def pred_input():
hyper_parameters = {
'model': {
'label': 17,
'batch_size': 64,
'embed_size': 30,
'filters': [2, 3, 4],
'filters_num': 30,
'channel_size': 1,
'dropout': 0.5,
'decay_step': 100,
'decay_rate': 0.9,
'epochs': 20,
'len_max': 50,
'vocab_size': 20000, # 这里随便填的，会根据代码里修改
'lr': 1e-3,
'l2': 1e-6,
'activate_classify': 'softmax',
'embedding_type': 'bert', # 还可以填'random'、 'bert' or 'word2vec"
'is_training': True,
'model_path': path_model_fast_text_baiduqa_2019, # 地址可以自己设置
'rnn_type': 'GRU',
# type of rnn, select 'LSTM', 'GRU', 'CuDNNGRU', 'CuDNNLSTM', 'Bidirectional-LSTM', 'Bidirectional-GRU'
'rnn_units': 256, # large 650, small is 300
},
'embedding': {
'embedding_type': 'bert',
'corpus_path': path_embedding_bert,
'level_type': 'char',
'embed_size': 30,
'len_max': 50,
'layer_indexes': [12] # range 1 to 12
},
}
pt = PreprocessText()
graph = Graph(hyper_parameters)
graph.load_model()
ra_ed = graph.word_embedding
ques = '我要打王者荣耀'
ques_embed = ra_ed.sentence2idx(ques)
x_val_1 = np.array([ques_embed[0]])
x_val_2 = np.array([ques_embed[1]])
x_val = [x_val_1, x_val_2]
pred = graph.predict(x_val)
pre = pt.prereocess_idx(pred[0])
print(pre)
while True:
print("请输入: ")
ques = input()
ques_embed = ra_ed.sentence2idx(ques)
print(ques_embed)
ques_embed_1 = np.array([ques_embed[0]])
ques_embed_2 = np.array([ques_embed[1]])
pred = graph.predict([ques_embed_1, ques_embed_2])
pre = pt.prereocess_idx(pred[0])
print(pre)
'droupout_spatial': 0.2,
'activation_conv':
'linear', # Shortcut connections with pre-activation
'layer_repeats': 7,
'full_connect_unit': 256,
'path_hyper_parameters': 'path_hyper_parameters.json'
},
'embedding': {
'embedding_type': 'random',
'corpus_path': path_embedding_random_char,
'level_type': 'char',
'embed_size': 300,
'len_max': 50,
'len_max_word': 26
},
}
import time
time_start = time.time()
graph = Graph(hyper_parameters)
ra_ed = graph.word_embedding
pt = PreprocessText()
x_train, y_train = pt.preprocess_baidu_qa_2019_idx(
path_baidu_qa_2019_train, ra_ed)
x_val, y_val = pt.preprocess_baidu_qa_2019_idx(path_baidu_qa_2019_valid,
ra_ed)
print(len(y_train))
graph.fit(x_train, y_train, x_val, y_val)
print("耗时:" + str(time.time() - time_start))
def pred_tet():
# 测试集的准确率
from keras_textclassification.conf.path_config import path_baidu_qa_2019_valid
hyper_parameters = {
'model': {
'label': 17,
'batch_size': 64,
'embed_size': 30,
'filters': [2, 3, 4],
'filters_num': 30,
'channel_size': 1,
'dropout': 0.5,
'decay_step': 100,
'decay_rate': 0.9,
'epochs': 20,
'len_max': 50,
'vocab_size': 20000, # 这里随便填的，会根据代码里修改
'lr': 1e-3,
'l2': 1e-6,
'activate_classify': 'softmax',
'embedding_type': 'bert', # 还可以填'random'、 'bert' or 'word2vec"
'is_training': True,
'model_path': path_model_fast_text_baiduqa_2019, # 地址可以自己设置
'rnn_type': 'GRU',
# type of rnn, select 'LSTM', 'GRU', 'CuDNNGRU', 'CuDNNLSTM', 'Bidirectional-LSTM', 'Bidirectional-GRU'
'rnn_units': 256, # large 650, small is 300
},
'embedding': {
'embedding_type': 'bert',
'corpus_path': path_embedding_bert,
'level_type': 'char',
'embed_size': 30,
'len_max': 50,
'layer_indexes': [12] # range 1 to 12
},
}
pt = PreprocessText()
graph = Graph(hyper_parameters)
graph.load_model()
ra_ed = graph.word_embedding
x_val, y_val = pt.preprocess_baidu_qa_2019_idx(path_baidu_qa_2019_valid,
ra_ed,
rate=1)
x_val = x_val.tolist()
y_val = y_val.tolist()
y_pred = []
count = 0
for x_val_one in x_val:
count = count + 1
print(x_val_one)
ques_embed_1 = np.array([x_val_one[0]])
ques_embed_2 = np.array([x_val_one[1]])
pred = graph.predict([ques_embed_1, ques_embed_2])
print(pred)
pred_top1 = pt.prereocess_pred_id(pred[0])
print(pred_top1)
y_pred.append(pred_top1)
acc = 0
for i in range(len(y_val)):
if y_val[i] == y_pred[i]:
acc += 1
print('true: {} total: {} acc: {}'.format(acc, len(y_val),
acc / len(y_val)))