Visualize images1/5/2024 To understand how the Image Summary API works, you're now going to simply log the first training image in your training set in TensorBoard.īefore you do that, examine the shape of your training data: print("Shape: ", train_images.shape) # Names of the integer classes, i.e., 0 -> T-short/top, 1 -> Trouser, etc.Ĭlass_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', (train_images, train_labels), (test_images, test_labels) = \ # The labels are integers representing classes.įashion_mnist = _mnist The data is already divided into train and test. This dataset consist of 70,000 28x28 grayscale images of fashion products from 10 categories, with 7,000 images per category.įirst, download the data: # Download the data. You're going to construct a simple neural network to classify images in the the Fashion-MNIST dataset. "This notebook requires TensorFlow 2.0 or above." Print("TensorFlow version: ", tf._version_)Īssert version.parse(tf._version_).release >= 2, \ # Load the TensorBoard notebook extension. # %tensorflow_version only exists in Colab. You will work through a simple but real example that uses Image Summaries to help you understand how your model is performing. You will also learn how to take an arbitrary image, convert it to a tensor, and visualize it in TensorBoard. In this tutorial, you will learn how to use the Image Summary API to visualize tensors as images. You can also log diagnostic data as images that can be helpful in the course of your model development. This can be extremely helpful to sample and examine your input data, or to visualize layer weights and generated tensors. Using the TensorFlow Image Summary API, you can easily log tensors and arbitrary images and view them in TensorBoard.
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