简单线性回归 y = 2*x + 1 import numpy as np import torch import torch.nn as nn class LinearRegressionModel(nn.Module): def __init__(self, input_dim, output_
torch.nn.Module.load_state_dict(state_dict,strict=True) 示例: torch.save(model,'save.pt') model.load_state_dict(torch.load('save.pt'))#model.load_state_dict()函数把加载的权重复制到模型的权重中去 3.1 什么是state_dict? 在PyTorch中,一个torch.nn.Module模型中的可学习参数(比如weights和biases...
torch.from_ numpy(ndarry) a.numpy() AI检测代码解析 import torch import numpy as np a = np.zeros([2, 2]) out = torch.from_numpy(a) # out = out.to(torch.device("cuda")) out = out.to(torch.device("cpu")) 1. 2. 3. 4. 5. 6. 7. 8. 2. AutoGrad的概念 torch.autograd.g...
def __init__(self): super(CNN, self).__init__() self.cnn1_weight = nn.Parameter(torch.rand(16, 1, 5, 5)) self.bias1_weight = nn.Parameter((torch.rand(16))) self.cnn2_weight = nn.Parameter(torch.rand(32, 16, 5, 5)) self.bias2_weight = nn.Parameter(torch.rand(32)) ...
state_dict(): 将优化器管理的参数和其状态信息以 dict 形式返回 load_state_dict(state_dict): 加载之前返回的 dict,更新参数和其状态 两个方法可用来实现模型训练中断后继续训练功能 def state_dict(self): r"""Returns the state of the optimizer as a :class:`dict`. It contains two entries: * ...
import numpy as np import torchvision from torchvision import datasets, models, transforms import matplotlib.pyplot as plt import time import os import copy # 定义模型 class TheModelClass(nn.Module): def __init__(self): super(TheModelClass, self).__init__() ...
保存模型总体来说有两种： 第一种：保存训练的模型，之后我们可以继续训练 （1）保存模型 state = { ...
核心操作非常简单,和使用cuda类似,训练前把模型和数据都移动到torch.device("mps")就可以了。 importtorch fromtorchimportnn importtorchvision fromtorchvisionimporttransforms importtorch.nn.functionalasF importos,sys,time importnumpyasnp importpandasaspd ...
fromts.torch_handlerimportTorchHandler class ImageClassifierHandler(TorchHandler): def initialize(self,params):"""初始化模型"""self.model=SimpleCNN()self.model.load_state_dict(torch.load('model.pth',map_location=torch.device('cuda:0')))self.model.eval()def preprocess(self,batch):"""预处理输...
classDataset(object):"""An abstract class representing a Dataset. All other datasets should subclass it. All subclasses should override ``__len__``, that provides the size of the dataset, and ``__getitem__``, supporting integer indexing in range from 0 to len(self) exclusive. """def_...