Programming Ocean Academy

A Typical Convolutional Neural Network (CNN)

Learn the inner workings of CNNs in an interactive and visually appealing way.

A Typical Convolutional Neural Network (CNN)

How CNNs Work

  • Input Image: The network processes an image (e.g., Tweety).
  • Convolution Layer: Extracts features using kernels (filters).
  • Pooling Layer: Reduces the spatial size for computational efficiency.
  • Feature Maps: Hierarchical features are extracted (from edges to complex patterns).
  • Flatten Layer: Converts feature maps into a vector for classification.
  • Fully Connected Layer: Processes features and makes predictions.
  • Output: Probabilistic predictions using the Softmax function.

CNN Code Example

Here's how we can define the layers of a CNN:


import tensorflow as tf
from tensorflow.keras import layers

model = tf.keras.Sequential([
    layers.Input(shape=(224, 224, 3)),  # Input Layer
    layers.Conv2D(filters=10, kernel_size=3),  # Convolution Layer
    layers.MaxPool2D(pool_size=(2, 2)),  # Max Pooling Layer
    layers.Flatten(),  # Flatten Layer
    layers.Dense(10, activation="softmax")  # Fully Connected (Dense) Layer
])