Building VanillaNN: A Simplified Neural Network Framework from Scratch in Python
<p>In this article, we’ll delve into creating a foundational framework for a neural network, reminiscent of the Keras model, entirely from scratch in Python. Before proceeding, it’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’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='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
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’s vital to encapsulate its key functionalities. To this end, we’re introducing the <code>Vanilla</code> 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>
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