x= F.relu(self.hidden(x))#activation function for hidden layerx =self.out(x)returnx net= Net(n_feature=2, n_hidden=10, n_output=2)#define the networkprint(net)#net architectureoptimizer= torch.optim.SGD(net.parameters(), lr=0.02) loss_func= torch.nn.CrossEntropyLoss()#the target ...
plt.plot(PAINT_POINTS[0], 2 * np.power(PAINT_POINTS[0], 2) + bound[1], c='#74BCFF', lw=3, label='upper bound (class 1)') plt.plot(PAINT_POINTS[0], 1 * np.power(PAINT_POINTS[0], 2) + bound[0], c='#FF9359', lw=3, label='lower bound (class 1)') plt.ylim((0...
在这个章节中，我们将引入一种非常强大的神经网络结构，名为卷积神经网络（Convolutional Neural Network，...
net = Net(n_feature=2, n_hidden=10, n_output=2) # define the network print(net) # net architecture optimizer = torch.optim.SGD(net.parameters(), lr=0.02) loss_func = torch.nn.CrossEntropyLoss() # loss of classification plt.ion() # dynamic plot for t in range(100): out = net...
net = Net(n_feature=2, n_hidden=10, n_output=2) # define the network print(net) # net architecture optimizer = torch.optim.SGD(net.parameters(), lr=0.02) loss_func = torch.nn.CrossEntropyLoss() # the target label is NOT an one-hotted ...
运行 复制 Accuracy of the network on the 10000 test images: 54 % 如果您跟随进行,您应该看到模型在这一点上大约有 50%的准确率。这并不是最先进的,但比我们从随机输出中期望的 10%的准确率要好得多。这表明模型确实发生了一些通用学习。 脚本的总运行时间:(1 分钟 54.089 秒) ...
from torch import nn, optim import torch.nn.functional as F # TODO: Define your network architecture here class Classifier(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(784, 256) self.fc2 = nn.Linear(256, 128) self.fc3 = nn.Linear(128, 64) self....
(1) Update D network: maximize log(D(x)) + log(1 - D(G(z))) ### ## Train with all-real batch netD.zero_grad() # Format batch real_cpu = data[0].to(device) b_size = real_cpu.size(0) label = torch.full((b_size,), real_label, dtype=torch.float, device=device) #...
(round(prec,4)))print('Recall: {}'.format(round(rec,4)))fig=plt.figure(figsize=(5,5))fig.set_facecolor('black')# Plot confusion matrixcm=metrics.confusion_matrix(target,prediction)cm_display=cmd(cm,display_labels=['GO','STOP'])cm_display.plot()---Accuracy:0.82Precision:0.771Recall:...
Below is a plot of a ReLU function. As you can see, this ReLU function simply changes negative values to zeros. Thishelps prevent the vanishing gradient problem. If a gradient vanishes, it will not have large impact in tuning the neural network’s weight. ...