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How does a CNN work?
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The goal of the pooling layer is to pull the most significant features from the convoluted matrix. This is done by applying some aggregation operations, which reduce the dimension of the feature map (convoluted matrix), hence reducing the memory used while training the network.
Benefits of Using CNNs for Machine and Deep Learning. Deep learning is a form of machine learning that requires a neural network with a minimum of three layers. Networks with multiple layers are more accurate than single-layer networks. Deep learning applications often use CNNs or RNNs (recurrent neural networks).
- Convolutional Kernels
- Creating A Feature Map from A Convolutional Kernel
- Padding
- Creating Multiple Channels/Feature Maps with Multiple Kernels
- RGB 3 Channel Input
- Strides
- Many Kernels
- Classification
- Rectified Linear Unit
- Normalisation
Each convolutional layer contains a series of filters known as convolutional kernels. The filter is a matrix of integers that are used on a subset of the input pixel values, the same size as the kernel. Each pixel is multiplied by the corresponding value in the kernel, then the result is summed up for a single value for simplicity representing a gr...
Below is a diagram showing the operation of the convolutional kernel. Below is a visualisation from an excellent presentation, showing the kernel scanning over the values in the input matrix.
To handle the edge pixels there are several approaches: 1. Losing the edge pixels 2. Padding with zero value pixels 3. Reflection padding Reflection padding is by far the best approach, where the number of pixels needed for the convolutional kernel to process the edge pixels are added onto the outside copying the pixels from the edge of the image. ...
When multiple convolutional kernels are applied within a convolutional layer, many channels/feature maps are created, one from each convolutional kernel. Below is a visualisation below showing the channels/feature maps being created.
Most image processing needs to operate on RGB images with three channels. A RGB image is a three dimensional array of numbers otherwise known as a rank three tensor. When processing a three channel RGB image, a convolutional kernel that is a three dimensional array/rank 3 tensor of numbers would normally be used. It is very common for the convoluti...
It is common to use a stride two convolution rather than a stride one convolution, where the convolutional kernel strides over 2 pixels at a time, for example our 3x3 kernel would start at position (1,1), then stride to (1,3), then to 1, 5) and so on, halving the size of the output channel/feature map, compared to the convolutional kernel taking st...
In CNN models there are often there are many more than three convolutional kernels, 16 kernels or even 64 kernels in a convolutional layer is common. These different convolution kernels each act as a different filter creating a channel/feature map representing something different. For example, kernels could be filtering top edges, bottom edges, dia...
If, for example, an image belongs to one of 42 categories and the network’s goal is to predict which category the image belongs to. Following on from the above example with an output of 128x8x8, first the average pool of the rank 3 tensor is taken. The average pool is the mean average of each channel, in the this example each 8x8 matrix is averaged...
A Rectified Linear Unit is used as a non-linear activation function. A ReLU says if the value is less than zero, round it up to zero.
Normalisation is the process of subtracting the mean and dividing by the standard deviation. It transforms the range of the data to be between -1 and 1 making the data use the same scale, sometimes called Min-Max scaling. It is common to normalize the input features, standardising the data by removing the mean and scaling to unit variance. It is of...
Apr 24, 2018 · The main special technique in CNNs is convolution, where a filter slides over the input and merges the input value + the filter value on the feature map. In the end, our goal is to feed new images to our CNN so it can give a probability for the object it thinks it sees or describe an image with text.
Oct 10, 2020 · The goal is to train a generator, a CNN for image tasks that would generate images replicating those in the pre-specified distribution. The discriminator here acts as a judge.
- Aliaksei Mikhailiuk
A convolutional neural network (CNN or ConvNet) is a network architecture for deep learning that learns directly from data. CNNs are particularly useful for finding patterns in images to recognize objects, classes, and categories. They can also be quite effective for classifying audio, time-series, and signal data. How CNNs Work.
Sep 10, 2023 · Convolutional Part. Max Pooling. Application of CNN. Recognition of a Santa Claus by a CNN model. What are the main applications for CNNs? A convolutional neural network draws inspiration from nature, as the connectivity between artificial neurons resembles the organization of the animal visual cortex.
