A problem with training neural networks is in the choice of the number of training epochs to use. Too many epochs can lead to overfitting of the training dataset, whereas too few may result in an underfit model. Early stopping is a method that allows you to specify an arbitrary large number of training epochs and stop training once the model performance stops improving on a hold out validation dataset. In this tutorial, you will discover the Keras API for adding early stopping to overfit deep learning neural network models. How to Stop Training Deep Neural Networks At the Right Time With Using Early Stopping Photo by Ian D. Keating, some rights reserved. Callbacks provide a way to execute code and interact with the training model process automatically. Callbacks can be provided to the fit() function via the "callbacks" argument.
You can learn a lot about neural networks and deep learning models by observing their performance over time during training. Keras is a powerful library in Python that provides a clean interface for creating deep learning models and wraps the more technical TensorFlow and Theano backends. In this post you will discover how you can review and visualize the performance of deep learning models over time during training in Python with Keras. Display Deep Learning Model Training History in Keras Photo by Gordon Robertson, some rights reserved. Keras provides the capability to register callbacks when training a deep learning model.
In the previous article, I discussed building a linear regression model using Tensorflow. In this article, I will try to solve a multiclass classification problem using Tensorflow. I have used the MNIST-digit recognizer dataset here. Please note that even though a Convolutional Neural Network might have worked better for this problem as this is an image recognition problem, but I have used a generic neural network as I wanted to showcase solving a classification problem using Neural Networks. The dataset consists of 784 pixel columns, where each row represents a 28 x 28 image flattened out into a row vector, and a label column, with the image labels given by the digits the image represent, from 0–9.
Model training can be seen as the generation of subsequent versions of a model -- after each batch, the model weights are adjusted, and as a result, a new version of the model is created. Each new version will have varying levels of performance (as evaluated against a validation set). If everything goes well, training and validation loss will decrease with the number of training epochs. However, the best performing version of a model (here abbreviated as best model) is rarely the one obtained at the end of the training process. Take a typical overfitting case -- at first, both training and validation losses decrease as training progresses.
In a previous tutorial, I demonstrated how to create a convolutional neural network (CNN) using TensorFlow to classify the MNIST handwritten digit dataset. TensorFlow is a brilliant tool, with lots of power and flexibility. However, for quick prototyping work it can be a bit verbose. Enter Keras and this Keras tutorial. Keras is a higher level library which operates over either TensorFlow or Theano, and is intended to stream-line the process of building deep learning networks. In fact, what was accomplished in the previous tutorial in TensorFlow in around 42 lines* can be replicated in only 11 lines* in Keras. This Keras tutorial will show you how to do this.