vocab_size=feature_encoder.vocabulary_size,
word_embedding_dim=48,
# input_length=None,
input_length=sentence_padding_length,
num_labels=4,
conv_filter_type=[
[100, 2, 'full'],
[100, 4, 'full'],
# [100,6,5,'valid'],
],
ktop=1,
embedding_dropout_rate=0.5,
output_dropout_rate=0.5,
nb_epoch=10,
earlyStoping_patience=5,
)
# dcnn.print_model_descibe()
# 训练模型
# dcnn.model_from_pickle('model/modelA.pkl')
dcnn.fit((feature_encoder.train_padding_index, trian_y),
(map(feature_encoder.transform_sentence, test_X), test_y))
quit()
print(dcnn.predict(feature_encoder.transform_sentence(test_X[0])))
dcnn.accuracy((map(feature_encoder.transform_sentence, test_X), test_y))
print(dcnn.batch_predict(map(feature_encoder.transform_sentence, test_X)))
# 保存模型
# dcnn.save_model('model/modelA.pkl')
# 从保存的pickle中加载模型
# print onehot_cnn.predict(feature_encoder.transform_sentence('你好吗'))
# -------------- region end : 3. 初始化CNN模型并训练 ---------------
# -------------- region start : 4. 预测 -------------
if verbose > 1:
logging.debug('-' * 20)
print '-' * 20
logging.debug('4. 预测')
print '4. 预测'
# -------------- code start : 开始 -------------
y_predict = map(rand_embedding_cnn.predict,test_X_feature)
test_data[u'Y_PRED'] = [index_to_label[item] for item in y_predict]
data_util.save_data(test_data,path=result_file_path)
quit()
rand_embedding_cnn.predict(feature_encoder.transform_sentence('你好吗'))
print index_to_label[rand_embedding_cnn.predict(feature_encoder.transform_sentence('你好吗'))]
y_pred, is_correct, accu,f1 = rand_embedding_cnn.accuracy((test_X_feature, test_y))
logging.debug('F1(macro)为：%f'%(np.average(f1[:-1])))
print 'F1(macro)为：%f'%(np.average(f1[:-1]))
test_data[u'IS_CORRECT'] = is_correct
test_data[u'PREDICT'] = [index_to_label[item] for item in y_pred]
# data_util.save_data(test_data,'tmp.tmp')
# quit()
data_util.save_data(test_data,
path=result_file_path)
# -------------- region start : 生成深度特征编码 -------------
verbose=1,
batch_size=2,
vocab_size=feature_encoder.vocabulary_size,
word_embedding_dim=48,
# input_length=None,
input_length=sentence_padding_length,
num_labels=4,
conv_filter_type=[[100, 2, 'full'],
[100, 4, 'full'],
# [100,6,5,'valid'],
],
ktop=1,
embedding_dropout_rate=0.5,
output_dropout_rate=0.5,
nb_epoch=10,
earlyStoping_patience=5,
)
dcnn.print_model_descibe()
# 训练模型
# dcnn.model_from_pickle('model/modelA.pkl')
dcnn.fit((train_X_features, trian_y),
(test_X_features, test_y))
print(dcnn.predict(feature_encoder.transform_sentence(test_X[0])))
dcnn.accuracy((test_X_features, test_y))
print(dcnn.batch_predict(test_X_features))
# 保存模型
# dcnn.save_model('model/modelA.pkl')
# 从保存的pickle中加载模型
# print onehot_cnn.predict(feature_encoder.transform_sentence('你好吗'))
if verbose > 2:
logging.debug('-' * 20)
print('-' * 20)
# -------------- region end : 3. 初始化CNN模型并训练 ---------------
# -------------- region start : 4. 预测 -------------
if verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
logging.debug('4. 预测')
print('4. 预测')
# -------------- code start : 开始 -------------
print(index_to_label[dcnn_model.predict(feature_encoder.transform_sentence('你好吗'))])
y_pred, is_correct, accu,f1 = dcnn_model.accuracy((map(feature_encoder.transform_sentence, test_X), test_y))
logging.debug('F1(macro)为：%f'%(np.average(f1[:-1])))
print('F1(macro)为：%f' % (np.average(f1[:-1])))
test_data[u'IS_CORRECT'] = is_correct
test_data[u'PREDICT'] = [index_to_label[item] for item in y_pred]
# data_util.save_data(test_data,'tmp.tmp')
# quit()
data_util.save_data(test_data,
path=result_file_path)
# -------------- code start : 结束 -------------
if verbose > 1:
logging.debug('-' * 20)
# -------------- code start : 结束 -------------
if verbose > 2:
logging.debug('-' * 20)
print('-' * 20)
# -------------- region end : 3. 初始化CNN模型并训练 ---------------
# -------------- region start : 4. 预测 -------------
if verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
logging.debug('4. 预测')
print('4. 预测')
# -------------- code start : 开始 -------------
print(index_to_label[dcnn_model.predict(
feature_encoder.transform_sentence('你好吗'))])
y_pred, is_correct, accu, f1 = dcnn_model.accuracy(
(map(feature_encoder.transform_sentence, test_X), test_y))
logging.debug('F1(macro)为：%f' % (np.average(f1[:-1])))
print('F1(macro)为：%f' % (np.average(f1[:-1])))
test_data[u'IS_CORRECT'] = is_correct
test_data[u'PREDICT'] = [index_to_label[item] for item in y_pred]
# data_util.save_data(test_data,'tmp.tmp')
# quit()
data_util.save_data(test_data, path=result_file_path)
# -------------- code start : 结束 -------------
if verbose > 1:
logging.debug('-' * 20)
if verbose > 2:
logging.debug('-' * 20)
print '-' * 20
# -------------- region end : 3. 初始化CNN模型并训练 ---------------
# -------------- region start : 4. 预测 -------------
if verbose > 1:
logging.debug('-' * 20)
print '-' * 20
logging.debug('4. 预测')
print '4. 预测'
# -------------- code start : 开始 -------------
print index_to_label[rand_embedding_cnn.predict(feature_encoder.transform_sentence('你好吗'))]
y_pred, is_correct, accu,f1 = rand_embedding_cnn.accuracy((map(feature_encoder.transform_sentence, test_X), test_y))
logging.debug('F1(macro)为：%f'%(np.average(f1[:-1])))
print 'F1(macro)为：%f'%(np.average(f1[:-1]))
test_data[u'IS_CORRECT'] = is_correct
test_data[u'PREDICT'] = [index_to_label[item] for item in y_pred]
# data_util.save_data(test_data,'tmp.tmp')
# quit()
result_file_path = ''.join(config['result_file_path'])
data_util.save_data(test_data,
path=result_file_path)
# -------------- code start : 结束 -------------
if verbose > 1:
# -------------- region end : 3. 初始化CNN模型并训练 ---------------
# -------------- region start : 4. 预测 -------------
if verbose > 1:
logging.debug('-' * 20)
print '-' * 20
logging.debug('4. 预测')
print '4. 预测'
# -------------- code start : 开始 -------------
y_predict = map(rand_embedding_cnn.predict, test_X_feature)
test_data[u'Y_PRED'] = [index_to_label[item] for item in y_predict]
data_util.save_data(test_data, path=result_file_path)
quit()
rand_embedding_cnn.predict(feature_encoder.transform_sentence('你好吗'))
print index_to_label[rand_embedding_cnn.predict(
feature_encoder.transform_sentence('你好吗'))]
y_pred, is_correct, accu, f1 = rand_embedding_cnn.accuracy(
(test_X_feature, test_y))
logging.debug('F1(macro)为：%f' % (np.average(f1[:-1])))
print 'F1(macro)为：%f' % (np.average(f1[:-1]))
test_data[u'IS_CORRECT'] = is_correct
test_data[u'PREDICT'] = [index_to_label[item] for item in y_pred]
# data_util.save_data(test_data,'tmp.tmp')
# quit()
data_util.save_data(test_data, path=result_file_path)
# -------------- region start : 生成深度特征编码 -------------
