This code defines a neural network with one input layer, three hidden layers with 8, 4, and 2 neurons, respectively, and one output layer with one neuron. The activation function used in the hidden layers is ReLU, and the output layer uses the sigmoid activation function. The model is compiled with the binary cross-entropy loss function, the Adam optimization algorithm, and the accuracy metric.
The dataset used in this example is the Pima Indians Diabetes dataset, which is loaded using the numpy library. The model is trained for 150 epochs with a batch size of 10. Finally, the output of the last hidden layer is extracted as the embedding of the input data.
These steps provide a simple example of creating an embedding using an ANN in Python. More complex embeddings can be created by adjusting the architecture of the neural network and the training parameters. There are many resources available online to learn more about creating embeddings using ANNs, including tutorials and courses.
Want to print your doc? This is not the way.
Try clicking the ⋯ next to your doc name or using a keyboard shortcut (