python生成的图片保存成为pdf_python将图像保存为pdf及图片
引言:
用python进行机器学习时需要分析数据画图及结果画图需要保存结果图片，为此，本博客稍微总结了一下常用的图片数据保存，如保存图像数据为pdf.
下面是一个用pytorch搭建的LSTM对sin函数进行预测，但是这不是本博客的重点，重点是总结一下图像数据保存，虽然内容小，但是对于像我这样的新手，显然不可或缺。 # -*- coding: utf-8 -*- """ Created on Sat May 4 18:38:06 2019 @author: adminster """ #机器学习或者深度学习，拟合程度可以非常高，但是要注意其泛化能力 from __future__ import print_function import torch import torch.nn as nn import torch.optim as optim import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt class Sequence(nn.Module): #序列模型，名字能理解 def __init__(self): super(Sequence, self).__init__() self.lstm1 = nn.LSTMCell(1, 51) self.lstm2 = nn.LSTMCell(51, 51) self.linear = nn.Linear(51, 1) def forward(self, input, future = 0): outputs = [] h_t = torch.zeros(input.size(0), 51, dtype=torch.double) c_t = torch.zeros(input.size(0), 51, dtype=torch.double) h_t2 = torch.zeros(input.size(0), 51, dtype=torch.double) c_t2 = torch.zeros(input.size(0), 51, dtype=torch.double) for i, input_t in enumerate(input.chunk(input.size(1), dim=1)): print('i,input_t',i,input_t.shape) h_t, c_t = self.lstm1(input_t, (h_t, c_t)) h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))#承接关系 output = self.linear(h_t2)#h是记住的特征信息的状态 print('output',output.shape) outputs += [output] for i in range(future):# if we should predict the future h_t, c_t = self.lstm1(output, (h_t, c_t)) h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2)) output = self.linear(h_t2) outputs += [output] outputs = torch.stack(outputs, 1).squeeze(2) return outputs if __name__ == '__main__': # set random seed to 0#查看之前结果的好方法 np.random.seed(0) torch.manual_seed(0) # load data and make training set # data = torch.load('traindata.pt') input = torch.from_numpy(data[3:, :-1]) #[-0.9613, -0.9738, -0.9840, ..., -0.7353, -0.7683, -0.7993] target = torch.from_numpy(data[3:, 1:]) #[-0.9738, -0.9840, -0.9917, ..., -0.7683, -0.7993, -0.8283] test_input = torch.from_numpy(data[:3, :-1]) test_target = torch.from_numpy(data[:3, 1:]) # build the model seq = Sequence() seq.double()#double()函数来自于object类对象 criterion = nn.MSELoss()# # use LBFGS as optimizer since we can load the whole data to train optimizer = optim.LBFGS(seq.parameters(), lr=0.8) ''' paramters()函数 for name, param in self.named_parameters(): yield param ''' #begin to train for i in range(15): print('STEP: ', i) def closure(): optimizer.zero_grad() out = seq(input)#因为权重是随机的，当然就会是很错误的数据 loss = criterion(out, target) print('loss:', loss.item()) loss.backward() return loss optimizer.step(closure) # begin to predict, no need to track gradient here with torch.no_grad(): future = 1000 pred = seq(test_input, future=future)#用训练好了的网络去预测 loss = criterion(pred[:, :-future], test_target) print('test loss:', loss.item()) y = pred.detach().numpy() # draw the result plt.figure(figsize=(30,10)) plt.title('Predict future values for time sequences\n(Dashlines are predicted values)', fontsize=30) plt.xlabel('x', fontsize=20) plt.ylabel('y', fontsize=20) plt.xticks(fontsize=20) plt.yticks(fontsize=20) def draw(yi, color): plt.plot(np.arange(input.size(1)), yi[:input.size(1)], color, linewidth = 2.0) plt.plot(np.arange(input.size(1), input.size(1) + future), yi[input.size(1):], color + ':', linewidth = 2.0) draw(y[0], 'r') draw(y[1], 'g') draw(y[2], 'b') #加一个文件夹 import time ,os #获取日期 time1=time.strftime('%Y-%m-%d') sv_path='pre_data/'+time1 os.makedirs(sv_path,exist_ok=True) plt.savefig(f'{sv_path}/predict%d.pdf'%i)#保存文件在指定文件夹下很方便 plt.close() #加一个文件夹 import time ,os #获取日期 time1=time.strftime('%Y-%m-%d') sv_path='pre_data/'+time1 os.makedirs(sv_path,exist_ok=True) plt.savefig(f'{sv_path}/predict%d.pdf'%i)#保存文件在指定文件夹下很方便 plt.close()
主要讲解一下这么一小段代码，高手轻拍，主要引入了两个包，time,os分别用来获取当前时间和创建文件夹，保存数据时这两步是必要操作，sv_path='pre_data/'+time1 用于获取年月日，
sv_path='pre_data/'+time1
os.makedirs(sv_path,exist_ok=True)创建多层文件夹目录，用于存放数据
plt.savefig(f'{sv_path}/predict%d.pdf'%i)#保存文件在指定文件夹下很方便
plt.close()调用matplotlib库中已有的函数savefig(path).里面可以保存tiff/png/jpg...等格式