Algorithm Description
Convolutional neural network (CNN) is a deep learning algorithm, which is
mainly used for image
recognition and computer vision tasks.
It extracts features from images
through multi-layer convolution
and pooling operations, and classifies
them through fully connected layers and
softmax classifiers.
The following are the main implementation steps of the algorithm:
1.Convolutional Layer: Convolutional layer is the core part of CNN, used to extract
local features in
images. It performs convolution operations on the input image
and a set of learnable convolution kernels
to generate a set of Feature Maps.
The formula for the convolution operation is as follows:
output(i,j) = ∑(m,n) input(i + m, j + n) * kernel(m,n)
Among them, output (i, j) represents the pixel values of the output feature map, input (i+m, j+n)
represents the pixel values of the input image, and kernel (m, n) represents the weight of the
convolutional kernel.
2. Pooling Layer: Pooling layer is used to reduce the spatial resolution of feature
maps, reduce the
number of parameters and computational complexity. The commonly used
pooling operations include Max
Pooling and Average Pooling.
The formula for maximum pooling is as follows:
output(i,j) = max(input(2i,2j), input(2i,2j+1), input(2i+1,2j), input(2i+1,2j+1))
Among them, output (i, j) represents the pixel values of the output feature map, and input (2i, 2j)
represents the pixel values of the input feature map.
3.Fully Connected Layer: The fully connected layer flattens the output feature maps of
the pooling layer
into one-dimensional vectors and classifies them through a
series of fully connected operations. Each
neuron is connected to all neurons in the previous layer,
outputting a scalar value.
The formula for fully connected layers is as follows:
output = activation(input · weights + bias)
Where, output represents the output of the full connection layer, input represents the input of the full
connection layer, weights represents the weight, bias represents the offset, and activation represents
the Activation function.
4.Softmax classifier: The last layer is usually a Softmax classifier, which converts
the output of the
fully connected layer into a probability distribution.
The formula for the Softmax function is as follows:
output_i = e^(input_i) / ∑(j) e^(input_j)
Among them, output_ I represents the probability of the i-th category, input_ I represents the input of
the i-th category.
The above is the working principle and formula of Convolutional neural network. By stacking convolutional
layers, pooling layers, and fully connected layers multiple times, CNN can learn complex features from
the original image and be used for tasks such as image classification and object detection.
