What is the role of the fully connected layer in a CNN?

/ / Published in Artificial Intelligence, EITC/AI/DLPTFK Deep Learning with Python, TensorFlow and Keras, Convolutional neural networks (CNN), Introduction to convolutional neural networks (CNN), Examination review

The fully connected layer, also known as the dense layer, plays a important role in convolutional neural networks (CNNs) and is an essential component of the network architecture. Its purpose is to capture global patterns and relationships in the input data by connecting every neuron from the previous layer to every neuron in the fully connected layer. This layer is typically placed at the end of the CNN, following the convolutional and pooling layers.

The primary function of the fully connected layer is to perform high-level reasoning and decision-making based on the features extracted by the preceding layers. It accomplishes this by learning complex non-linear mappings between the input and output data. Each neuron in the fully connected layer receives inputs from all the neurons in the previous layer and produces an output by applying a set of weights and biases, followed by an activation function.

By connecting every neuron to every other neuron in the fully connected layer, the network is able to learn intricate relationships and dependencies in the data. This allows the model to make predictions based on a combination of different features rather than relying solely on individual features. The fully connected layer acts as a powerful feature extractor, transforming the learned features into a format that can be used for classification or regression tasks.

To illustrate the role of the fully connected layer, consider a CNN trained to classify images of handwritten digits. The convolutional layers extract low-level features such as edges, corners, and textures, while the pooling layers reduce the spatial dimensions of the feature maps. The fully connected layer then takes these abstracted features and combines them to make predictions about the digit shown in the image. For example, it might learn that a combination of curved lines, loops, and closed shapes indicates the presence of a particular digit.

In addition to its feature extraction capabilities, the fully connected layer also contributes to regularization and model capacity control. The large number of parameters in the fully connected layer enables the network to learn complex representations, but it also increases the risk of overfitting. To mitigate this, regularization techniques such as dropout or L2 regularization can be applied to the fully connected layer, preventing the network from relying too heavily on any single connection.

The fully connected layer in a CNN is responsible for capturing global patterns and relationships in the input data by connecting every neuron from the previous layer to every neuron in the fully connected layer. It performs high-level reasoning and decision-making based on the learned features and contributes to regularization and model capacity control.

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