This is because we can get any number between 0 and 1 as the output. The last dense layer will have an output dimension of 10. Both the hidden layers will have the relu activation function. The first one will have an output dimension of 64 and the second one will have 128 as the output dimension. Then we will continue with two hidden Dense() layers after that. The first layer will be a Flatten() that will take the dimensions of the dataset as the input. Now, we can proceed to build the model using Keras API. The color bar at the end shows that the pixels are already scaled with the minimum value of 0.0 and the maximum value of 1.0. This will give us a better idea about what type of images we are dealing with. Visualizing the Imagesīefore building our model, let’s visualize some of the images that are available in the data set. Neural networks always work better with floating-point values. ![]() X_train, x_test = x_train / 255.0, x_test / 255.0Īfter scaling the data, the neural network will be able to learn much faster. Therefore, we can normalize the pixel values by diving them with 255.0. We know that the pixel values can be anything between 0.0 and 255.0. After normalization, the data will range from 0.0 to 1.0. When dealing with neural networks, it is always better to normalize the data. So, the train data and labels contain 60000 instances each and test data and labels contain 10000 instances. Let’s look at all the shapes of the data sets. So, if it is 0, then the item is a T-shirt/top and so on. Now, the data in y_train are labeled as per the table. names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', We can make a list of these items which will help us while visualizing the images later. The data set contains images of 10 types of fashion items. Similarly, the test data and labels are stored in x_test and y_test. The tuples x_train and y_train hold the training data and training labels respectively. (x_train, y_train), (x_test, y_test) = fashion_mnist.load_data() That means we can directly load the data by calling datasets module. The Fashion MNIST data set is already included in the Keras API. This means that our model will consist of densely connected neural network layers.Īlso, Flatten() will help us to flatten the input shape. We will be using Dense() layer to build our neural network. Necessary Importsįirst, we should import all the required packages. If you want you can go take a look at one of the previous articles which take you through the MNIST digit classification first. ![]() This data set is perfect for starting out with deep learning as it is just slightly more challenging than the hand-written digits data set. It contains 60000 training samples and 10000 test samples with each being a greyscale image of size 28×28. This data set is very similar to the hand-written digits data set. This contains images of different fashion items which are divided into 10 classes. But we will be using a newer MNIST data set, that is the fashion MNIST images data set. When starting with deep learning, the most common dataset that people use is the MNIST handwritten digits data set. Building a Dense Neural Network for classifying Fashion MNIST images.How to build your own deep neural network?.About Keras Sequential Model in detail.This is the third part of the series Introduction to Keras Deep Learning. We will be using Keras deep learning to build a classification network. If you are following this tutorial series, then this is going to be your first major step in deep learning with Keras. ![]() ![]() In this post, we are going to learn about the Keras Sequential Model and how to use it to build our own deep neural network.
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