and easy to use input pipelines tensorflow dev summit 2018 скачать в хорошем качестве

and easy to use input pipelines tensorflow dev summit 2018

Download 1M+ code from https://codegive.com/959b8a9 the tensorflow dev summit 2018 introduced several improvements and best practices for building input pipelines in tensorflow. the focus was on using the `tf.data` api, which allows for efficient data loading and preprocessing. this tutorial will cover the fundamentals of `tf.data`, from creating datasets to optimizing performance. what is `tf.data`? the `tf.data` api provides tools to build complex input pipelines from simple, reusable pieces. this makes it easier to handle large datasets, perform complex preprocessing, and streamline the input pipeline for training machine learning models. key concepts 1. **dataset**: the primary building block of the `tf.data` api. it represents a sequence of elements, each consisting of one or more tensors. 2. **transformations**: methods to manipulate datasets (e.g., mapping functions, batching, shuffling). 3. **input pipeline**: a sequence of operations that reads data, preprocesses it, and feeds it to the model. basic example of an input pipeline let's create a simple input pipeline using the `tf.data` api. in this example, we'll assume you have a dataset of images and labels. step 1: import libraries step 2: create a sample dataset for demonstration purposes, we will create a synthetic dataset of random images and labels. step 3: preprocess the dataset you can apply various transformations to the dataset, such as shuffling, batching, and mapping functions. step 4: iterate through the dataset you can easily iterate through the dataset in a training loop. advanced features 1. **loading from disk**: you can load images from disk using `tf.data.dataset.list_files()` and then read them using `tf.io.read_file()` and `tf.image.decode_image()`. 2. **using tf.data api with tf.keras**: you can directly use the dataset with a keras model. conclusion the `tf.data` api is a powerful tool for building input pipelines in tensorflow. it simplifies the process of managing data flow, allows for complex t ... #TensorFlow #DevSummit2018 #python TensorFlow input pipelines Dev Summit 2018 easy to use data processing machine learning TensorFlow tutorials data input TensorFlow datasets performance optimization deep learning scalable training data augmentation TensorFlow ecosystem efficient data loading