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 -------------
if verbose > 1 :
logging.debug('-' * 20)
print('-' * 20)
logging.debug('生成深度特征编码')
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:
logging.debug('-' * 20)
print '-' * 20
word_embedding_dim=config["word_embedding_dim"],
input_length=sentence_padding_length,
num_labels=len(label_to_index),
l1_conv_filter_type=config["l1_conv_filter_type"],
l2_conv_filter_type=config["l2_conv_filter_type"],
full_connected_layer_units=config["full_connected_layer_units"],
embedding_dropout_rate=config["embedding_dropout_rate"],
output_dropout_rate=config["output_dropout_rate"],
nb_epoch=int(config["cnn_nb_epoch"]),
earlyStoping_patience=config["earlyStoping_patience"],
lr=config["lr"],
batch_size=config["batch_size"],
)
rand_embedding_cnn.fit((dev_X, dev_y), (val_X, val_y))
y_pred, is_correct, accu, f1 = rand_embedding_cnn.accuracy((val_X, val_y))
# print 'F1(macro)为：%f' % (np.average(f1))
# train
rand_embedding_cnn.accuracy((train_X_feature, train_y))
else:
# 从保存的pickle中加载模型
rand_embedding_cnn.model_from_pickle(model_file_path)
logging.debug("CNN-static模型已经编译完毕...")
print "CNN-static模型已经编译完毕..."
end_time = timeit.default_timer()
print "end! Running time:%ds!" % (end_time - start_time)
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 -------------
if verbose > 1:
logging.debug('-' * 20)
print('-' * 20)
logging.debug('生成深度特征编码')
print('生成深度特征编码')
# -------------- code start : 开始 -------------