Convolutional Variational Autoencoder | TensorFlow Core
Aug 16, 2024 · This notebook demonstrates how to train a Variational Autoencoder (VAE) (1, 2) on the MNIST dataset. A VAE is a probabilistic take on the autoencoder, a model which takes …
Intro to Autoencoders | TensorFlow Core
Aug 16, 2024 · Define a convolutional autoencoder. In this example, you will train a convolutional autoencoder using Conv2D layers in the encoder, and Conv2DTranspose layers in the decoder.
컨볼루셔널 변이형 오토인코더 | TensorFlow Core
이 노트북은 MNIST 데이터세트에서 변이형 오토인코더(VAE, Variational Autoencoder)를 훈련하는 방법을 보여줍니다(1, 2). VAE는 오토인코더의 확률론적 형태로, 높은 차원의 입력 데이터를 더 …
自编码器简介 | TensorFlow Core
autoencoder. compile (optimizer = 'adam', loss = losses. MeanSquaredError ()) 使用 x_train 作为输入和目标来训练模型。encoder 会学习将数据集从 784 个维度压缩到隐空间，而 decoder …
Variational Autoencoders with Tensorflow Probability Layers
Mar 8, 2019 · In this blog post, we demonstrated how to combine deep learning with probabilistic programming: we built a variational autoencoder that used TFP Layers to pass the output of a …
Autoencoder 소개 | TensorFlow Core
이 튜토리얼에서는 3가지 예(기본 사항, 이미지 노이즈 제거 및 이상 감지)를 통해 autoencoder를 소개합니다. autoencoder는 입력을 출력에 복사하도록 훈련된 특수한 유형의 신경망입니다.
Deep Convolutional Generative Adversarial Network - TensorFlow
Aug 16, 2024 · This tutorial demonstrates how to generate images of handwritten digits using a Deep Convolutional Generative Adversarial Network (DCGAN). The code is written using the …
Convolutional Neural Network (CNN) | TensorFlow Core
Aug 16, 2024 · This tutorial demonstrates training a simple Convolutional Neural Network (CNN) to classify CIFAR images. Because this tutorial uses the Keras Sequential API , creating and …
Video classification with a 3D convolutional neural network
Aug 16, 2024 · This tutorial demonstrates training a 3D convolutional neural network (CNN) for video classification using the UCF101 action recognition dataset. A 3D CNN uses a three …
Neural machine translation with a Transformer and Keras
May 31, 2024 · Unlike RNNs (such as seq2seq, 2014) or convolutional neural networks (CNNs) (for example, ByteNet), Transformers are able to capture distant or long-range contexts and …