Programming Ocean Academy

A Typical Feedforward Neural Network (FFN)

Understand the intricate workings of FFNs in an interactive and visually appealing way.

A Typical Feedforward Neural Network (FFN)

How FFNs Work

  • Input Layer: Accepts input features (e.g., SNPs or other data points) for processing.
  • Hidden Layers: - Multiple layers of interconnected nodes that transform the input. - Each node applies weights, biases, and an activation function to the inputs.
  • Weights and Bias: Each connection between neurons has a weight, and each neuron has a bias to modify the weighted sum of inputs.
  • Activation Function: Introduces non-linearity, enabling the network to learn complex patterns. Common examples include Sigmoid and ReLU.
  • Forward Propagation: Data flows in one direction—from the input layer, through hidden layers, to the output layer.
  • Output Layer: Produces the final result, such as a classification or prediction, based on the processed input.
  • Applications: Widely used in tasks like image classification, regression, and time-series prediction.

FFN Code Example

Here's how we can define the layers of an FFN:


import tensorflow as tf
from tensorflow.keras import layers

model = tf.keras.Sequential([
    layers.Input(shape=(784,)),  # Input Layer (Flattened input for FFN)
    layers.Dense(128, activation="relu"),  # Hidden Layer 1
    layers.Dense(64, activation="relu"),  # Hidden Layer 2
    layers.Dense(10, activation="softmax")  # Output Layer
])