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High-performance image generation using Stable Diffusion in KerasCV
Overview In this guide, we will show how to generate novel images based on a text prompt using the KerasCV implementation of stability.ai 's text-to-image model, Stable Diffusion . Stable Diffusion is...
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Transfer learning for video classification with MoViNet
MoViNets (Mobile Video Networks) provide a family of efficient video classification models, supporting inference on streaming video. In this tutorial, you will use a pre-trained MoViNet model to class...
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Warm-start embedding layer matrix
This tutorial shows how to "warm-start" training using the tf.keras.utils.warmstart_embedding_matrix API for text sentiment classification when changing vocabulary. You will begin by training a simple...
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Video classification with a 3D convolutional neural network
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-dimensional filter to perform co...
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Load video data
This tutorial demonstrates how to load and preprocess AVI video data using the UCF101 human action dataset . Once you have preprocessed the data, it can be used for such tasks as video classification/...
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Neural machine translation with a Transformer and Keras
This tutorial demonstrates how to create and train a sequence-to-sequence Transformer model to translate Portuguese into English . The Transformer was originally proposed in "Attention is all you need...
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Scalable model compression
Overview This notebook shows how to compress a model using TensorFlow Compression . In the example below, we compress the weights of an MNIST classifier to a much smaller size than their floating poin...
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TensorFlow Hub Object Detection Colab
Welcome to the TensorFlow Hub Object Detection Colab! This notebook will take you through the steps of running an "out-of-the-box" object detection model on images. More models This collection contain...
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Learned data compression
Overview This notebook shows how to do lossy data compression using neural networks and TensorFlow Compression . Lossy compression involves making a trade-off between rate , the expected number of bit...
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Image classification with Model Garden
This tutorial fine-tunes a Residual Network (ResNet) from the TensorFlow Model Garden package ( tensorflow-models ) to classify images in the CIFAR dataset. Model Garden contains a collection of state...
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Using DTensors with Keras
Overview In this tutoral, you will learn how to use DTensor with Keras. Through DTensor integration with Keras, you can reuse your existing Keras layers and models to build and train distributed machi...
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Distributed Training with DTensors
Overview DTensor provides a way for you to distribute the training of your model across devices to improve efficiency, reliability and scalability. For more details on DTensor concepts, see The DTenso...
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Basic classification: Classify images of clothing
This guide trains a neural network model to classify images of clothing, like sneakers and shirts. It's okay if you don't understand all the details; this is a fast-paced overview of a complete Tensor...
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Time series forecasting
This tutorial is an introduction to time series forecasting using TensorFlow. It builds a few different styles of models including Convolutional and Recurrent Neural Networks (CNNs and RNNs). This is ...
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Image classification
This tutorial shows how to classify images of flowers using a tf.keras.Sequential model and load data using tf.keras.utils.image_dataset_from_directory . It demonstrates the following concepts: Effici...
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Load a pandas DataFrame
This tutorial provides examples of how to load pandas DataFrames into TensorFlow. You will use a small heart disease dataset provided by the UCI Machine Learning Repository. There are several hundred ...
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Save and load a model using a distribution strategy
Overview This tutorial demonstrates how you can save and load models in a SavedModel format with tf.distribute.Strategy during or after training. There are two kinds of APIs for saving and loading a K...
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Parameter server training with ParameterServerStrategy
Overview Parameter server training is a common data-parallel method to scale up model training on multiple machines. A parameter server training cluster consists of workers and parameter servers . Var...
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Custom training loop with Keras and MultiWorkerMirroredStrategy
Overview This tutorial demonstrates how to perform multi-worker distributed training with a Keras model and with custom training loops using the tf.distribute.Strategy API. The training loop is distri...
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Convolutional Neural Network (CNN)
This tutorial demonstrates training a simple Convolutional Neural Network (CNN) to classify CIFAR images . Because this tutorial uses the Keras Sequential API , creating and training your model will t...
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Distributed training with Keras
Overview The tf.distribute.Strategy API provides an abstraction for distributing your training across multiple processing units. It allows you to carry out distributed training using existing models a...
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Custom training: walkthrough
This tutorial shows you how to train a machine learning model with a custom training loop to categorize penguins by species. In this notebook, you use TensorFlow to accomplish the following: Import a ...
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Distributed Input
The tf.distribute APIs provide an easy way for users to scale their training from a single machine to multiple machines. When scaling their model, users also have to distribute their input across mult...
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Customization basics: tensors and operations
This is an introductory TensorFlow tutorial that shows how to: Import the required package. Create and use tensors. Use GPU acceleration. Build a data pipeline with tf.data.Dataset . Import TensorFlow...
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