ubuntu配置matplotlib使用qt5作為後端
阿新 • • 發佈:2020-07-27
ubuntu 16.04
miniconda裝的python, 3.7.3
sudo apt install pyqt5-dev
sudo apt install python3-pyqt5
pip install pyqt5 --only-binary pyqt5
測試程式碼:morvan.py
#!/usr/bin/env python #coding: utf-8 """ View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/ My Youtube Channel: https://www.youtube.com/user/MorvanZhou Dependencies: torch: 0.4 matplotlib """ import torch import torch.nn.functional as F import matplotlib.pyplot as plt import matplotlib matplotlib.use('QT5Agg') #!!! 指定後端 # torch.manual_seed(1) # reproducible x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1) # x data (tensor), shape=(100, 1) y = x.pow(2) + 0.2*torch.rand(x.size()) # noisy y data (tensor), shape=(100, 1) # torch can only train on Variable, so convert them to Variable # The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors # x, y = Variable(x), Variable(y) # plt.scatter(x.data.numpy(), y.data.numpy()) # plt.show() class Net(torch.nn.Module): def __init__(self, n_feature, n_hidden, n_output): super(Net, self).__init__() self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer self.predict = torch.nn.Linear(n_hidden, n_output) # output layer def forward(self, x): x = F.relu(self.hidden(x)) # activation function for hidden layer x = self.predict(x) # linear output return x net = Net(n_feature=1, n_hidden=10, n_output=1) # define the network print(net) # net architecture optimizer = torch.optim.SGD(net.parameters(), lr=0.2) loss_func = torch.nn.MSELoss() # this is for regression mean squared loss plt.ion() # something about plotting for t in range(200): prediction = net(x) # input x and predict based on x loss = loss_func(prediction, y) # must be (1. nn output, 2. target) optimizer.zero_grad() # clear gradients for next train loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients if t % 5 == 0: # plot and show learning process plt.cla() plt.scatter(x.data.numpy(), y.data.numpy()) plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5) plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color': 'red'}) plt.pause(0.1) plt.ioff() plt.show()