}'.format(total_batch, len(x_batch)))
feed_dict = feed_data(x_batch, y_batch, config.dropout_keep_prob)
if total_batch % config.print_per_batch == 0:
loss_train, acc_train = session.run([model.loss, model.acc],
feed_dict=feed_dict)
time_dif = get_time_dif(start_time)
logging.info(
'epoch:{4: >3}, Iter: {0:>6}, Train Loss: {1:>6.8}, Train Acc: {2:>7.8%}, Time: {3}'
.format(total_batch, loss_train, acc_train, time_dif,
epoch))
# 运行优化
session.run(model.optim, feed_dict=feed_dict)
total_batch += 1
# 所有batch结束，用验证集验证
feed_dict[model.keep_prob] = 1.0
loss_train, acc_train = session.run([model.loss, model.acc],
feed_dict=feed_dict)
loss_val, acc_val = evaluate(session, x_val, y_val)
if acc_val > best_acc_val:
best_acc_val = acc_val
last_improved = total_batch
saver.save(sess=session, save_path=save_path)
improved_str = '*'
else:
improved_str = ''
time_dif = get_time_dif(start_time)
msg = 'epoch:{0: >3}, Iter: {1:>6}, Train Loss: {2:>6.8}, Train Acc: {3:>7.8%},' \
'Val Loss: {4:>6.8}, Val Acc: {5:>7.8%}, Time: {6} {7}'
logging.info(
msg.format(epoch, total_batch, loss_train, acc_train, loss_val,
acc_val, time_dif, improved_str))
if epoch % config.save_per_epoch == 0:
s = session.run(merged_summary, feed_dict=feed_dict)
writer.add_summary(s, total_batch)
epoch_name = os.path.join(save_dir, "epoch_{0}".format(epoch))
saver.save(sess=session, save_path=epoch_name)
session.close()
def train(restore=False):
logging.info('Configuring TensorBoard and Saver...')
# 配置tensor board
tensorboard_dir = 'textcnn/tensorboard'
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
tf.summary.scalar('loss', model.loss)
tf.summary.scalar('accuracy', model.acc)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(tensorboard_dir)
#配置Saver
saver = tf.train.Saver()
if not os.path.exists(save_dir):
os.makedirs(save_dir)
logging.info('Loading training and validation data...')
# 载入训练集与验证集
start_time = time.time()
train_set_data = process_file(train_set_file, config.num_classes, config.seq_length)
x_train, y_train = train_set_data['train_set']
x_val, y_val = train_set_data['validate_set']
logging.info('Time usage: {}'.format(get_time_dif(start_time,)))
random_vector_generate(train_set_file)
# 创建session
conf = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
session = tf.Session(config=conf)
if restore and os.path.exists(save_dir+"checkpoint"):
logging.info("Restoring Variables from Checkpoint for cnn model.")
saver.restore(session, tf.train.latest_checkpoint(save_dir))
else:
logging.info('first training cnn model, Initializing Variables')
session.run(tf.global_variables_initializer())
writer.add_graph(session.graph)
logging.info('Training and evaluating...')
start_time = time.time()
total_batch = 0
best_acc_val = 0.0
last_improved = 0
require_improvement = 10000
flag = False
for epoch in range(config.num_epochs):
logging.info('Epoch: {}'.format(epoch + 1,))
batch_train = batch_iter(x_train, y_train, config.batch_size)
for x_batch, y_batch in batch_train:
feed_dict = feed_data(x_batch, y_batch, config.dropout_keep_prob)
if total_batch % config.save_per_batch == 0:
# 训练结果写入tensorboard轮数
s = session.run(merged_summary, feed_dict=feed_dict)
writer.add_summary(s, total_batch)
if total_batch % config.print_per_batch == 0:
# 输出训练集和验证集性能轮数
feed_dict[model.keep_prob] = 1.0
loss_train, acc_train = session.run([model.loss, model.acc], feed_dict=feed_dict)
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>6.4}, Train Acc: {2:>8.4%}, ' \
'Time: {3:>8.4}'
logging.info(msg.format(total_batch, loss_train, acc_train, time_dif))
# 运行优化
session.run(model.optim, feed_dict=feed_dict)
total_batch += 1
# if total_batch - last_improved > require_improvement:
# # 验证集正确率长期不提升，提前结束训练
# logging.info('No optimization for a long time, auto-stopping...')
# flag = True
# break
# 输出训练集和验证集性能轮数
feed_dict[model.keep_prob] = 1.0
loss_train, acc_train = session.run([model.loss, model.acc], feed_dict=feed_dict)
loss_val, acc_val = evaluate(session, x_val, y_val)
if acc_val > best_acc_val:
best_acc_val = acc_val
last_improved = total_batch
saver.save(sess=session, save_path=save_path)
improved_str = '*'
else:
improved_str = ''
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>6.4}, Train Acc: {2:>7.4%},' \
'Val Loss: {3:>6.4}, Val Acc: {4:>7.4%}, Time: {5} {6}'
logging.info(msg.format(total_batch, loss_train, acc_train, loss_val, acc_val,
time_dif, improved_str))
if flag:
break
