Visualizing models, data, and training using tensorboard

abstract In order to understand what happened, we printed out some statistics during model training to see whether the training was in progress. However, we can do better: PyTorch is integrated with TensorBoard, which is a tool for visualizing the results of neural network training. This tutorial explains some of its functions using the fa ...

Added by p0pb0b on Fri, 17 Sep 2021 06:26:32 +0300

tensorflow study notes

         First, the first question: what is tensorflow?   It is an open source software library for solving numerical calculation based on data flow graph. Now it is mainly used for in-depth learning. In my understanding, tensorflow can complete the construction of neural network and carry out a ser ...

Added by curb on Wed, 15 Sep 2021 22:10:05 +0300

Second job: multi-layer perceptron

1, Linear neural network (1) Linear regression 1. Linear model The linear model is regarded as a single-layer neural network. 2. Loss function The loss function can quantify the difference between the actual value and the predicted value of the target.   3. Analytical solution     4. Optimization method: small batch grad ...

Added by cyberlew15 on Mon, 13 Sep 2021 04:38:22 +0300

NIPS15 - STN Spatial Transformer Network (including code reproduction) of spatial transformation module in neural network

Original address original text Thesis reading methods Three times thesis method First acquaintance CNN method is brilliant in the field of computer vision, and has replaced the traditional method in many fields. However, the architecture of convolutional neural network lacks spatial invariance. Even if convolution and Max pooling opera ...

Added by hacksurfin on Sun, 12 Sep 2021 23:09:02 +0300

Target detection | common dataset annotation format and conversion code

My blog https://blog.justlovesmile.top/ Target detection is an important research direction in computer vision tasks. It is used to detect specific kinds of visual target instances in digital images. As one of the fundamental problems of computer vision, target detection is the basis and premise of many other computer vision tasks, su ...

Added by stormx on Sat, 11 Sep 2021 22:46:13 +0300

TensorFlow by Google CNN machine learning foundations: EP #5 - classifying real world images

The picture classification structure is convenient for training and testing 1. Use convolution for complex images https://bit.ly/tfw-lab5 #@title Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apac ...

Added by php_guest on Fri, 10 Sep 2021 03:19:16 +0300

Tree Difference and LCA

Tree Difference and LCA You can see this video from station b Very nice and highly recommended LCA What is LCA Least Common Ancester, the full name of the LCA, is the latest common ancestor Ancestor: The point that passes through the path from the root of the tree to the current node (excluding the current node, and because it is a tree, t ...

Added by leon_zilber on Wed, 08 Sep 2021 19:48:57 +0300

pytorch | Dragon Boat Festival learning notes

pytorch learning import torch import numpy as np a=torch.rand(4,3,28,28) #torch.rand(batch_size, channel, row, column) a[0].shape #batch_ shape with size = 0: torch.Size([3, 28, 28]) a[0,0].shape #batch_ shape with size = 0 and channel 0: torch.Size([28, 28]) a[0,0,2,4] #batch_size=0, pixels in Row 2 and column 4 on the 0 ...

Added by tacojohn on Sat, 04 Sep 2021 02:45:12 +0300

100 cases of deep learning - convolutional neural network (perception V3) recognition of sign language | day 13

🔱 Hello, I'm 👉 Classmate K,100 cases of deep learning The series will be updated continuously. Welcome to like 👍, Collection ⭐, follow 👀 This paper will use the concept V3 model to realize sign language recognition, focusing on understanding the structure and construction method of the concept V3 model. 1, Preliminary work My environment ...

Added by NeoSsjin on Thu, 02 Sep 2021 11:11:57 +0300