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read_model_and_test.py
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import tensorflow as tf
from file_utils import read_file_and_to_numpy
# 加载模型文件，对测试数据进行测试
import configparser
import os
import pandas as pd
from data_process import generate_vector,write_vector_to_file
# from file_utils512 import read_file_and_to_numpy_val
# from batch_read256 import read_csv_,TFRecordReader
from offer_data import read_data_return
file = './config/parameter_256_3_5.ini'
# import matplotlib
# matplotlib.use('Agg')
# import matplotlib.pyplot as plt
import numpy as np
import time
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
# 读取配置文件，拿到参数
def read_config(file):
cf = configparser.ConfigParser()
cf.read(file)
learning_rate = cf.getfloat('model','learning_rate')
training_steps = cf.getint('model','train_steps')
batch_size = cf.getint('model','batch_size')
display_step = cf.getint('model','display_step')
test_steps = cf.getint('model','test_steps')
num_input = cf.getint('model','num_input')
num_hidden = cf.getint('model','num_hidden')
num_classes = cf.getint('model','num_classes')
timesteps = cf.getint('model','timesteps')
epoch_num = cf.getint('model','epoch_num')
Is_Vali = cf.getint('model','Is_Vali')
train_file = cf.get('file','train_file')
test_file = cf.get('file','test_file')
val_file = cf.get('file','val_file')
model_file = cf.get('file','model_file')
test_file_path = cf.get('file','data_file_path')
plot_test_step = cf.getint('model','plot_test_steps')
return learning_rate,training_steps,batch_size,display_step,test_steps,num_input,\
num_hidden,num_classes,timesteps,epoch_num,Is_Vali,train_file,test_file,val_file,\
model_file,test_file_path,plot_test_step
def read_model(filename,test_file,num_input,timesteps,batch_size,test_steps,test_file_path):
# filename 为模型文件保存路径
with tf.Session() as sess:
model_file = filename+"model.meta"
model_saver = tf.train.import_meta_graph(model_file)
filename = tf.train.latest_checkpoint(filename)
model_saver.restore(sess,filename)
# 拿到损失函数和准确率指标
loss = tf.get_collection('loss')[0]
accuracy = tf.get_collection('accuracy')[0]
prediction = tf.get_collection('prediction')[0]
tf.summary.scalar('test_loss',loss)
tf.summary.scalar('test_acc',accuracy)
meraged = tf.summary.merge_all()
write = tf.summary.FileWriter('./tensorboard/',sess.graph)
# 加载placeholder
graph = tf.get_default_graph()
X = graph.get_operation_by_name('X').outputs[0]
Y = graph.get_operation_by_name('Y').outputs[0]
# test_data,test_label,test_file_list = TFRecordReader(test_file,0)
test_data,test_label,file_list= read_data_return(False,test_file)
temp_label = test_label
test_label = tf.cast(test_label,tf.int32)
# test_label = tf.one_hot(test_label,3)
test_label = sess.run(test_label)
acc_list = []
loss_list = []
epoch_list = []
count = 0
acc_sum = 0
loss_sum = 0
# test_data = test_data[:51200]
# test_label = test_label[:51200]
# 循环测试所有的csv测试文件
start_time = time.time()
for i in range(len(test_data)):
# file = str(test_file_list[i])
file = file_list[i]
batch_data = test_data[i]
batch_label = test_label[i]
batch_data = np.reshape(batch_data,[1,256,30])
# batch_data = batch_data.reshape(len(batch_data),1,num_input)
print(str(file)+":"+"Final Test Accuracy:",\
sess.run(accuracy,feed_dict={X:batch_data,Y:batch_label}))
# print(file+":"+"Final Test Loss:",\
# sess.run(loss,feed_dict={X:batch_data,Y:batch_label}))
# pre = sess.run(tf.argmax(prediction,1),feed_dict={X:test_data})
# print(sess.run(tf.argmax(prediction,1),feed_dict={X:test_data}))
end_time = time.time()
cost_time_sum = end_time - start_time
print("测试文件的总时间为:"+str(cost_time_sum))
print("平均的测试时间:"+str(cost_time_sum/len(test_data)))
print("计算平均准确率中.................")
for i in range(len(test_data)):
# file = str(test_file_list[i])
batch_data = test_data[i]
batch_label = test_label[i]
batch_data = np.reshape(batch_data,[1,256,30])
print(file+":"+"Final Test Accuracy:",\
sess.run(accuracy,feed_dict={X:batch_data,Y:batch_label}))
print(file+":"+"Final Test Loss:",\
sess.run(loss,feed_dict={X:batch_data,Y:batch_label}))
acc_sum = acc_sum+sess.run(accuracy,feed_dict={X:batch_data,Y:batch_label})
loss_sum = loss_sum+sess.run(loss,feed_dict={X:batch_data,Y:batch_label})
print("平均准确率为："+str(acc_sum/len(test_data)))
print("平均损失为："+str(loss_sum/len(test_data)))
# pre_list = np.array( sess.run(tf.argmax(prediction,1),feed_dict={X:batch_data,Y:batch_label}))
# wirte_csv = open('./pre.csv','a')
# zeros_list = []
# zeros_sum = 0
# one_sum = 0
# two_sum = 0
# temp_count_0 = 0
# temp_count_1 = 0
# temp_count_2 = 0
# for i in pre_list:
# print(i)
# zeros_list.append(i)
# wirte_csv.write(str(i))
# wirte_csv.write('\n')
# for i in range(len(zeros_list)):
# if temp_label[0][i] == 0:
# zeros_sum+=1
# if temp_label[0][i] == 1:
# one_sum+=1
# if temp_label[0][i] == 2:
# two_sum+=1
# for i in range(len(zeros_list)):
# if zeros_list[i] == 0:
# if temp_label[0][i] == 0:
# temp_count_0+=1
# if zeros_list[i] == 1:
# if temp_label[0][i] == 1:
# temp_count_1+=1
# if zeros_list[i]==2:
# if temp_label[0][i] == 2:
# temp_count_2+=1
# print(temp_count_0/zeros_sum)
# print(temp_count_1/one_sum)
# print(temp_count_2/two_sum)
def read_file_produce_vector(path,i):
# 拿到文件夹下所有的csv文件
filename = "test"+str(i)+"csv"
csv_path = path+filename
# 将当前文件转换成目标向量
df = pd.read_csv(csv_path,usecols=["Label","Data"])
data = list(df.get("Data"))
label = list(df.get("Label"))
# 此处参数只要不填1,2,3即可
label_list , pri_list = generate_vector(filename,data,label,5)
temp_file = 'one_test_vector.csv'
# 将当前测试文件写入csv文件
write_vector_to_file(filename,temp_file,label_list,pri_list,0)
label,data = read_file_and_to_numpy(temp_file,0)
return label,data,filename
if __name__ == "__main__":
learning_rate,training_steps,batch_size,display_step,test_steps,\
num_input,num_hidden,num_classes,timesteps,epoch_num,Is_Vali,\
train_file,test_file,val_file,model_file,test_file_path,plot_test_step = read_config(file)
read_model(model_file,test_file,num_input,timesteps,batch_size,test_steps,test_file_path)