It's important that the training set and the testing set be preprocessed in the same way: train_images = train_images / 255.0 Scale these values to a range of 0 to 1 before feeding them to the neural network model. If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255: plt.figure() The data must be preprocessed before training the network. Again, each image is represented as 28 x 28 pixels: test_images.shapeĪnd the test set contains 10,000 images labels: len(test_labels) Likewise, there are 60,000 labels in the training set: len(train_labels)Įach label is an integer between 0 and 9: train_labelsĪrray(, dtype=uint8) The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels: train_images.shape Let's explore the format of the dataset before training the model. 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] Since the class names are not included with the dataset, store them here to use later when plotting the images: class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', These correspond to the class of clothing the image represents: LabelĮach image is mapped to a single label. The labels are an array of integers, ranging from 0 to 9. The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255.

The model is tested against the test set, the test_images, and test_labels arrays.The train_images and train_labels arrays are the training set-the data the model uses to learn.Loading the dataset returns four NumPy arrays: Import and load the Fashion MNIST data directly from TensorFlow: fashion_mnist = tf._mnist You can access the Fashion MNIST directly from TensorFlow. Here, 60,000 images are used to train the network and 10,000 images to evaluate how accurately the network learned to classify images. They're good starting points to test and debug code. Both datasets are relatively small and are used to verify that an algorithm works as expected. This guide uses Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc.) in a format identical to that of the articles of clothing you'll use here. Fashion-MNIST samples (by Zalando, MIT License).įashion MNIST is intended as a drop-in replacement for the classic MNIST dataset-often used as the 'Hello, World' of machine learning programs for computer vision. The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here:įigure 1. This guide uses the Fashion MNIST dataset which contains 70,000 grayscale images in 10 categories.

This guide uses tf.keras, a high-level API to build and train models in TensorFlow. It's okay if you don't understand all the details this is a fast-paced overview of a complete TensorFlow program with the details explained as you go. It creates common layouts of all subplots.ĭata = įig, axs = plt.This guide trains a neural network model to classify images of clothing, like sneakers and shirts. The pyplot.subplots create a figure and a set of subplots.

Plt.subplot(ax2) # add ax2 to the figure again Plt.delaxes(ax2) # delete ax2 from the figure Plt.subplot(224, sharex=ax1, facecolor='red') # add a red subplot that shares the x-axis with ax1 Plt.subplot(223, projection='polar') # add a polar subplot In the second syntax, pos is a three-digit integer, where the first digit defines a number of rows, the second digit define the number of columns, and the last third digit define an index of the subplot.ĭifferent ways to use subplot: plt.subplot(221)Īx1=plt.subplot(2, 2, 1) # equivalent but more generalĪx2=plt.subplot(222, frameon=False) # add a subplot with no frame In the first syntax, three parameters nrows, ncols and index define the position of the subplot. A pyplot.subplot method adds a subplot to the current figure.

Pyplot subplot