{"version":"1.0","type":"rich","title":"configure random transformations and normalization operations to be done on your image data during training","description":"The example demonstrates how Keras’s `ImageDataGenerator` applies random geometric transformations and normalization to a single input image, producing a grid of augmented variations saved as JPEG files. It shows the effects of rotation, width and height shifts, shear, zoom, and horizontal flipping on a cat photo, with pixel fill mode set to nearest. The code loads an image via `load_img`, converts it to a NumPy array with `img_to_array`, reshapes it into a batch, and then uses the generator’s `.flow()` method to generate and save 20 preview images to a directory. The approach relies entirely on Keras’s preprocessing API, with no custom data source or rendering.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-6167cf71863bdf736df84376b18ce39e?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}