Categories: Deep Learning
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A Convolutional Neural Network (CNN) is composed of three main types of layers: convolutional layers, pooling layers, and fully connected layers. These layers are stacked together to build the architecture of the CNN. A typical sequence of layers in a CNN looks like this:

Convolution -> ReLU -> Pooling -> Convolution -> ReLU -> Pooling -> Flattening -> Fully Connected Layer

Layer Types:

  1. Convolution Layer: This is the first layer used to extract features from the input images, such as edges, textures, and patterns. It applies convolution operations using filters to detect these features.
  2. Pooling Layer: This layer reduces the size of the feature maps produced by the convolution layers, which helps in lowering computational costs. It works by downsampling the feature maps, typically using operations like max pooling, and operates independently on each feature map.
  3. Fully Connected Layer: This layer consists of neurons that are fully connected to all neurons from the previous layer. It connects the features extracted by previous layers and helps in making final predictions. It contains weights and biases that are adjusted during training.
  4. Dropout: Dropout is a technique used to prevent overfitting by randomly “dropping” (i.e., setting to zero) some of the neurons during training. This forces the network to rely on different combinations of neurons, making the model more robust and reducing the chance of overfitting.
  5. ReLU Activation Function: The Rectified Linear Unit (ReLU) function determines which information should be passed to the next layer in the network. It applies a threshold, where any negative values are set to zero and positive values are passed forward, adding non-linearity to the model.

In summary:

Stacking layers in a CNN allows it to progressively extract features from the image, reduce dimensionality, and finally make predictions based on the learned features. Each layer type has a specific role, from extracting features to making final decisions, and techniques like dropout help in preventing overfitting.