| Copyright 2018 The TensorFlow Authors.¶ | |
| #@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.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| 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: | |
| Efficiently loading a dataset off disk. | |
| Identifying overfitting and applying techniques to mitigate it, including data augmentation and dropout. | |
| This tutorial follows a basic machine learning workflow: | |
| Examine and understand data | |
| Build an input pipeline | |
| Build the model | |
| Train the model | |
| Test the model | |
| Improve the model and repeat the process | |
| In addition, the notebook demonstrates how to convert a saved model to a TensorFlow Lite model for on-device machine learning on mobile, embedded, and IoT devices. |