Building VanillaNN: A Simplified Neural Network Framework from Scratch in Python

<p>In this article, we&rsquo;ll delve into creating a foundational framework for a neural network, reminiscent of the Keras model, entirely from scratch in Python. Before proceeding, it&rsquo;s essential to have a foundational grasp of neural networks, including concepts like forward-backward propagation, loss functions, optimizers, and regularization techniques. With the introduction out of the way, let&rsquo;s dive straight in!</p> <p>For context, a standard neural network model in the Keras framework appears as follows. Our goal with this custom implementation is to mirror the Keras framework, albeit in a simplified form.</p> <pre> # define the keras model model = Sequential() model.add(Dense(12, input_dim=8, activation=&#39;relu&#39;)) model.add(Dense(8, activation=&#39;relu&#39;)) model.add(Dense(1, activation=&#39;sigmoid&#39;)) model.compile(loss=&#39;binary_crossentropy&#39;, optimizer=&#39;adam&#39;, metrics=[&#39;accuracy&#39;]) model.fit(X, y, epochs=150, batch_size=10)</pre> <h1><strong>Creating the Core Neural Network Class</strong></h1> <p>For a structured and well-organized neural network, it&rsquo;s vital to encapsulate its key functionalities. To this end, we&rsquo;re introducing the&nbsp;<code>Vanilla</code>&nbsp;class. This class will not only house the main functions of our neural network but also maintain variables that define its structure and parameter settings.</p> <p><a href="https://blog.guptanitish.com/building-vanillann-a-simplified-neural-network-framework-from-scratch-in-python-c82f39071ee0">Website</a></p>