网站搜索排名优化软件,如何批量做网站,万网个人网站建设教程,免费的企业网站Building the model layers 生成模型层 文章目录 Building the model layers 生成模型层What is a neural network 什么是神经网络Components of a neural network 神经网络的组成部分Build a neural network 构建神经网络Get a hardware device for training 获取用于训练的硬…Building the model layers 生成模型层 文章目录 Building the model layers 生成模型层What is a neural network 什么是神经网络Components of a neural network 神经网络的组成部分Build a neural network 构建神经网络Get a hardware device for training 获取用于训练的硬件设备Define the Class 定义类Weight and Bias 权重和偏差 Model Layers 模型层nn.Flattennn.Linearnn.ReLUnn.Sequentialnn.Softmax Model Parameters 模型参数知识检查Further Reading 进一步阅读References 参考资料Github What is a neural network 什么是神经网络
神经网络是按层连接的神经元的集合。每个神经元都是一个小的计算单元执行简单的计算来共同解决问题。神经元分为 3 种类型的层输入层、隐藏层和输出层。隐藏层和输出层包含许多神经元。神经网络模仿人脑处理信息的方式。
Components of a neural network 神经网络的组成部分 activation function 激活函数 决定神经元是否应该被激活。神经网络中发生的计算包括应用激活函数。如果神经元激活则意味着输入很重要。有不同种类的激活函数。选择使用哪个激活函数取决于您想要的输出。激活函数的另一个重要作用是为模型添加非线性。 Binary 如果函数结果为正则用于将输出节点设置为 1如果函数结果为零或负则将输出节点设置为 0。 f ( x ) { 0 , if x 0 1 , if x ≥ 0 f(x) {\small \begin{cases} 0, \text{if } x 0\\ 1, \text{if } x\geq 0\\ \end{cases}} f(x){0,1,if x0if x≥0Sigmoid 用于预测输出节点介于 0 和 1 之间的概率。$f(x) {\large \frac{1}{1e^{-x}}} $Tanh 用于预测输出节点是否在 1 到 -1 之间用于分类用例。$f(x) {\large \frac{e^{x} - e{-x}}{e{x} e^{-x}}} $ReLU (rectified linear activation function) 如果函数结果为负则用于将输出节点设置为 0如果结果为正则保持结果值。 f ( x ) { 0 , if x 0 x , if x ≥ 0 f(x) {\small \begin{cases} 0, \text{if } x 0\\ x, \text{if } x\geq 0\\ \end{cases}} f(x){0,x,if x0if x≥0 Weights 权重 影响我们网络的输出与预期输出值的接近程度。当输入进入神经元时它会乘以权重值所得输出要么被观察要么被传递到神经网络中的下一层。一层中所有神经元的权重被组织成一个张量。 Bias 偏差 弥补了激活函数的输出与其预期输出之间的差异。低偏差值表明网络对输出形式做出更多假设而高偏差值对输出形式做出更少假设。
我们可以说具有weights W W W 和bias b b b 的神经网络层的输出 y y y 的计算为输入乘以 weights加上bias的总和。 $x \sum{(weights * inputs) bias} $其中 f ( x ) f(x) f(x) 是激活函数。
Build a neural network 构建神经网络
神经网络由对数据执行操作的层/模块组成。torch.nn命名空间提供了构建您自己的神经网络所需的所有构建块。在PyTorch 中每个模块都是nn.Module 的子类。神经网络本身就是一个模块由其他模块层组成。这种嵌套结构允许轻松构建和管理复杂的架构。
在以下部分中我们将构建一个神经网络来对 FashionMNIST 数据集中的图像进行分类。
%matplotlib inline
import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets, transformsGet a hardware device for training 获取用于训练的硬件设备
我们希望能够在 GPU 等硬件加速器如果可用上训练我们的模型。让我们检查一下torch.cuda 否则我们使用 CPU。
device cuda if torch.cuda.is_available() else cpu
print(Using {} device.format(device))Out:
Using cuda deviceDefine the Class 定义类
我们通过子类化nn.Module来定义我们的神经网络。在__init__中初始化神经网络层。每个nn.Module子类都在forward方法中实现对输入数据的操作。
我们的神经网络由以下部分组成
输入层具有 28x28 或 784 个特征/像素。第一个线性模块采用输入 784 个特征并将其转换为具有 512 个特征的隐藏层。ReLU 激活函数将应用于转换中。第二个线性模块将第一个隐藏层的 512 个特征作为输入并将其转换到具有 512 个特征的下一个隐藏层。ReLU 激活函数将应用于转换中。第三个线性模块将 512 个特征作为来自第二个隐藏层的输入并将这些特征转换到输出层其中 10 是类的数量。ReLU 激活函数将应用于转换中。
class NeuralNetwork(nn.Module):def __init__(self):super(NeuralNetwork, self).__init__()self.flatten nn.Flatten()self.linear_relu_stack nn.Sequential(nn.Linear(28*28, 512),nn.ReLU(),nn.Linear(512, 512),nn.ReLU(),nn.Linear(512, 10),nn.ReLU())def forward(self, x):x self.flatten(x)logits self.linear_relu_stack(x)return logits我们创建NeuralNetwork 的一个实例并将其移动到device并打印其结构。
model NeuralNetwork().to(device)
print(model)Out:
NeuralNetwork((flatten): Flatten(start_dim1, end_dim-1)(linear_relu_stack): Sequential((0): Linear(in_features784, out_features512, biasTrue)(1): ReLU()(2): Linear(in_features512, out_features512, biasTrue)(3): ReLU()(4): Linear(in_features512, out_features10, biasTrue)(5): ReLU())
)为了使用该model我们将输入数据传递给它。这将执行model的forward以及一些background operations。不要直接调用model.forward()在输入上调用model会返回一个二维张量其中 dim0 对应于每个类的 10 个原始predicted values的每个输出dim1 对应于每个输出的各个值。
我们通过将它传递给nn.Softmax模块的实例来获得prediction probabilities。
X torch.rand(1, 28, 28, devicedevice)
logits model(X)
pred_probab nn.Softmax(dim1)(logits)
y_pred pred_probab.argmax(1)
print(fPredicted class: {y_pred})Out:
Predicted class: tensor([7], devicecuda:0)Weight and Bias 权重和偏差
nn.Linear 模块随机初始化每层的权重和偏差并在内部将值存储在张量中。
print(fFirst Linear weights: {model.linear_relu_stack[0].weight} \n)print(fFirst Linear biases: {model.linear_relu_stack[0].bias} \n)Out:
First Linear weights: Parameter containing:
tensor([[ 8.9385e-03, -2.4055e-02, 1.9085e-03, ..., -1.8426e-05,-9.0800e-04, 1.9594e-02],[-7.0768e-03, 2.6314e-02, 2.8988e-02, ..., 2.2543e-02,9.9050e-03, -4.3447e-03],[-2.5320e-02, -3.2440e-02, -3.0216e-02, ..., -3.1892e-02,-2.0309e-03, -2.5925e-02],...,[-6.6404e-03, -1.9659e-03, -3.3045e-02, ..., -5.3951e-03,-1.1355e-02, 1.0398e-04],[ 1.3734e-02, 3.3571e-02, 3.4846e-02, ..., 3.1258e-02,-9.9484e-03, -1.1788e-02],[-1.3908e-02, 1.1488e-02, -6.8923e-03, ..., -9.5730e-03,-6.6496e-03, -4.7810e-03]], requires_gradTrue) First Linear biases: Parameter containing:
tensor([ 5.9986e-03, 1.9926e-02, -9.0487e-03, 9.3418e-03, 3.1350e-02,-3.1133e-02, -1.9971e-02, 9.2257e-03, 2.4641e-02, -3.9794e-03,-1.9599e-02, 1.5554e-02, -1.1251e-02, 2.0161e-02, 1.9584e-02,-2.3056e-02, 6.4135e-03, -1.2719e-02, 2.8192e-02, -1.1354e-02,-2.5184e-02, 1.4313e-02, 1.9746e-02, -2.6794e-02, 4.5221e-03,-1.9318e-02, 2.5716e-02, 2.3134e-03, -3.2787e-02, 2.5133e-02,1.3309e-02, -2.2916e-02, -2.9163e-02, 2.0085e-02, -1.9987e-02,-1.6186e-02, 2.7146e-02, 3.8904e-03, 3.3362e-02, 1.6783e-02,-3.2172e-02, -2.0039e-02, 1.5975e-02, -1.7357e-02, -6.5472e-03,-1.0733e-03, -6.6345e-03, 2.6318e-02, -1.3912e-02, 2.8931e-02,-8.0001e-03, 2.2949e-02, 3.3579e-02, -1.4285e-02, -3.5026e-02,-4.6408e-03, -3.2110e-02, 7.9603e-03, 1.6381e-02, -3.5188e-02,2.5518e-02, 2.2947e-02, 2.8763e-02, 2.4568e-02, 3.1417e-02,-4.2958e-03, 5.4503e-03, -2.6941e-02, -3.1337e-02, 6.5361e-03,1.5351e-02, 2.4380e-02, 3.4527e-02, 1.9956e-02, -1.6002e-02,-2.1571e-02, -3.1452e-02, -2.6187e-02, 2.8742e-02, 8.8401e-04,2.7811e-02, -2.1074e-03, -5.2441e-03, 1.9205e-02, -2.1756e-02,-2.8340e-02, -2.4008e-02, -3.2218e-02, 2.7938e-02, -1.8855e-02,2.6310e-02, 8.5549e-03, 3.2544e-02, -8.7869e-03, -5.4650e-03,-8.5808e-04, -1.9684e-02, -9.2285e-04, 2.6570e-02, 2.7112e-02,1.0834e-02, 2.9951e-02, -2.8885e-02, -8.7398e-03, -3.2123e-02,-3.4103e-02, -1.7104e-02, -3.5013e-02, 2.6816e-02, 1.3221e-02,4.7024e-03, -1.1069e-02, 1.1744e-02, 1.1716e-02, 2.2116e-02,-3.7134e-03, -3.1935e-02, -2.8137e-02, -4.2648e-03, 7.3065e-03,2.7714e-03, -2.0125e-02, -7.4680e-03, -5.7435e-03, -2.3287e-02,-1.8487e-02, -2.0353e-02, 3.4419e-02, 1.6447e-02, -2.6372e-02,3.0840e-02, 2.7868e-02, -2.5893e-02, -1.6408e-02, -3.5142e-02,2.4987e-02, -1.2068e-03, -3.3286e-02, 1.3896e-02, 1.4766e-02,2.7921e-02, -1.9777e-02, 1.6009e-03, -3.0369e-03, 5.8204e-03,1.3330e-02, -1.6057e-03, 3.3774e-02, 8.0411e-03, -1.3426e-02,-3.0065e-02, -3.3407e-02, -1.1686e-02, -1.1754e-03, -3.1514e-02,1.0637e-02, 3.4243e-02, 2.6827e-02, 1.9017e-02, 3.2513e-02,1.4470e-02, -2.0612e-02, -3.4506e-02, -1.3239e-02, -1.1074e-02,-2.1190e-02, 2.0960e-02, 1.1182e-02, -2.2666e-02, 6.2611e-03,-2.8990e-02, 1.9382e-02, 2.3962e-03, -2.0972e-03, -8.4757e-03,-9.1190e-03, -1.4236e-02, -2.2083e-03, -2.3094e-02, -2.9572e-03,-2.9041e-03, 2.0682e-02, -1.7084e-03, -3.3577e-02, 8.6727e-03,-9.0417e-03, -1.5183e-02, 1.6578e-02, 2.5495e-02, -9.8740e-03,3.2653e-03, -2.2072e-02, 1.0324e-02, 1.1515e-02, 2.2550e-02,-2.9260e-02, 7.6638e-03, 1.9953e-02, 2.0006e-02, -2.0214e-02,8.8572e-03, 1.0404e-02, 2.4252e-02, -3.2847e-02, -1.3980e-02,2.4789e-02, -5.2448e-03, 5.9182e-03, -2.0305e-02, 2.7687e-02,-2.7491e-02, 3.4065e-02, -1.5964e-02, -5.7720e-03, -2.2380e-02,-2.6087e-02, 1.7129e-04, 2.5295e-03, -3.2620e-02, -8.9806e-03,-1.7327e-02, -3.1212e-03, -1.8227e-02, 2.5046e-02, 3.3874e-02,-3.4658e-02, -3.3325e-02, 1.5169e-02, 2.9721e-02, -2.1360e-02,1.9001e-02, -3.4234e-02, -2.0162e-03, -3.3659e-02, -1.5272e-02,-3.6956e-03, -8.6415e-03, -2.1750e-02, -3.3776e-02, 3.4642e-02,1.6748e-04, -9.6430e-03, 3.1374e-02, 2.2172e-02, -2.1042e-02,2.7340e-02, 6.1807e-03, 1.2675e-03, -1.6533e-02, -1.1356e-03,2.8314e-02, 7.1925e-03, -2.1810e-02, -4.2207e-03, 5.8930e-03,-3.1270e-02, -2.1335e-02, -1.2622e-02, -2.5292e-02, -2.4345e-03,3.3701e-02, -5.3965e-03, 1.0012e-02, -8.9052e-04, -2.1508e-02,3.4990e-02, -3.1931e-02, 2.1711e-02, 1.7907e-02, 1.1928e-02,-2.4449e-02, 1.3951e-02, -1.2408e-02, -9.4584e-03, 1.6864e-02,-2.8035e-02, 2.9146e-02, -3.4494e-02, -3.4326e-02, 6.5326e-03,3.3425e-02, -2.1809e-02, -2.9216e-02, -6.3335e-03, 1.5225e-03,-2.3894e-02, -1.1101e-02, 9.0631e-03, 2.9225e-02, 5.1517e-03,-1.8896e-02, 2.1768e-02, -3.5104e-02, -2.2003e-02, 8.9227e-03,2.4530e-02, 4.0939e-03, 4.1382e-03, 5.8822e-03, -1.1990e-02,1.1077e-02, -9.5397e-03, -3.5084e-02, -2.9436e-02, -1.1752e-02,-1.3748e-02, 3.5164e-02, -1.6435e-02, -3.4502e-02, 3.3773e-03,-2.9251e-02, -2.1990e-02, 4.2471e-03, -2.3697e-02, 9.6990e-05,-3.2504e-02, -7.1421e-03, 1.7027e-02, 3.3400e-02, 6.4107e-03,1.1713e-03, 2.4070e-02, -1.2695e-02, -8.9952e-04, 2.4428e-02,-2.7448e-02, -3.6027e-03, 1.6652e-02, -1.2338e-03, 1.0408e-02,4.3328e-03, 1.8153e-02, 3.1082e-02, 2.7676e-02, 5.3654e-03,6.1815e-03, -2.0798e-02, -2.4612e-02, -3.3156e-02, 2.5055e-02,2.5179e-02, -1.5044e-02, -2.1547e-02, -2.2172e-02, 2.7281e-02,2.0324e-02, 2.7768e-02, -3.5495e-02, -1.7735e-02, -1.8990e-02,-7.6506e-03, 2.4374e-02, -2.6513e-02, -2.2248e-02, 4.7401e-03,1.5162e-02, 1.1040e-02, -2.7058e-02, -9.3053e-03, -1.1417e-03,1.9759e-02, 8.8142e-03, -1.1458e-02, -3.0437e-02, 2.6083e-03,2.3219e-02, -1.3296e-02, 2.3401e-02, 2.9435e-02, -2.4347e-02,-2.8407e-02, 3.2922e-03, -9.7309e-03, -3.1861e-03, 1.5294e-02,-3.1260e-02, 1.6128e-02, -2.6976e-02, -2.3860e-02, -2.8258e-02,3.3300e-02, 2.1957e-02, 1.8276e-02, 3.3821e-02, 3.2459e-02,-1.4380e-02, 2.8679e-02, -1.8167e-02, 1.4250e-02, -2.6868e-02,4.6922e-03, 3.0262e-02, 3.3328e-02, 1.7418e-03, -1.3915e-03,2.1020e-02, -3.2912e-04, 2.7675e-02, 2.8924e-02, 2.6323e-02,1.4407e-03, 1.7175e-02, -1.7259e-02, -2.4208e-02, 2.5289e-02,3.4845e-02, 8.8181e-03, 1.3848e-02, 2.3637e-02, 2.6063e-02,1.7485e-02, -5.0237e-03, 1.5242e-02, -5.2527e-03, 2.8615e-02,-6.4647e-03, 2.7292e-02, 1.2469e-02, 1.4604e-02, 2.3259e-02,-1.3001e-02, -1.4321e-02, -7.7171e-03, 9.9475e-03, 1.7257e-03,-1.4338e-02, 2.7782e-03, -1.9520e-02, -1.1003e-03, -3.5199e-02,5.0515e-03, 6.2458e-03, 3.1785e-02, 2.2085e-02, -1.8765e-02,-1.9637e-02, 5.6673e-03, 3.9483e-03, 6.8746e-03, -9.1332e-03,3.7987e-03, -1.3767e-02, -1.0537e-02, 2.8263e-02, 3.3773e-02,3.3666e-02, -9.3893e-03, -1.2266e-03, 3.4049e-02, 2.3165e-03,-3.1737e-02, -3.4418e-02, -5.2358e-03, -1.8076e-02, -1.0501e-02,7.2267e-03, -2.5573e-02, 1.2106e-02, 2.1317e-02, 1.4924e-02,7.0579e-03, -1.9364e-02, -6.4564e-03, -2.1039e-02, -1.1712e-02,-1.3358e-02, 2.7151e-02, -1.2927e-03, -5.1539e-03, -2.5093e-02,-1.7757e-02, -2.6099e-02, 1.2471e-02, 1.8767e-02, -1.4756e-02,-2.7813e-02, -1.0629e-02, 2.9636e-02, 7.8347e-03, -4.1875e-03,-5.7266e-03, -2.7923e-02, -2.1416e-02, 3.4688e-02, -1.2472e-02,1.8679e-02, 2.6543e-02, 1.3168e-02, 2.9893e-02, 1.3526e-02,-1.8278e-02, -8.5952e-03, -1.6681e-02, -2.1498e-03, 3.2721e-02,-1.2839e-02, -3.3540e-02, -1.6349e-02, -3.5600e-02, -1.3388e-02,-1.4139e-02, -1.4343e-02, -1.3964e-02, -2.3136e-02, 3.4252e-02,1.4078e-02, 2.8221e-02, 8.8933e-03, -2.3626e-02, 1.8151e-03,2.0952e-02, 2.1661e-02], requires_gradTrue) Model Layers 模型层
让我们分解 FashionMNIST model中的layers。为了说明这一点我们将采用 3 张大小为 28x28 的图像的小批量样本看看当我们将其传递到网络时会发生什么。
input_image torch.rand(3,28,28)
print(input_image.size())Out:
torch.Size([3, 28, 28])nn.Flatten
我们初始化nn.Flatten layer将每个 2D 28x28 图像转换为 784 个像素值的连续数组维持小批量维度在 dim0 时。
flatten nn.Flatten()
flat_image flatten(input_image)
print(flat_image.size())Out:
torch.Size([3, 784])nn.Linear
linear layer是一个使用其存储的权重和偏差对输入应用线性变换的模块。输入层中每个像素的灰度值将连接到隐藏层中的神经元进行计算。用于转换的计算是 ${{weight * input bias}} $。
layer1 nn.Linear(in_features28*28, out_features20)
hidden1 layer1(flat_image)
print(hidden1.size())Out:
torch.Size([3, 20])nn.ReLU
非线性激活是在模型的输入和输出之间创建复杂映射的原因。它们在线性变换后应用以引入非线性帮助神经网络学习各种现象。
在此模型中我们在线性层之间使用nn.ReLU但还有其他激活可以在模型中引入非线性。
ReLU 激活函数获取线性层计算的输出并将负值替换为零。 Linear output: ${ x {weight * input bias}} $。 ReLU: $ f(x) \begin{cases} 0, \text{if } x 0\ x, \text{if } x\geq 0\ \end{cases} $
print(fBefore ReLU: {hidden1}\n\n)
hidden1 nn.ReLU()(hidden1)
print(fAfter ReLU: {hidden1})Out:
Before ReLU: tensor([[ 0.4158, -0.0130, -0.1144, 0.3960, 0.1476, -0.0690, -0.0269, 0.2690,0.1353, 0.1975, 0.4484, 0.0753, 0.4455, 0.5321, -0.1692, 0.4504,0.2476, -0.1787, -0.2754, 0.2462],[ 0.2326, 0.0623, -0.2984, 0.2878, 0.2767, -0.5434, -0.5051, 0.4339,0.0302, 0.1634, 0.5649, -0.0055, 0.2025, 0.4473, -0.2333, 0.6611,0.1883, -0.1250, 0.0820, 0.2778],[ 0.3325, 0.2654, 0.1091, 0.0651, 0.3425, -0.3880, -0.0152, 0.2298,0.3872, 0.0342, 0.8503, 0.0937, 0.1796, 0.5007, -0.1897, 0.4030,0.1189, -0.3237, 0.2048, 0.4343]], grad_fnAddmmBackward0)After ReLU: tensor([[0.4158, 0.0000, 0.0000, 0.3960, 0.1476, 0.0000, 0.0000, 0.2690, 0.1353,0.1975, 0.4484, 0.0753, 0.4455, 0.5321, 0.0000, 0.4504, 0.2476, 0.0000,0.0000, 0.2462],[0.2326, 0.0623, 0.0000, 0.2878, 0.2767, 0.0000, 0.0000, 0.4339, 0.0302,0.1634, 0.5649, 0.0000, 0.2025, 0.4473, 0.0000, 0.6611, 0.1883, 0.0000,0.0820, 0.2778],[0.3325, 0.2654, 0.1091, 0.0651, 0.3425, 0.0000, 0.0000, 0.2298, 0.3872,0.0342, 0.8503, 0.0937, 0.1796, 0.5007, 0.0000, 0.4030, 0.1189, 0.0000,0.2048, 0.4343]], grad_fnReluBackward0)nn.Sequential
nn.Sequential是模块的有序容器。数据按照定义的相同顺序传递通过所有模块。您可以使用顺序容器来组合一个快速网络例如seq_modules.
seq_modules nn.Sequential(flatten,layer1,nn.ReLU(),nn.Linear(20, 10)
)
input_image torch.rand(3,28,28)
logits seq_modules(input_image)nn.Softmax
神经网络的最后一个线性层返回logits [-infty, infty] 中的原始值被传递到 nn.Softmax模块。Softmax激活函数用于计算神经网络输出的概率。它仅用于神经网络的输出层。Logits 缩放为值 [0, 1]表示模型对每个类别的预测概率。dim参数指示维度沿该维度值的总和必须为 1。具有最高概率的节点预测所需的输出。
softmax nn.Softmax(dim1)
pred_probab softmax(logits)Model Parameters 模型参数
神经网络内的许多层都是参数化的。在训练期间优化的相关权重和偏差。子类化nn.Module会自动跟踪模型对象中定义的所有字段并使所有参数都可以使用模型parameters()或named_parameters()方法进行访问。
在此示例中我们迭代每个参数并打印其大小及其值的预览。
print(fModel structure: {model}\n\n)for name, param in model.named_parameters():print(fLayer: {name} | Size: {param.size()} | Values : {param[:2]} \n)Out:
Model structure: NeuralNetwork((flatten): Flatten(start_dim1, end_dim-1)(linear_relu_stack): Sequential((0): Linear(in_features784, out_features512, biasTrue)(1): ReLU()(2): Linear(in_features512, out_features512, biasTrue)(3): ReLU()(4): Linear(in_features512, out_features10, biasTrue))
)Layer: linear_relu_stack.0.weight | Size: torch.Size([512, 784]) | Values : tensor([[ 0.0273, 0.0296, -0.0084, ..., -0.0142, 0.0093, 0.0135],[-0.0188, -0.0354, 0.0187, ..., -0.0106, -0.0001, 0.0115]],devicecuda:0, grad_fnSliceBackward0)Layer: linear_relu_stack.0.bias | Size: torch.Size([512]) | Values : tensor([-0.0155, -0.0327], devicecuda:0, grad_fnSliceBackward0)Layer: linear_relu_stack.2.weight | Size: torch.Size([512, 512]) | Values : tensor([[ 0.0116, 0.0293, -0.0280, ..., 0.0334, -0.0078, 0.0298],[ 0.0095, 0.0038, 0.0009, ..., -0.0365, -0.0011, -0.0221]],devicecuda:0, grad_fnSliceBackward0)Layer: linear_relu_stack.2.bias | Size: torch.Size([512]) | Values : tensor([ 0.0148, -0.0256], devicecuda:0, grad_fnSliceBackward0)Layer: linear_relu_stack.4.weight | Size: torch.Size([10, 512]) | Values : tensor([[-0.0147, -0.0229, 0.0180, ..., -0.0013, 0.0177, 0.0070],[-0.0202, -0.0417, -0.0279, ..., -0.0441, 0.0185, -0.0268]],devicecuda:0, grad_fnSliceBackward0)Layer: linear_relu_stack.4.bias | Size: torch.Size([10]) | Values : tensor([ 0.0070, -0.0411], devicecuda:0, grad_fnSliceBackward0)知识检查
PyTorch 中所有神经网络模块的基类为 torch.nn.Module
Further Reading 进一步阅读
torch.nn API
Build the Neural Network — PyTorch Tutorials 2.2.0cu121 documentation Build the Neural Network — PyTorch Tutorials 2.2.0cu121 documentation
References 参考资料
使用 PyTorch 进行机器学习的简介 - Training | Microsoft Learn
使用 PyTorch 进行机器学习的简介 - Training | Microsoft Learn
Github
storm-ice/PyTorch_Fundamentals
storm-ice/PyTorch_Fundamentals
