Programmer Think

reference: https://zhuanlan.zhihu.com/p/71328244 catalogue 1. Visual calculation diagram 2. Visualization parameters 3. Remote tensorboard 4. Error reporting It's true that you have to pay it back sooner or later. You've always refused to learn Tensorboard because there are alternatives until you find that you have to use it. The follow ...

Given the error function, learning rate, and even the size of the target variable, the training neural network may become unstable. Large updating of weights during training will lead to numerical overflow or underflow, which is usually called gradients exploding. Gradient explosion is more common in recurrent neural networks, such as LSTM, b ...

A simple classification training is carried out on the jetson nano development board 1. jetson inference Library Download, compilation and simple use Here please refer to: Github: https://github.com/dusty-nv/jetson-inference Gitee: https://gitee.com/jinwei_1/jetson-inference#https://www.youtube.com/watch?v=QXIwdsyK7Rw&list=PL5B692fm6 ...

Project introduction Federated learning algorithm is used to classify emnist data sets. First of all, we should clarify a knowledge point, which is the modifier @ TFF tf_ Calculation and @ TFF federated_ The difference between computation: tensorflow_ The federated (TFF) operation deals with federated values;Each Federation value has a f ...

preface This article is based on KITTI data set training yolov5. Friends can divide the data set completely according to my steps (strictly follow the directory structure I listed, there will be no problem), so as to successfully train yolov5 in KITTI. If you strictly follow my directory structure, you don't need to modify any sentence in the ...

Neural network case Learning objectives Able to use TF Keras get datasetConstruction of multilayer neural networkBe able to complete network training and evaluation The MNIST dataset using handwritten digits is shown in the figure above. The dataset contains 60000 samples for training and 10000 samples for testing. The image is a fixed ...

Visualization helps to explain and understand the internal structure of deep learning model. Through the visualization of the model calculation diagram, we can find out how the neural network is calculated. The visualization of the model mainly includes the following aspects:How many layers does the model haveInput and output shapes of each lay ...

1 tf.data tf.data is tenosrflow2 The module used to create datasets in 0 contains many types of dataset classes, among which dataset and TfRecordDataset are commonly used to encapsulate datasets. Dataset contains a large amount of existing data. TFRecord is a special data format in TF, which can improve the data reading and writing speed. ...

Structure explanation After extracting the github download code, you can see the following directory The data directory stores data sets, which can be replaced with their own data sets as needed.The src directory is all the code Before looking at the code, let's look at the readme file. If there is readme in a code, we must look at it f ...

I introduction: Mentioned before Custom Loss and metrics , the following is an inventory of the loss functions commonly used in Keras. Of course, they are also commonly used in daily production and development for future user-defined loss functions. Although there are many forms of loss function, it can never change without its origin. Loss fu ...