Be sure to refer to my Keras tutorial for additional reading.
X Show Full Example ShowingTo help you gain hands-on experience, Ive included a full example showing you how to implement a Keras data generator from scratch.If you are using tensorflow2.2.0 or tensorflow-gpu2.2.0 (or higher), then you must use the.fit method (which now supports data augmentation).![]() Once Keras hits this step count it knows that its a new epoch. This data could be raw images on disk or data that has been modified or augmented in some manner. Instead, a custom Keras.fitgenerator function is likely all you need it. In this blog post well write a custom Keras generator to parse the CSV data and yield batches of images to the.fitgenerator function. X Show Generator From KerasThe generator engine is the ImageDataGenerator from Keras coupled with our custom csvimagegenerator. The generator will burn the CSV fuel to create batches of images for training. If you have virtualenvwrapper installed you can create an environment with mkvirtualenv and activate your environment with the workon command. Make sure you use the Downloads section of todays post grab the source code and Flowers-17 CSV image dataset. Since well be saving our training plot to disk, Line 3 sets matplotlib s backend appropriately. This function is responsible for reading our CSV data file and loading images into memory. It yields batches of data to our Keras.fitgenerator function. Again, I generated the text strings from the Flowers-17 dataset. Additionally, I know this isnt the most efficient way to store an image, but it is great for the purposes of this example. Ill be covering how to do this process later in the tutorial. If you arent familiar with the yield keyword, it is used for Python Generator functions as a convenient shortcut in place of building an iterator class with less memory consumption. Notice that labels is a set which only allows unique entries. Our image data augmentation object will randomly rotate, flip, shear, etc. Were using a Stochastic Gradient Descent optimizer with a hardcoded initial learning rate of 1e-2. Categorical crossentropy is used since we have more than 2 classes (binary crossentropy would be used otherwise).
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