Читайте Pytorch предложение за предложением (обновлено)

y.backward()
y.backward(torch.FloatTensor(x.size())

net.conv2.register_forward_hook(printnorm)
net.conv2.register_backward_hook(printgradnorm)

conv2_param_list = list(self.parameters()) # self: conv2
conv2_param_list.__len__() # 2
conv2_param_list[0].size() 

data_transforms = {
    'train': transforms.Compose([
        transforms.RandomSizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Scale(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = '/Users/Natsume/Desktop/data/hymenoptera_data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'val']}

ax1 = plt.subplot2grid((2, 1), (0, 0), colspan=1, rowspan=1)
ax1.set_title("original close price with mv_avg_volume window %d" %vol_window)
# plot predictions(pct) as color into prices
for start, stop, col in zip(xy[:-1], xy[1:], color_data):
    x, y = zip(start, stop)
    ax1.plot(x, y, color=uniqueish_color3(col))

dataloders = {x: torch.utils.data.DataLoader(
             image_datasets[x], batch_size=4, shuffle=True, num_workers=4)
                                             for x in ['train', 'val']}

def imshow(inp, title=None):
    """Imshow for Tensor."""
    inp = inp.numpy().transpose((1, 2, 0))
    mean = np.array([0.485, 0.456, 0.406])
    std = np.array([0.229, 0.224, 0.225])
    inp = std * inp + mean
    plt.imshow(inp)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)  # pause a bit so that plots are updated

inputs, classes = next(iter(dataloders['train']))
out = torchvision.utils.make_grid(inputs)
imshow(out, title=[class_names[x] for x in classes])

model_ft = models.resnet18(pretrained=True)

model_ft = models.resnet18(pretrained=True)

model_ft.fc = nn.Linear(num_ftrs, 2)

exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

for epoch in range(num_epochs):
    for phase in ['train', 'val']:
        for data in dataloders[phase]:
# -0.26s val

def step(self, epoch=None):
    if epoch is None:
        epoch = self.last_epoch + 1
    self.last_epoch = epoch
    for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
        param_group['lr'] = lr
def get_lr(self):
        return [base_lr * self.gamma ** (self.last_epoch // self.step_size)
                for base_lr in self.base_lrs]
def train(self, mode=True):
        """Sets the module in training mode.
        This has any effect only on modules such as Dropout or BatchNorm.
        """
        self.training = mode
        for module in self.children():
            module.train(mode)
        return self

# 阻止计算参数的gradients
param.requires_grad = False

for j in range(inputs.size()[0]):
    images_so_far += 1
    ax = plt.subplot(num_images//2, 2, images_so_far)
    ax.axis('off')
    ax.set_title('predicted: {}'.format(class_names[preds[j]]))
    imshow(inputs.cpu().data[j])

from torch.utils.data import TensorDataset, DataLoader
train_dataset = TensorDataset(train_features.data, train_targets.data)
train_loader = DataLoader(train_dataset, batch_size=64,
                        shuffle=True, num_workers=1)

让pytorch使用tensorboard
1. torchvision.datasets.MNIST()
1. iter(data_loader): 构建iterator
2. tensor.view == np.reshape
3. argmax.squeeze() 去除（n, m, 1)中的1
4. tensor.float(): 改变type
5. logger: 
	plot curves: loss, acc are scalar; 
	plot histogram: params, grads, np.array;
	plot images: from tensor to (m, h, w)