Our entire training set can fit into RAM.fit is making two primary assumptions here: ![]() We then instruct Keras to allow our model to train for 50 epochs with a batch size of 32. Here you can see that we are supplying our training data ( trainX ) and training labels ( trainY ). fit : model.fit(trainX, trainY, batch_size=32, epochs=50) Let’s explore each of these functions one-by-one, looking at an example function call, and then discussing how they are different from each other. Keras provides three functions that can be used to train your own deep learning models:Īll three of these functions can essentially accomplish the same task - but how they go about doing it is very different. When to use Keras’ fit, fit_generator, and train_on_batch functions? Our goal will be to implement a Keras generator capable of training a network on this CSV image data (don’t worry, I’ll show you how to implement such a generator function from scratch).įinally, we’ll train and evaluate our network. images at all! Instead, the entire image dataset is represented by two CSV files, one for training and the second for evaluation. train_on_batch functions.įrom there I’ll show you an example of a “non-standard” image dataset which doesn’t contain any actual PNG, JPEG, etc. In the first part of today’s tutorial we’ll discuss the differences between Keras’. Please note that the code in the tutorial is updated for TensorFlow 2.2+ compatibility, however you may still see in-text references for the legacy. Of course the concept of data augmentation stays the same. Please keep this in mind while reading this legacy tutorial. fit method (which now supports data augmentation). If you are using tensorflow=2.2.0 or tensorflow-gpu=2.2.0 (or higher), then you must use the. fit_generator method which supported data augmentation. Update: This blog post is now TensorFlow 2+ compatible! TensorFlow is in the process of deprecating the. ![]() Looking for the source code to this post? Jump Right To The Downloads Section How to use Keras fit and fit_generator (a hands-on tutorial) fit method can automatically detect if the input data is an array or a generator. fit_generator method will be deprecated in future releases of TensorFlow as the.
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