在这个章节中,我们将引入一种非常强大的神经网络结构,名为卷积神经网络(Convolutional Neural Network,简称CNN)。CNN 在视觉领域有着卓越的表现,它能够自动地从图像中提取特征,并进行分类。就像一个火眼金睛的时尚专家,CNN 可以辨认出不同的服装款式和类型。无论是酷炫的鞋子、潮流的裤子还是时髦的T恤,CNN 都能一眼...
PyTorch(3):经典网络实现包括CNN、RNN、LSTM等神经网络的介绍以及PyTorch实现。 卷积神经网络(CNN)CNN(Convolutional Neural Network) 卷积 “卷积” 和 “神经网络”。卷积也就是说神经网络不再是对每个像素的输…
import torch from torch import nn import torchvision.datasets as dsets import torchvision.transforms as transforms import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible # Hyper Parameters EPOCH = 1 # train the training data n times,to save time,we just train 1 epoch BATCH_SIZE...
shuffle=False) # Convolutional neural network (two convolutional layers) 2层卷积 classConvNet(nn.Module): def__init__(self, num_classes=10): super(ConvNet,self).__init__() self.layer1=nn.Sequential( nn.Conv2d(1,16, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(16), nn.R...
Fine-tune pretrained Convolutional Neural Networks with PyTorch. Features Gives access to the most popular CNN architectures pretrained on ImageNet. Automatically replaces classifier on top of the network, which allows you to train a network with a dataset that has a different number of classes. ...
# convolutional neural network (2 convolutional layers) class ConvNet(nn.Module): def __init__(self, num_classes=10): super(ConvNet, self).__init__() self.layer1 = nn.Sequential( nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2), nn.BatchNorm2d(16), nn.ReLU(), nn.Max...
These GNN layers can be stacked together to create Graph Neural Network models. GCNConv from Kipf and Welling: Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017) [Example] ChebConv from Defferrard et al.: Convolutional Neural Networks on Graphs with Fast Localized Spectral ...
Superresolution using an efficient sub-pixel convolutional neural network Hogwild training of shared ConvNets across multiple processes on MNIST Training a CartPole to balance in OpenAI Gym with actor-critic Natural Language Inference (SNLI) with GloVe vectors, LSTMs, and torchtext Time sequence predict...
Deeply Convolutional GANs In the first part of the notebook, we implemented an almost direct copy of the original GAN network from Ian Goodfellow. However, this network architecture allows no real spatial reasoning. It is unable to reason about things like “sharp edges” in general because it...
Convolutional Neural Network VGG-16 [TensorFlow 1: GitHub | Nbviewer] https://nbviewer.jupyter.org/github/rasbt/deeplearning-models/blob/master/tensorflow1_ipynb/cnn/cnn-vgg16.ipynb [PyTorch: GitHub | Nbviewer] VGG-16 Gender Classifier Trained on CelebA [PyTorch: GitHub | Nbviewer] ...