Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

支持多分类 #7

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

支持多分类 #7

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
25 changes: 15 additions & 10 deletions ClassificationText/bert/keras_bert_classify_text_cnn.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -38,8 +38,10 @@
from conf.feature_config import config_name, ckpt_name, vocab_file, max_seq_len, layer_indexes, gpu_memory_fraction


#from trains import Task
#task = Task.init(project_name="文本相似度", task_name="平安医疗")
def attention(inputs, single_attention_vector=False):
# attention机制
# attention机制
time_steps = k_keras.int_shape(inputs)[1]
input_dim = k_keras.int_shape(inputs)[2]
x = Permute((2, 1))(inputs)
Expand Down Expand Up @@ -229,7 +231,9 @@ def classify_pair_corpus_webank(bert_model, path_webank):
label = q_2_l[-1]
questions.append([text_preprocess(q_1), text_preprocess(q_2)])
label_int = int(label)
labels.append([0, 1] if label_int == 1 else [1, 0])
label_arr = [0] * args.label
label_arr[label_int] = 1
labels.append(label_arr)

questions = np.array(questions)
labels = np.array(labels)
Expand Down Expand Up @@ -265,9 +269,9 @@ def tet():
labels_pred_np_arg = np.argmax(labels_pred_np, axis=1)
labels_test_np = np.array(labels_test)
labels_test_np_arg = np.argmax(labels_test_np, axis=1)
target_names = ['不相似', '相似']
report_predict = classification_report(labels_test_np_arg, labels_pred_np_arg,
target_names=target_names, digits=9)
#target_names = ['不相似', '相似']
#report_predict = classification_report(labels_test_np_arg, labels_pred_np_arg, target_names=target_names, digits=9)
report_predict = classification_report(labels_test_np_arg, labels_pred_np_arg, digits=9)
print(report_predict)


Expand All @@ -276,20 +280,21 @@ def predict():
bert_model = BertTextCnnModel()
bert_model.load_model()
pred = bert_model.predict(sen_1='jy', sen_2='myz')
print(pred[0][1])
print(pred[0])
while True:
print("sen_1: ")
sen_1 = input()
print("sen_2: ")
sen_2 = input()
pred = bert_model.predict(sen_1=sen_1, sen_2=sen_2)
print(pred[0][1])
lable = np.argmax(pred)
print(lable)


if __name__ == "__main__":
train()
# tet()
# predict()
#train()
#tet()
predict()

# text cnn, real stop
# 100000/100000 [==============================] - 1546s 15ms/step - loss: 0.4168 - acc: 0.8108 - val_loss: 0.4379 - val_acc: 0.8008
Expand Down