How to setup a Multiclass Classification Experiment using Fashion MNIST dataset in Keras

How to setup a Multiclass Classification Experiment using Fashion MNIST dataset in Keras

 

 

Setting up a multiclass classification experiment using the Fashion MNIST dataset in Keras involves several steps. First, you need to import the Fashion MNIST dataset, which is a dataset of images of clothing items and their corresponding labels. The dataset contains 60,000 images for training and 10,000 images for testing.

Next, you will need to define the model architecture. The architecture of the model is the structure of the layers and the number of units or neurons in each layer. This can be done using the Sequential class in Keras and adding layers to it. The architecture should be appropriate for the specific task of multiclass classification.

After that, you will need to choose the optimizer and the learning rate. The optimizer is used to adjust the weights of the model to minimize the loss function and the learning rate controls the step size that the optimizer takes in the direction of the gradient.

You will also need to decide the evaluation metrics that you will use to evaluate the model performance. The most common evaluation metrics for deep learning models include accuracy, precision, recall, and F1 score.

Finally, you will need to decide the number of training iterations (epochs) and the batch size. The number of epochs controls the number of times the model will see the entire dataset during training, while the batch size controls the number of samples that the model sees at a time.

In summary, setting up a multiclass classification experiment using the Fashion MNIST dataset in Keras involves importing the dataset, defining the model architecture, choosing the optimizer and learning rate, deciding the evaluation metrics, and deciding the number of training iterations and batch size. The goal of this experiment is to train a deep learning model that is able to classify images of clothing items into different classes with a high level of accuracy.

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to setup a Multiclass Classification Experiment using Fashion MNIST dataset in Keras.

 



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