Win10 pycharm中显示PyTorch tensorboard图
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | import numpy import numpy as np import torch import matplotlib.pyplot as plt import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torchvision import torchvision.transforms as transforms import tensorboard from torch.utils.tensorboard import SummaryWriter # print(tensorboard.__version__) device = torch.device( "cuda:0" if torch.cuda.is_available() else "cpu" ) # Assuming that we are on a CUDA machine, this should print a CUDA device: # print(device) ''' device="cuda" if torch.cuda.is_available() else "cpu" # print(device) ''' torch.set_printoptions(linewidth = 120 ) # Display options for output torch.set_grad_enabled( True ) # Already on by default print (torch.__version__, torchvision.__version__, sep = '\n' ) def get_num_correct(preds, labels): return preds.argmax(dim = 1 ).eq(labels). sum ().item() class Network(nn.Module): def __init__( self ): super ().__init__() self .conv1 = nn.Conv2d(in_channels = 1 , out_channels = 6 , kernel_size = 5 ) self .conv2 = nn.Conv2d(in_channels = 6 , out_channels = 12 , kernel_size = 5 ) self .fc1 = nn.Linear(in_features = 12 * 4 * 4 , out_features = 120 ) self .fc2 = nn.Linear(in_features = 120 , out_features = 60 ) self .out = nn.Linear(in_features = 60 , out_features = 10 ) def forward( self , t): # (1) input layer t = t # (2) hidden conv layer t = self .conv1(t) t = F.relu(t) t = F.max_pool2d(t, kernel_size = 2 , stride = 2 ) # (3) hidden conv layer t = self .conv2(t) t = F.relu(t) t = F.max_pool2d(t, kernel_size = 2 , stride = 2 ) # (4) hidden Linear layer t = t.reshape( - 1 , 12 * 4 * 4 ) # -1表示对行没约束,反正是12*4*4列 t = self .fc1(t) t = F.relu(t) # (5) hidden Linear layer t = self .fc2(t) t = F.relu(t) # (6) output layer t = self .out(t) # t=F.softmax(t,dim=1) #此处不使用softmax函数,因为在训练中我们使用了交叉熵损失函数,而在torch.nn函数类中,已经在其输入中隐式的 # 执行了一个softmax操作,这里我们只返回最后一个线性变换的结果,也即是 return t,也即意味着我们的网络将使用softmax操作进行训练,但在 # 训练完成后,将不需要额外的计算操纵。 return t # get data train_set = torchvision.datasets.FashionMNIST( root = './data/FashionMNIST' , train = True , download = True , transform = transforms.Compose([transforms.ToTensor()]) ) data_loader = torch.utils.data.DataLoader(train_set,batch_size = 100 ,shuffle = True ) # shuffle=True # from collections import Iterable # # print(isinstance(data_loader,Iterable)) #返回True ##################### # starting out with TensorBoard(Network Graph and Images) 下面一段为生成日志文件的代码,直到tb.close() ##################### tb = SummaryWriter() network = Network() images,labels = next ( iter (data_loader)) grid = torchvision.utils.make_grid(images) #网格效用函数 tb.add_image( 'images' ,grid) tb.add_graph(network,images) tb.close() # optimizer = optim.Adam(network.parameters(), lr=0.01) ''' for epoch in range(3): total_loss = 0 total_correct = 0 for batch in data_loader: # get batch images, labels = batch images, labels = images.to(device), labels.to(device) preds = network(images) # pass batch loss = F.cross_entropy(preds, labels) # calculate loss optimizer.zero_grad() loss.backward() # calculate gradients optimizer.step() # update weights using the gradient and the learning rate total_loss += loss.item() total_correct += get_num_correct(preds, labels) print('epoch:', epoch, 'total_correct:', total_correct, 'total_loss:', total_loss) print(total_correct / len(train_set)) ''' |
其中 runs为该代码所在文件夹中位置,日志文件生成后也在这个文件夹里
如下图:
在runs文件夹上点击鼠标右键 有一个open in terminal 点击
打开后如下图所示:
然后再再上图红框右边输入:tensorboard --logdir=日志文件所在的绝对路径
日志文件绝对路径可以直接在runs文件夹右击 有一个copy path 即可
回车后出现一个网址,点击就可以看到tensorboard图:
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