Convolutional Neural Networks (CNNs) are a type of deep learning model widely used for image and video processing tasks, such as image classification, object detection, and facial recognition. CNNs are particularly well-suited for tasks involving spatial data because they can automatically capture spatial hierarchies of features (edges, textures, shapes, etc.) through layers of convolutions.
Here’s a breakdown of how CNNs work:
- The input to a CNN is usually an image, represented as a tensor (a multi-dimensional array) with dimensions corresponding to height, width, and the number of color channels (e.g., 3 for RGB images).
- For example, a 224x224 RGB image would be represented as a tensor of shape
(224, 224, 3).
- Purpose: Extract spatial features like edges, corners, and textures by applying filters (or kernels).
- How It Works:
- A filter is a small matrix of weights (e.g., 3x3 or 5x5) that slides over the input image and performs element-wise multiplication with the image pixels it overlaps. The result is summed to produce a single value.
- This process is repeated across the entire image, producing a feature map (or activation map).
- Parameters:
- Stride: Determines the step size of the filter movement. Larger strides result in smaller feature maps.
- Padding: Adds extra pixels around the input image to control the output size. It can be:
- Valid padding: No padding; the output size shrinks.
- Same padding: Pads to maintain the same output size as input.
- Purpose: Introduce non-linearity into the model, as most real-world data is non-linear.
- How It Works: Applies the Rectified Linear Unit (ReLU) function, which replaces all negative values in the feature map with zero.
- Purpose: Reduce the spatial dimensions of feature maps to decrease computation and prevent overfitting.
- Types:
- Max Pooling: Takes the maximum value from a patch of the feature map (e.g., a 2x2 region).
- Average Pooling: Takes the average of the values in a patch.
- Pooling layers retain the most important features while discarding less relevant details.
- Purpose: Map the extracted spatial features to output categories (e.g., dog, cat, etc.).
- How It Works: Flatten the feature maps into a 1D vector and pass them through one or more fully connected layers. Each neuron in these layers is connected to every neuron in the previous layer.
- Purpose: Generate predictions.
- How It Works: Typically, a softmax activation function is applied in the output layer to convert raw scores into probabilities for each class.
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Feature Hierarchy:
- Early layers detect basic features like edges.
- Middle layers detect complex patterns like textures and shapes.
- Deeper layers detect high-level concepts like objects.
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Weight Sharing:
- Filters are shared across the input image, reducing the number of parameters compared to fully connected networks.
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Training Process:
- CNNs are trained using backpropagation and gradient descent. The weights of filters and neurons are updated to minimize a loss function (e.g., cross-entropy for classification tasks).
- Input: A 32x32 RGB image.
- Convolution + ReLU: Produces multiple feature maps, each highlighting specific patterns.
- Pooling: Reduces feature map dimensions (e.g., 32x32 → 16x16).
- Repeat Convolution + Pooling: Extracts deeper features.
- Fully Connected Layer: Flattens and classifies the features into categories.
This structured approach allows CNNs to learn and recognize patterns in data hierarchically, making them powerful for visual and spatial tasks.