def do_tes(sess, test_data):
# 训练完之后通过测试集测试模型
start_time = time.time()
batch_train = tcnn_data_helper.batch_iter(test_data[0], test_data[1],
test_data[2],
tcnn_config.batch_size)
all_test_pred = []
for x_batch, x_pseg_batch, y_batch in batch_train:
test_pred = sess.run(model.pred_label,
feed_dict={
model.input_x: x_batch,
model.input_x_pseg: x_pseg_batch,
model.input_y: y_batch,
model.keep_prob: 1.0
})
all_test_pred.extend(test_pred)
test_label = np.argmax(test_data[2], 1)
time_dif = get_time_dif(start_time)
print("test usage:", time_dif)
#要和id所代表的类别标签顺序相同
categories = ['教育', '时尚', '家居', '娱乐', '财经', '体育', '房产', '游戏', '时政', '科技']
# 评估
print("Precision, Recall and F1-Score...")
print(
classification_report(test_label,
all_test_pred,
target_names=categories))
# 混淆矩阵
print("Confusion Matrix...")
cm = confusion_matrix(test_label, all_test_pred)
print(cm)
def train_val_test_model(train_data, val_data, test_data, model, tcnn_config):
# 配置 Tensorboard，重新训练时，请将tensorboard文件夹删除，不然图会覆盖
tensorboard_dir = 'tensorboard/textcnn'
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
#删除原来已存在的tensorboard文件
else:
file_list = os.listdir(tensorboard_dir)
if len(file_list) > 0:
for file in file_list:
os.remove(os.path.join(tensorboard_dir, file))
tf.summary.scalar("loss", model.losses)
tf.summary.scalar("accuracy", model.accuracy)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(tensorboard_dir)
# 配置 Saver，用以保存模型
saver = tf.train.Saver()
if not os.path.exists(model_save_location):
os.makedirs(model_save_location)
print('Training and Testing...')
start_time = time.time()
with tf.Session() as sess:
tf.global_variables_initializer().run()
writer.add_graph(sess.graph)
#批量获得数据
for epoch in range(tcnn_config.num_epochs):
batch_train = tcnn_data_helper.batch_iter(train_data[0],
train_data[1],
train_data[2],
tcnn_config.batch_size)
total_batch = 0
for x_batch, x_pseg_batch, y_batch in batch_train:
total_batch += 1
feed_dict = {
model.input_x: x_batch,
model.input_x_pseg: x_pseg_batch,
model.input_y: y_batch,
model.keep_prob: tcnn_config.keep_prob
}
if total_batch % tcnn_config.save_per_batch == 0:
summary_str = sess.run(merged_summary, feed_dict=feed_dict)
writer.add_summary(summary_str,
total_batch) # 将summary 写入文件
if total_batch % tcnn_config.print_per_batch == 0:
train_accuracy = model.accuracy.eval(feed_dict=feed_dict)
print("Epoch %d:Step %d accuracy is %f" %
(epoch + 1, total_batch, train_accuracy))
sess.run(model.optim, feed_dict=feed_dict)
here2(sess, val_data)
saver.save(sess, save_path)
time_dif = get_time_dif(start_time)
print("train usage:", time_dif)
do_tes(sess, test_data)
def do_evaluate(sess, val_data):
# 没训练完一个批次都用验证集验证模型的性能
start_time = time.time()
batch_train = tcnn_data_helper.batch_iter(val_data[0], val_data[1],
val_data[2],
tcnn_config.batch_size)
all_val_pred = []
for x_batch, x_pseg_batch, y_batch in batch_train:
test_pred = sess.run(model.pred_label,
feed_dict={
model.input_x: x_batch,
model.input_x_pseg: x_pseg_batch,
model.input_y: y_batch,
model.keep_prob: 1.0
})
all_val_pred.extend(test_pred)
val_label = np.argmax(val_data[2], 1)
time_dif = get_time_dif(start_time)
print('do eval usage:', time_dif)
final_precision, final_recall, final_f1_score = cal_mac_prf(
val_label, all_val_pred)
print("mac precision is:", final_precision, "mac recall is:", final_recall,
"mac f1-score is:", final_f1_score)
def train_tes_model(model, transformer_config):
# 配置Tensorboard，重新训练时，请将tensorboard文件夹删除，不然图会覆盖
tensorboard_dir = 'tensorboard/transformer'
if not os.path.exists(tensorboard_dir):
os.makedirs(tensorboard_dir)
#删除原来已存在的tensorboard文件
else:
file_list = os.listdir(tensorboard_dir)
if len(file_list) > 0:
for file in file_list:
os.remove(os.path.join(tensorboard_dir, file))
tf.summary.scalar("loss", model.losses)
tf.summary.scalar("accuracy", model.accuracy)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(tensorboard_dir)
# 配置 Saver，用以保存模型
saver = tf.train.Saver()
if not os.path.exists(model_save_location):
os.makedirs(model_save_location)
#获得训练数据和测试数据
start_time = time.time()
_, sen_index, _, sen_pseg_index, one_hot_label = tcnn_data_helper.process_file(
transformer_config.file_location,
transformer_config.w2v_model_location,
transformer_config.words_location, transformer_config.psegs_location,
False, transformer_config.sentence_length,
transformer_config.vector_size, transformer_config.pseg_size)
X_train, X_test, X_pseg_train, X_pseg_test, y_train, y_test = train_test_split(
sen_index, sen_pseg_index, one_hot_label, test_size=0.1)
time_dif = get_time_dif(start_time)
print("load data usage:", time_dif)
print('Training and Testing...')
start_time = time.time()
with tf.Session() as sess:
tf.global_variables_initializer().run()
writer.add_graph(sess.graph)
#批量获得数据
for epoch in range(transformer_config.num_epochs):
batch_train = tcnn_data_helper.batch_iter(
X_train, X_pseg_train, y_train, transformer_config.batch_size)
total_batch = 0
for x_batch, x_pseg_batch, y_batch in batch_train:
total_batch += 1
feed_dict = {
model.input_x: x_batch,
model.input_x_pseg: x_pseg_batch,
model.input_y: y_batch,
model.keep_prob: transformer_config.keep_prob
}
if total_batch % transformer_config.save_per_batch == 0:
summary_str = sess.run(merged_summary, feed_dict=feed_dict)
writer.add_summary(summary_str,
total_batch) # 将summary 写入文件
if total_batch % transformer_config.print_per_batch == 0:
train_accuracy = model.accuracy.eval(feed_dict=feed_dict)
print("Epoch %d:Step %d accuracy is %f" %
(epoch + 1, total_batch, train_accuracy))
sess.run(model.optim, feed_dict=feed_dict)
saver.save(sess, save_path)
#训练完之后通过测试集测试模型
batch_train = tcnn_data_helper.batch_iter(
X_test, X_pseg_test, y_test, transformer_config.batch_size)
all_test_pred = []
for x_batch, x_pseg_batch, y_batch in batch_train:
test_pred = model.pred_label.eval(
feed_dict={
model.input_x: x_batch,
model.input_x_pseg: x_pseg_batch,
model.input_y: y_batch,
model.keep_prob: 1.0
})
all_test_pred.extend(test_pred)
test_label = np.argmax(y_test, 1)
#要和id所代表的类别标签顺序相同
categories = [
'教育', '时尚', '家居', '娱乐', '财经', '体育', '房产', '游戏', '时政', '科技'
]
# 评估
print("Precision, Recall and F1-Score...")
print(
classification_report(test_label,
all_test_pred,
target_names=categories))
# 混淆矩阵
print("Confusion Matrix...")
cm = confusion_matrix(test_label, all_test_pred)
print(cm)
time_dif = get_time_dif(start_time)
print("train_and_test usage:", time_dif)