previous page) import torchvision from PIL import Image from torch import nn from torch.nn import functional as F from torch.utils import data from torchvision import transforms 目标受众 本书面向学生（本科生或研究生）、 总而言之，我们没有编写唤醒词识别器，而是编写了一个“学习”程序。如果我们用一个巨大的带标签的数 据集，它很可能可以“学习”识别唤醒词。这种“通过用数据集来确定程序行为”的方法可以被看作用数据 编程（programming with data）。比如，我们可以通过向机器学习系统，提供许多猫和狗的图片来设计一个 “猫图检测器”。检测器最终可以学会：如果输入是猫的图片就输出一个非常大的正数，如果输入是狗的图片 接隐藏层，然后是一个具有10个隐藏单元且不带激活函数的全连接 输出层。 import torch from torch import nn from torch.nn import functional as F net = nn.Sequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10)) X = torch.rand(2

follows right after. Following the lead from the previous chapters, the theory is complemented with programming projects to assist readers to implement these techniques from scratch. Our journey of learning 264, 3) 0 _________________________________________________________________ resnet50 (Functional) (None, 9, 9, 2048) 23587712 _________________________________________________________________

............................................................. 55 Keras v Functional API .......................................................................................... customized models as well. We can use sub-classing concept to create our own complex model. Functional API: Functional API is basically used to create complex models. Layer Each Keras layer in the Keras used to concatenate two inputs. It is defined below: keras.layers.concatenate(inputs, axis=-1) Functional interface to the Concatenate layer. Here, axis refers to Concatenation axis. dot It returns

xt: [b, word vec] ▪ ht_1/ht: [num layers, b, h dim] ▪ out = torch.stack([h1, h2,…, ht]) Functional Functional 下一课时 时间序列预测 Thank You.

import torch # 导入 pytorch from torch import nn # 导入 pytorch 的网络层子库 from torch.nn import functional as F # 导入网络层函数子库 from torch import optim # 导入优化器 import torchvision # 导入视觉库 from mse_loss 函数返回每个样本上的误差值，最后取误差的 均值作为当前 Batch 的误差，它是一个标量，代码如下： In [41]: from torch.nn import functional as F # 导入函数库 out = torch.randn(4,10) # 随机模拟网络输出 y = torch.tensor([2,3,2,0]) # 随机构造样本真实标签 在求解神经网络的时，通过需要计算目标值与预测值的差的平方和，再计算样本上的 平均误差。首先计算目标值与预测值的差的平方和，实现如下： In [20]: from torch.nn import functional as F out = torch.randn([4,10]) # 模拟网络预测输出 y = torch.tensor([1,2,2,0]) # 模拟真实标签 预览版202112

NumPy, SciPy, and Cython. Automatic differentiation is done with a tape-based system at both a functional and neural network layer level. This functionality brings a high level of flexibility and speed use `export CUDA_MODULE_LOADING=EAGER` or `unset CUDA_MODULE_LOADING`. Refer to the CUDA C++ Programming Guide for more information about this environment variable. Announcements ‣ NVIDIA Deep Learning

原文链接：pytorch 入门笔记 -03- 神经网络 定义网络 开始定义一个网络： import torch import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self,): super(Net, self).__init__()

神经网络关键组件及相互关系 3. 神经网络 29  PyTorch构建网络工具 torch.nn Module Linear Conv* *norm *Aative *Loss functional nn.Sequnce nn.Modelist forward Model() Loss() torch.autograd. backward Torch.optims

1）torch.nn 包，里面主要包含构建卷积神经网络的各种算子 操作，主要包括卷积操作（Conv2d、Conv1d、Conv3d）激 活函数、序贯模型 (Sequential)、功能函数 (functional)、损 失功能、支持自定义的模型类（Module）等。通过它们就可 以实现大多数的模型结构搭建与生成。 2）torch.utils 包，里面主要包括训练模型的输入数据处理类、 pytorch

264, 264, 3) 0 _________________________________________________________________ resnet50 (Functional) (None, 9, 9, 2048) 23587712 _________________________________________________________________