python保存log日志，并利用log日志来画图

　　在神经网络训练中，我们常常需要画出loss function的变化图，log日志里会显示每一次迭代的loss function的值，于是我们先把log日志保存为log.txt文档，再利用这个文档来画图。
　　
1，先来产生一个log日志。

import mxnet as mx
import numpy as np
import os
import logging
logging.getLogger().setLevel(logging.DEBUG)

# Training data
logging.basicConfig(filename = os.path.join(os.getcwd(), 'log.txt'), level = logging.DEBUG) # 把log日志保存为log.txt
train_data = np.random.uniform(0, 1, [100, 2])
train_label = np.array([train_data[i][0] + 2 * train_data[i][1] for i in range(100)])
batch_size = 1
num_epoch=5
# Evaluation Data
eval_data = np.array([[7,2],[6,10],[12,2]])
eval_label = np.array([11,26,16])
train_iter = mx.io.NDArrayIter(train_data,train_label, batch_size, shuffle=True,label_name='lin_reg_label')
eval_iter = mx.io.NDArrayIter(eval_data, eval_label, batch_size, shuffle=False)
X = mx.sym.Variable('data')
Y = mx.sym.Variable('lin_reg_label')
fully_connected_layer  = mx.sym.FullyConnected(data=X, name='fc1', num_hidden = 1)
lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro")
model = mx.mod.Module(
    symbol = lro ,
    data_names=['data'],
    label_names = ['lin_reg_label']  # network structure
)
model.fit(train_iter, eval_iter,
            optimizer_params={'learning_rate':0.005, 'momentum': 0.9},
            num_epoch=20,
            eval_metric='mse',)
model.predict(eval_iter).asnumpy()
metric = mx.metric.MSE()
model.score(eval_iter, metric)

　　上面的代码中logging.basicConfig(filename = os.path.join(os.getcwd(), 'log.txt'), level = logging.DEBUG) # 把log日志保存为log.txt 就是把log日志保存为log.txt文件。
　　
2，log.txt文档如下。

INFO:root:Epoch[0] Train-mse=0.470638
INFO:root:Epoch[0] Time cost=0.047
INFO:root:Epoch[0] Validation-mse=73.642301
INFO:root:Epoch[1] Train-mse=0.082987
INFO:root:Epoch[1] Time cost=0.047
INFO:root:Epoch[1] Validation-mse=41.625072
INFO:root:Epoch[2] Train-mse=0.044817
INFO:root:Epoch[2] Time cost=0.063
INFO:root:Epoch[2] Validation-mse=23.743375
INFO:root:Epoch[3] Train-mse=0.024459
INFO:root:Epoch[3] Time cost=0.063
INFO:root:Epoch[3] Validation-mse=13.511120
INFO:root:Epoch[4] Train-mse=0.013431
INFO:root:Epoch[4] Time cost=0.063
INFO:root:Epoch[4] Validation-mse=7.670062
INFO:root:Epoch[5] Train-mse=0.007408
INFO:root:Epoch[5] Time cost=0.063
INFO:root:Epoch[5] Validation-mse=4.344374
INFO:root:Epoch[6] Train-mse=0.004099
INFO:root:Epoch[6] Time cost=0.063
INFO:root:Epoch[6] Validation-mse=2.455608
INFO:root:Epoch[7] Train-mse=0.002274
INFO:root:Epoch[7] Time cost=0.062
INFO:root:Epoch[7] Validation-mse=1.385449
INFO:root:Epoch[8] Train-mse=0.001263
INFO:root:Epoch[8] Time cost=0.063
INFO:root:Epoch[8] Validation-mse=0.780387
INFO:root:Epoch[9] Train-mse=0.000703
INFO:root:Epoch[9] Time cost=0.063
INFO:root:Epoch[9] Validation-mse=0.438943
INFO:root:Epoch[10] Train-mse=0.000391
INFO:root:Epoch[10] Time cost=0.125
INFO:root:Epoch[10] Validation-mse=0.246581
INFO:root:Epoch[11] Train-mse=0.000218
INFO:root:Epoch[11] Time cost=0.047
INFO:root:Epoch[11] Validation-mse=0.138368
INFO:root:Epoch[12] Train-mse=0.000121
INFO:root:Epoch[12] Time cost=0.047
INFO:root:Epoch[12] Validation-mse=0.077573
INFO:root:Epoch[13] Train-mse=0.000068
INFO:root:Epoch[13] Time cost=0.063
INFO:root:Epoch[13] Validation-mse=0.043454
INFO:root:Epoch[14] Train-mse=0.000038
INFO:root:Epoch[14] Time cost=0.063
INFO:root:Epoch[14] Validation-mse=0.024325
INFO:root:Epoch[15] Train-mse=0.000021
INFO:root:Epoch[15] Time cost=0.063
INFO:root:Epoch[15] Validation-mse=0.013609
INFO:root:Epoch[16] Train-mse=0.000012
INFO:root:Epoch[16] Time cost=0.063
INFO:root:Epoch[16] Validation-mse=0.007610
INFO:root:Epoch[17] Train-mse=0.000007
INFO:root:Epoch[17] Time cost=0.063
INFO:root:Epoch[17] Validation-mse=0.004253
INFO:root:Epoch[18] Train-mse=0.000004
INFO:root:Epoch[18] Time cost=0.063
INFO:root:Epoch[18] Validation-mse=0.002376
INFO:root:Epoch[19] Train-mse=0.000002
INFO:root:Epoch[19] Time cost=0.063
INFO:root:Epoch[19] Validation-mse=0.001327

3，利用log.txt文件来画图。

import re
import matplotlib.pyplot as plt
import numpy as np


def main():
    file = open('log.txt','r')
    list = []
    # search the line including accuracy
    for line in file:
        m=re.search('Train-mse', line)
        if m:
            n=re.search('[0]\.[0-9]+', line) # 正则表达式
            if n is not None:
                list.append(n.group()) # 提取精度数字
    file.close()
    plt.plot(list, 'go')
    plt.plot(list, 'r')
    plt.xlabel('count')
    plt.ylabel('accuracy')
    plt.title('Accuracy')
    plt.show()

if __name__ == '__main__':
    main()