python保存log日志,實(shí)現(xiàn)用log日志畫圖

更新時(shí)間：2019年12月24日 11:42:50 作者：xiaotao_1

今天小編就為大家分享一篇python保存log日志,實(shí)現(xiàn)用log日志來(lái)畫圖，具有很好的參考價(jià)值，希望對(duì)大家有所幫助。一起跟隨小編過(guò)來(lái)看看吧

在神經(jīng)網(wǎng)絡(luò)訓(xùn)練中，我們常常需要畫出loss function的變化圖，log日志里會(huì)顯示每一次迭代的loss function的值，于是我們先把log日志保存為log.txt文檔，再利用這個(gè)文檔來(lái)畫圖。　　

1，先來(lái)產(chǎn)生一個(gè)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日志保存為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)

上面的代碼中l(wèi)ogging.basicConfig(filename = os.path.join(os.getcwd(), 'log.txt'), level = logging.DEBUG) # 把log日志保存為log.txt 就是把log日志保存為log.txt文件。　　

2，log.txt文檔如下。

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文件來(lái)畫圖。

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) # 正則表達(dá)式
      if n is not None:
        list.append(n.group()) # 提取精度數(shù)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()

以上這篇python保存log日志,實(shí)現(xiàn)用log日志來(lái)畫圖就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持腳本之家。

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