将两个loss曲线一个accuracy曲线画在一个图上

#!/usr/bin/python
#coding:utf-8
import os
import sys
import numpy as np
import matplotlib.pyplot as plt

caffe_root = '/usr/local/Cellar/caffe/'

sys.path.insert(0, caffe_root + 'python')
import caffe
# MODEL_FILE = caffe_root+'examples/mnist/lenet.prototxt'
# PRETRAINED = caffe_root+'examples/mnist/lenet_iter_10000.caffemodel'
# IMAGE_FILE = caffe_root+'examples/images/test4.bmp'

import matplotlib.pyplot as plt
from numpy import zeros, arange
from math import ceil
import caffe

#caffe.set_device(0)
caffe.set_mode_cpu()
# 使用SGDsolver,即随机梯度下降算法,这个要看你solver文件里,一般不写的话就是sgd
# solver=caffe.AdamSolver('/root/caffe/examples/mnist/lenet_solver_adam.prototxt')
solver = caffe.SGDSolver(caffe_root + 'examples/mnist/lenet_solver_test_show.prototxt')

# 等价于solver文件中的max_iter，即最大解算次数
niter = 10000
# 每隔100次收集一次数据
display = 100

# 每次测试进行100次解算，10000/100
test_iter = 100
# 每500次训练进行一次测试（100次解算），60000/64
test_interval = 500

# 初始化
train_loss = zeros(int(niter * 1.0 / display))
test_loss = zeros(int(niter * 1.0 / test_interval))
test_acc = zeros(int(niter * 1.0 / test_interval))

# iteration 0，不计入
solver.step(1)

# 辅助变量
_train_loss = 0;
_test_loss = 0;
_accuracy = 0
# 进行解算
for it in range(niter):
    # 进行一次解算
    solver.step(1)
    # 每迭代一次，训练batch_size张图片
    _train_loss += solver.net.blobs['loss'].data  # 注意，这里的loss表示你定义网络中loss层使用的名称，原博客中定义该网络使用的是SoftmaxWithLoss

    if it % display == 0:
        # 计算平均train loss
        train_loss[it // display] = _train_loss / display
        _train_loss = 0

    if it % test_interval == 0:
        for test_it in range(test_iter):
            # 进行一次测试
            solver.test_nets[0].forward()
            # 计算test loss
            _test_loss += solver.test_nets[0].blobs['loss'].data  # loss名称和上面的一样
            # 计算test accuracy
            _accuracy += solver.test_nets[0].blobs['accuracy'].data  # 这里和上面一样需要注意一下

        # 计算平均test loss
        test_loss[it / test_interval] = _test_loss / test_iter
        # 计算平均test accuracy
        test_acc[it / test_interval] = _accuracy / test_iter
        _test_loss = 0
        _accuracy = 0

    # 绘制train loss、test loss和accuracy曲线
print '\nplot the train loss and test accuracy\n'
_, ax1 = plt.subplots()
ax2 = ax1.twinx()

# train loss -> 绿色
ax1.plot(display * arange(len(train_loss)), train_loss, 'g')
# test loss -> 黄色
ax1.plot(test_interval * arange(len(test_loss)), test_loss, 'y')
# test accuracy -> 红色
ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')

ax1.set_xlabel('iteration')
ax1.set_ylabel('loss')
ax2.set_ylabel('accuracy')
plt.show()

#coding:utf-8

添加后支持中文字符；

caffe_root = '/usr/local/Cellar/caffe/'

配置自己的caffe路径；

solver = caffe.SGDSolver(caffe_root + 'examples/mnist/lenet_solver_test_show.prototxt')

配置自己的solver文件路径；（注：其中的net和snapshot_prefix建议配置绝对路径）

net: "/usr/local/Cellar/caffe/examples/mnist/lenet_train_test.prototxt"

snapshot_prefix: "/Users/taily/pycharmproj/"

【Caffe】caffe可视化训练过程实操

训练完成后显示训练过程的loss曲线和accuracy曲线：

【Caffe】caffe可视化训练过程实操