代码指定map_location参数:pythonCopy codedevice = torch.device('cuda' if torch.cuda.is_available() else...torch.nn.Linear(10, 1)# 保存模型model = Model()torch.save(model.state_dict(), 'model.pth')# 加载模型device = torch.device...该参数可以接收一个字符串或者一个 torch.device 对象作为...
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)) ...
from ts.torch_handler.base_handler import BaseHandler import torch class MyHandler(BaseHandler): def __init__(self): # 初始化实例,看具体情况重写 # 默认的Handler参数有如下几个,需要用到其它参数自行定义 # self.model = None # self.mapping = None # self.device = None # self.initialized = ...
cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=state_size,state_is_tuple=True) init_state = cell.zero_state(batch_size,tf.float32) rnn_outpus, final_state = tf.contrib.rnn.static_rnn(cell=cell,initial_state=init_state,inputs=rnn_inputs) #使用LSTM的最后状态对函数进行预测 with tf.vari...
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: * ...
out = self.encoder(x) return out net =Net().to(S_DEVICE) optimizer = torch.optim.Adam(net.parameters(), lr=1e-3) loss_fun = nn.CrossEntropyLoss() 运行输出 Net( (encoder): Sequential( (0): Conv2d(1,16, kernel_size=(3,3), stride=(1,1)) ...
device('cuda') torch.set_default_tensor_type('torch.cuda.FloatTensor') dataset = DiamondDataset(num_points=int(1e6), width=20, bound=2.5, std=0.04) from utils import torchutils from matplotlib import pyplot as plt data = torchutils.tensor2numpy(dataset.data) fig, ax = plt.subplots(1, ...
device = None): self._fsm = fsm self.monitor_hub = MonitorHub() self._checkpoint_manager = CheckpointsManager(self._fsm) self.__epoch_num = 100 self._resume_from = None self._on_epoch_end = [] self._best_state_rule = None self._train_config = train_config self._data_processor...
概述简单的记录一下几个激活函数的图像,以便自己以后又忘了回来查阅 激活函数在pytorch中,使用relu,sigmoid和tanh可以直接用torch.relu(x),torch.sigmoid(x)和torch.numpy(x)来调用,而softplus则是用torch.nn.functional来调用,即F.softplus(x) 莫烦教程pytorch基础 ...
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...