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Based on the previous learning, the training of CNN using convolutional neural network has greatly improved the accuracy. The difference from before is that two layers of convolution neural network are added. The full connection layer that was not understood by the previous learning theory is also understood after writing this code. Moreover, d ...

neuron    the most basic concept in deep learning: neuron, the popular neural network, is almost composed of neurons combined in different ways. A complete neuron is mainly composed of two parts, namely linear function and excitation function.    linear function:      y = wX + b       the formulas o ...

1. General 1.1 introduction of convolutional neural network In the previous blog, we made an implementation of fully connected neural network. Among them, the input layer has 784 (28 * 28 picture) elements, the four hidden layers have 400, 300, 200 and 100 neurons respectively, and the output layer includes 10 categories of handwritten digits ...

Source: PyTorch deep learning quick start tutorial (absolutely easy to understand!) [small mound] 1, Environment configuration Installing anaconda: software that integrates various package s Install anacodna3 five point two Mirror image of Tsinghua UniversityCreate environment In Anaconda Prompt: conda create -n pytorch python=3.6 #Create an ...

0. Preface In the middle, there are many process notes to modify the code, which are not available. They are only used to record ideas. If you want to see the final code, turn to the bottom directly. Run Li Mo 2021 deep learning linear regression code as follows def synthetic_data(w, b, num_examples): """generate y = Xw + b + Noise."" ...

1, Business background This experiment will use the time depth learning technology to reconstruct the image with super-resolution. The designed technology includes convolution neural network, generation countermeasure network, residual network and so on. 2, Development environment This experiment uses "Microsoft Visual Studio", &qu ...

abstract This example extracts part of the data in the plant seedling data set as the data set. The data set has 12 categories. Today, I will work with you to implement tensorflow2 For the X version image classification task, the classification model uses ResNet50. Through this article, you can learn: 1. How to load picture data and process ...

matters needing attention This is the reconstructed deep labv3 + semantic segmentation network, mainly the construction on the file framework and the implementation of code. Compared with the previous semantic segmentation network, it is more complete and clearer. It is recommended to learn this version of DeeplabV3 +. Study Preface Deep ...

Reference link: https://blog.csdn.net/qyhaill/article/details/103043637 Call example: import torch import torch.nn as nn from torch.optim.lr_scheduler import LambdaLR initial_lr = 5 class model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(in_channels=3, out_channels=3, kernel_size=3) def ...

Implementation of Pytorch single machine multi card GPU (principle overview, basic framework and common error reporting)   usually, when large-scale training tasks are required such as pre training, it is difficult for a single card to meet the needs. Therefore, resources such as clusters need to be used to meet the needs of acceleration. ...