Preface1. What is pandas?2. Steps for use
1. Introducing Libraries2. Read in datasummary
With the continuous development of in-depth learning, neural networks are becoming more and more popular. As we all know, cnn is very effective in image classification. This paper will show a simple convolution neural n ...
Posted on Mon, 06 Dec 2021 13:56:59 -0500 by naboth_abaho
I'm a programming Xiaobai. Although the registration time is long, I'm not engaged in coding. I began to teach myself Python in order to learn AI. I usually knock the code according to the book, but I don't have a deep understanding. Now I want to study chatbot and find that my coding level needs to be strengthened, so I open this s ...
Posted on Fri, 03 Dec 2021 14:50:14 -0500 by dbakker
4.1 Fine tuning model
4.1.1 what is fine tuning
What if you don't have much training data for a task? We first find a similar model trained by others, take the ready-made trained model of others, replace it with our own data, adjust the parameters, and train again. This is fine tune.
Why fine tune
For the case that the data set itself is ve ...
Definition: change the vector arr dimension again without modifying the vector itself. That is, it does not affect the content under the original address, creates a copy result and outputs it.
import numpy as np
arr = np.arange(10)
# Transform the vector arrr dimension into 2 rows and 5 columns
Posted on Wed, 01 Dec 2021 08:33:17 -0500 by dhvani
1. ResNet and DenseNet
ResNet (Deep Residual Network): By establishing a "short-circuit connection" between the front and back layers, this helps to reverse the propagation of gradients during training, resulting in a deeper CNN network.
DenseNet: uses a dense join mechanism, where all layers are connected to each other, a ...
Posted on Wed, 01 Dec 2021 05:16:07 -0500 by rsasalm
In order to enhance semantics, the traditional object detection model usually only carries out subsequent operations on the last feature map of the deep convolution network, and the down sampling rate (multiple of image reduction) corresponding to this layer is usually large, ...
Posted on Tue, 30 Nov 2021 11:14:55 -0500 by monloi
Method for obtaining picture mean and variance
In domain adaptive change, or when processing data sets, it is often necessary to analyze the mean and variance of images or data sets
By analyzing the mean and variance, we can efficiently obtain the data distribution, especially the large data set
Therefore, here we record and introduce the ac ...
Posted on Tue, 30 Nov 2021 03:44:06 -0500 by abgoosht
Welcome to my official account, reply to 001 Google programming specification. O_o >_< o_O O_o ~_~ o_O this tutorial shares the method of adding layer by layer operators to pytorch MLU on Cambrian equipment. the basic unit of data transfer and storage between operators in pytorch MLU layer by layer mode is tensor. ...
Posted on Wed, 24 Nov 2021 06:57:11 -0500 by Nexus10
recently, in addition to some experiments on large-scale data sets (ImageNet-1k and ImageNet-21k), we also did some ablation studies on small data sets. Among them, pytorch has its own cifar10 and cifar100 data loading, while Tiny ImageNet does not. So simply record the processing of this data set here. ...
Posted on Wed, 24 Nov 2021 00:58:46 -0500 by abhijeet
For the general classification, we import the data into the neural network, which tells us what kind it is. For some necessary cases, we need to know how the neural network makes judgment, and which parameters of the input data affect the judgment of the neural network. Therefore, the paper Learning Deep Features for Discriminat ...
Posted on Tue, 23 Nov 2021 16:39:41 -0500 by jonez