def predict():
#a,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r):
#获取url传来的需要进行预测的数据
result_k = []
result_line = []
data = [[
0, 1.57, 0, 0, 1.58, 0, 0, 99.92, 0, 0, 99.92, 0, 477.79, 348.23, 6.24,
0, 12.4545, -5.2992
],
[
0, 1.23, 0, 0, 1.31, 0, 0, 99.92, 0, 0, 99.92, 0, 475.82,
348.45, 6.19, 0, -0.3895, 0.576
],
[
60.57, 60.49, 60.5, 60.5, 60.49, 60.49, 99.9, 99.86, 99.9,
99.9, 99.86, 99.9, 453.07, 351.83, 6.07, 111.4, -1.254, 1.536
]]
#for l in range(3):
l = random.randint(0, 2)
line = data[l]
#line=[a,b,c,d]
lines = range(3)
lines = [float(i) for i in line]
line_train = []
line_train.append(lines[-2])
line_train.append(lines[-1])
#因为我们全量的数据是31列，所以我们要在数据后面增加一个元素
line_train.append(0)
COLUMNS = ['29', '30', '31']
#将数组转换成dataframe[[1,2,3]]
lines = pd.DataFrame([line_train], columns=COLUMNS)
#predict_result=train.predict(lines)
predict_result = execute.predict(sess, lines, model)
if predict_result[0] == 0:
k = "正常"
key = 0
else:
k = "有漏油"
key = 1
result_k.append(k)
result_line.append(line)
return render_template('predict.html',
result_k=result_k,
result_line=result_line,
key=key)
def cnn_predict():
file = g_config["dataset_path"] + "batches.meta"
patch_bin_file = open(file, "rb")
label_names_dict = pickle.load(patch_bin_file)["label_names"]
global secure_filename
img = Image.open(os.path.join(app.root_path, secure_filename))
r, g, b = img.split()
r_arr = np.array(r)
g_arr = np.array(g)
b_arr = np.array(b)
image = img.reshape([1, 32, 32, 3]) / 255
predicted_class = execute.predict(image)
return flask.render_template(
template_name_or_list="prediction_result.html",
predicted_class=predicted_class)
def cnn_predict():
global secure_filename
# 获取每个图像类别的名称
filename = config['dataset_path'] + 'batches.meta'
fp = open(filename, 'rb')
label_name_dict = pickle.load(fp)['label_names']
# 读取用户上传的图片
img = Image.open(os.path.join(app.root_path, secure_filename))
r, g, b = img.split()
r_arr = np.array(r)
g_arr = np.array(g)
b_arr = np.array(b)
image = np.concatenate((r_arr, g_arr, b_arr)).reshape((1, 32, 32, 3))/255
predicted_class = label_name_dict[execute.predict(image)[0]]
# 将返回的结果用页面渲染出来
return flask.render_template('prediction_result.html', predicted_class=predicted_class)
def reply():
#从请求中获取参数信息
req_msg = request.form['msg']
#将语句使用结巴分词进行分词
req_msg = " ".join(jieba.cut(req_msg))
#调用decode_line对生成回答信息
res_msg = execute.predict(req_msg)
#将unk值的词用微笑符号袋贴
res_msg = res_msg.replace('_UNK', '^_^')
res_msg = res_msg.strip()
# 如果接受到的内容为空，则给出相应的回复
if res_msg == ' ':
res_msg = '请与我聊聊天吧'
return jsonify({'text': res_msg})
def CNN_predict():
global secure_filename
img = Image.open(
os.path.join(app.root_path, 'predict_img/' + secure_filename))
img = img.convert("RGB")
r, g, b = img.split()
r_arr = np.array(r)
g_arr = np.array(g)
b_arr = np.array(b)
img = np.concatenate((r_arr, g_arr, b_arr))
image = img.reshape([1, 32, 32, 3]) / 255
predicted_class = execute.predict(image)
print(predicted_class)
return flask.render_template(
template_name_or_list="prediction_result.html",
predicted_class=predicted_class)
def CNN_predict():
global secure_filename
#使用PIL中 的Image打开文件并获取图像文件中的信息
img = Image.open(
os.path.join(app.root_path, 'predict_img/' + secure_filename))
img = img.resize([32, 32])
#将图像文件的格式转换为RGB
img = img.convert("RGB")
#分别获取r,g,b三元组的像素数据并进行拼接
r, g, b = img.split()
r_arr = np.array(r)
g_arr = np.array(g)
b_arr = np.array(b)
img = np.concatenate((r_arr, g_arr, b_arr))
#将拼接得到的数据按照模型输入维度需要转换为（32，32，3)，并对数据进行归一化
image = img.reshape([1, 32, 32, 3]) / 255
#调用execute中的predict方法进行预测
predicted_class = execute.predict(image)
print(predicted_class)
#将预测结果返回并使用模板进行页面渲染
return flask.render_template(
template_name_or_list="prediction_result.html",
predicted_class=predicted_class)
def CNN_predict():
file = gConfig['dataset_path'] + 'batchs.meta'
patch_bin_file = open(file ,"rb")
label_name_dict = pickle.load(patch_bin_file)['label_names']
global secure_filename
img = Image.open(os.path.join(app.root_path,secure_filename))
r,g,b = img.split()
img=np.concatenate((
np.array(r),
np.array(g),
np.array(b)
))
image = img.reshape([1,32,32,3])/255
predicted_class = execute.predict(image)
return flask.render_template(
template_name_or_list='prediction_result.html',
predicted_class = predicted_class)
def CNN_predict():
#获取图片分类名称存放路径
file = getConfig['dataset_path'] + 'batches.meta'
#读取图片分类名称，并保存到一个字典中
patch_bin_file = open(file, 'rb')
label_names_dict = pickle.load(patch_bin_file)['label_names']
#全局声明一个文件名
global secure_filename
#从本地目录中读取需要分类的图片
img = Image.open(os.path.join(app.root_path, secure_filenname))
#将读取的像素格式转换为RGB,并分别获取RGB通道对应的像素数据
r, g, b = img.split()
#分别将获取的像素数据放入数组中
r_arr = np.array(r)
g_arr = np.array(g)
b_arr = np.array(b)
#将三个数组进行拼接
img = np.concatenate((r_arr, g_arr, b_arr))
#对拼接狗的数据进行维度变换和归一化处理
image = img.reshape([1, 32, 32, 3]) / 255
#调用执行器execute的predict函数对图像数据进行预测
predicted_class = execute.predict(image)
#将返回结果用页面模版渲染出来
return flask.render_template(
template_name_or_list='prediction_result.html',
predicted_class=predicted_class)
while count < 1000:
# 读取数据集
f.readline() # 读取E无效信息
question = f.readline() # 读取问题
question = question[2:] # 去掉问题的前缀
_answer = f.readline() # 读取参考答案
_answer = _answer[2:] # 去前缀
# 分词
question_fenci = ' '.join(jieba.cut(question))
_answer_fenci = ' '.join(jieba.cut(_answer))
# 与机器人聊天
print('--------------------------------')
print('question_fenci: ' + str(question_fenci))
answer = execute.predict(question_fenci)
# 答案分词
answer_fenci = ' '.join(jieba.cut(answer))
print('_answer_fenci: ' + str(_answer_fenci))
print('answer_fenci: ' + str(answer_fenci))
# 计算BLEU
reference.append(_answer_fenci.split())
candidate = (answer_fenci.split())
score1 = sentence_bleu(reference, candidate, weights=(1, 0, 0, 0))
score2 = sentence_bleu(reference, candidate, weights=(0.5, 0.5, 0, 0))
score3 = sentence_bleu(reference, candidate, weights=(0.33, 0.33, 0.33, 0))
score4 = sentence_bleu(reference, candidate, weights=(0.25, 0.25, 0.25, 0.25))
reference.clear()
print('Cumulate 1-gram :%f' % score1)