Programmer Think

1. GAN introduction GAN (generative advantageous networks) is a network proposed by Ian J. Goodfellow and others at the 2014 nips conference. They proposed a new framework for estimating generative models using confrontation processing. The framework can train two models at the same time: one is generative model G (Generator generator Gene ...

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 ...

This article is excerpted from the fourth chapter of Keras deep learning: introduction, actual combat and advanced. What is EBImage EBImage is an extension package of R. it provides general functions for reading, writing, processing and analyzing images, which is very easy to use. The EBImage package is installed in Bioconductor thro ...

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 ...

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 work with you to implement tensorflow2 For the X version image classification task, the classification model uses MobileNetV1. The algorithm implemented in this paper has the following characteristics: 1. Th ...

Abstract: This article will explain in detail Keras's implementation of classical deep learning text classification algorithms, including LSTM, BiLSTM, BiLSTM+Attention, CNN and TextCNN. This article is shared from Huawei cloud community< Keras deep learning Chinese text classification ten thousand word summary (CNN, TextCNN, BiLSTM, attenti ...

2021SC@SDUSC Code location: similarity/distances.py at master · tensorflow/similarity · GitHub  @tf.keras.utils.register_keras_serializable(package="Similarity") class CosineDistance(Distance): """Compute pairwise cosine distances between embeddings. The [Cosine Distance](https://en.wikipedia.org/wiki/Cosine_similarity) is an angu ...

Like RNN neurons, LSTM neurons can maintain memory in their pipeline to allow solving sequence and time problems without disappearing gradient problems affecting their performance. Using the ancient poetry data set, the LSTM neural network model is used to train and automatically generate Chinese Tibetan head poetry. import pandas as pd i ...

preface SRGAN network is a network that uses GAN network to realize image super-resolution reconstruction. After training the network. Only the Generator is used to reconstruct low resolution images. The network structure mainly uses generators and discriminators. The training process is not very stable. It is generally used for image re ...

Hello, I'm a graduate student from the ECG group of Hebei University. This article is my understanding and sharing of mnist recognition learning. This paper is mainly used to give a guide to students who want to build a network with keras to identify mnist. Please correct any mistakes I will accept it with an open mind The first is the inst ...