AI News, Train your first neural network: basic classification
- On 30. september 2018
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Train your first neural network: basic classification
This guide trains a neural network model to classify images of clothing, like sneakers and shirts.
The images show individual articles of clothing at low resolution (28 by 28 pixels), as seen here: Fashion MNIST is intended as a drop-in replacement for the classic MNIST dataset—often used as the "Hello, World"
The MNIST dataset contains images of handwritten digits (0, 1, 2, etc) in an identical format to the articles of clothing we'll use here.
You can access the Fashion MNIST directly from TensorFlow, just import and load the data: Loading the dataset returns four NumPy arrays: The images are 28x28 NumPy arrays, with pixel values ranging between 0 and 255.
Since the class names are not included with the dataset, store them here to use later when plotting the images: Let's explore the format of the dataset before training the model.
The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels: Likewise, there are 60,000 labels in the training set: Each label is an integer between 0 and 9: There are 10,000 images in the test set.
Again, each image is represented as 28 x 28 pixels: And the test set contains 10,000 images labels: The data must be preprocessed before training the network.
Here's the function to preprocess the images: It's important that the training set and the testing set are preprocessed in the same way: Display the first 25 images from the training set and display the class name below each image.
The first layer in this network, tf.keras.layers.Flatten, transforms the format of the images from a 2d-array (of 28 by 28 pixels), to a 1d-array of 28 * 28 = 784 pixels.
These are added during the model's compile step: Training the neural network model requires the following steps: To start training, call the model.fit method—the model is "fit"
And we can check the test label to see this is correct: We can graph this to look at the full set of 10 channels Let's look at the 0th image, predictions, and prediction array.
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