We recommend using tf.keras as a high-level API for building neural networks. That said, most TensorFlow APIs are usable with eager execution. import tensorflow as tf tfe = tf.contrib.eager tf.enable_eager_execution() Layers: common sets of useful operations Most of the time when writing code for machine learning models you want to operate at a higher level…
In the previous tutorial we covered the TensorFlow APIs for automatic differentiation, a basic building block for machine learning. In this tutorial we will use the TensorFlow primitives introduced in the prior tutorials to do some simple machine learning. TensorFlow also includes a higher-level neural networks API (tf.keras) which provides useful abstractions to reduce boilerplate.…
In the previous tutorial we introduced Tensors and operations on them. In this tutorial we will cover automatic differentiation, a key technique for optimizing machine learning models. Setup import tensorflow as tf tf.enable_eager_execution() tfe = tf.contrib.eager # Shorthand for some symbols Derivatives of a function TensorFlow provides APIs for automatic differentiation – computing the derivative…
This is an introductory tutorial for using TensorFlow. It will cover: Importing required packages Creating and using Tensors Using GPU acceleration Datasets Import TensorFlow To get started, import the tensorflow module and enable eager execution. Eager execution enables a more interactive frontend to TensorFlow, the details of which we will discuss much later. import tensorflow…
Model progress can be saved during—and after—training. This means a model can resume where it left off and avoid long training times. Saving also means you can share your model and others can recreate your work. When publishing research models and techniques, most machine learning practitioners share: code to create the model, and the trained…
As always, the code in this example will use the tf.keras API, which you can learn more about in the TensorFlow Keras guide. In both of the previous examples—classifying movie reviews, and predicting housing prices—we saw that the accuracy of our model on the validation data would peak after training for a number of epochs,…
In a regression problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a classification problem, where we aim to predict a discrete label (for example, where a picture contains an apple or an orange). This notebook builds a model to predict the median price…
After learning about basic classification at lesson 1: Basic classification. Today, we learn about text classification with Keras. This notebook classifies movie reviews as positive or negative using the text of the review. This is an example of binary—or two-class—classification, an important and widely applicable kind of machine learning problem. We’ll use the IMDB dataset…
The 1st Lesson: Basic Classification
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 TensorFlow program with the details explained as we go. This guide uses tf.keras, a high-level API to build and train models in…
Read More