Programming VIP

Realization of convolutional neural network by TensorFlow A course introduction 1.1 knowledge points 1. Introduction of convolutional neural network; 2. TensorFlow practices CNN network; II. Course content 2.1 basic introduction of convolutional neural network Convolution neural network is a neural network model constructed by convolution ...

This article is mainly reproduced in Blog github project address: https://github.com/albu/albumentations Help document - evaluations This paper mainly introduces some common methods of image data enhancement library augmentation Python image processing library – augmentations, which can be used for image data enhancement during net ...

In this article, we will learn the graph feature learning method based on graph neural network. Graph feature learning requires generating a vector as the representation of the graph according to the node attributes, edges and edge attributes (if any). Based on graph representation, we can predict the graph. The graph representation network ba ...

Basic concepts of pytoch Tensor Pytorch Tensor (Tensor) is the most basic operation object of Pytorch. Its English name is Tensor. It represents a multi-dimensional matrix. For example, zero dimension is a point, one dimension is a vector, two dimension is a general matrix, and multi dimension is equivalent to a multi-dimensional array, which ...

1, Stacked RNN from keras.models import Sequential from keras.layers import LSTM,Embedding,Dense # Set super parameters vocabulary = 10000 # There are 10000 words in the dictionary embedding_dim=32 # shape(x)=32, and the dimension of word vector x is 32 word_num = 500 # Each film review has 500 words. If it exceeds 500 words ...

Network optimization exercises 0. Overview Among them, Dropout and Weight Decay are two methods that have been practiced in future experiments (fitting thematic). In this experiment, we will practice some other important network optimization methods. These include: (1) Small batch gradient drop (this section will discuss the setup of ba ...

Dataset hyperlink #Load the propeller, Numpy and related class libraries import paddle from paddle.nn import Linear import paddle.nn.functional as F import numpy as np import os import random Dynamic graph mode (imperative programming paradigm, analogy to Python): analytical execution mode. Users do not need to define a complete network str ...

Welcome to paddedetection. In view of the problems encountered in the process of using paddedetection, we have sorted the high-frequency situations into FAQs (frequently asked questions) Portal: https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/FAQ.md Github address is: https://github.com/PaddlePaddle/PaddleDetec ...

[source code analysis] deep learning distributed training framework horovod (6) - background thread architecture 0x00 summary Horovod is an easy-to-use high-performance distributed training framework released by Uber in 2017, which has been widely used in the industry. This series will lead you to understand Horovod through source code a ...

In the previous two sections, we can realize some binary classification problems through a simple multilayer neural network, but in some cases, our samples may need to be divided into more than one class, so how to find the current samples in so many classification types is the most likely problem we need to solve. A very simple idea is to cal ...