代码实现 importtorchfromtorchimportnnclassMutiHeadAttention(torch.nn.Module):def__init__(self,hidden_size,num_heads):super(MutiHeadAttention,self).__init__()self.num_heads=num_headsself.head_dim=hidden_size//num_heads## 初始化Q、K、V投影矩阵self.q_linear=nn.Linear(hidden_size,hidden_size...
max_length, tokenizer=tokenizer ) 下面我们用这几个dataset来实例化data loaders。下图分为左,中,右三个部分。其中左侧是原文,中间部分是转换为token id并且统一padding到120长度token_ids。最后侧,是每一条sample对应的label,表示是否是垃圾邮件。 from torch.utils.data import DataLoader num_workers = 0 batch...
import torch from torch.nn import functional as F from torch import nn from pytorch_lightning.core.lightning import LightningModule from torchmetrics.functional import accuracy from torch.utils.data import DataLoader, random_split from torchvision.datasets import MNIST import os from torchvision import da...
softmax(logits, dim=-1) idx_next = torch.multinomial(probs, num_samples=1) idx = torch.cat((idx, idx_next), dim=1) return idx # create instance of the BigramLanguageModel class and assign it to the variable model with default settings model = BigramLanguageModel() # move the model...
torch.nn.init.xavier_normal_(layer.weight)iflayer.biasisnotNone: torch.nn.init.constant_(layer.bias, val=0.0)# Initialization with given tensor.layer.weight = torch.nn.Parameter(tensor) 计算Softmax输出的准确率 score = model(images)
import torch from torch import nn from torch import optim input_dim = 2 hidden_dim = 10 output_dim = 1 class NeuralNetwork(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim): super(NeuralNetwork, self).__init__() self.layer_1 = nn.Linear(input_dim, hidden_dim)...
from tensorflow import keras报错 import tensorflow as torch,背景不知则问,不能则学。早在17年实习时就用深度学习-卷积神经网络(CNN)在gesture、cifar-10样本数据集上做图像分类;在18年司博带着用keras做人脸识别和车牌识别。当时是新人,现在其实在深度学习方面还是
In some cases, when importNetworkFromPyTorch cannot convert a PyTorch layer into a MATLAB layer, the software converts the PyTorch layer into a Deep Learning Toolbox function with dlarray support. PyTorch LayerCorresponding Deep Learning Toolbox LayerAlternative Deep Learning Toolbox Function torch.nn...
map_location=torch.device('cpu'))) model.eval() state = torch.from_numpy(env.reset()) plt.figure(figsize=(10,10)) img = plt.imshow(env.render(mode='rgb_array')) while True: if torch.cuda.is_available(): state = state.cuda() logits, value = model(state) policy = F.softmax(...
output=self.softmax(output)returnoutput, hiddendefinitHidden(self):returntorch.zeros(1, self.hidden_size) 训练 准备工作:定义一些帮助函数随机生成一些语言-名字组合。 importrandom#Random item from a listdefrandomChoice(l):returnl[random.randint(0, len(l) - 1)]#Get a random category and random...