Understanding of Convolutional Neural Network (CNN) — Deep Learning
In neural networks, Convolutional neural network (ConvNets or CNNs) is one of the main categories to do images recognition, images classifications.
In neural networks, Convolutional neural network (ConvNets or CNNs) is one of the main categories to do images recognition, images classifications. Objects detections, recognition faces etc., are some of the areas where CNNs are widely used.CNNs use image recognition and classification in order to detect objects, recognize faces, etc.They are made up of neurons with learnable weights and biases. Each specific neuron receives numerous inputs and then takes a weighted sum over them, where it passes it through an activation function and responds back with an output.
CNNs are primarily used to classify images, cluster them by similarities, and then perform object recognition. Many algorithms using CNNs can identify faces, street signs, animals, etc.
Visualization of an image by a computer
Let’s say we have a color image in JPG form and its size is 480 x 480. The representative array will be 480 x 480 x 3, it will see h x w x d( h = Height, w = Width, d = Dimension ). Each of these numbers is given a value from 0 to 255 which describes the pixel intensity at that point. RGB intensity values of the image are visualized by the computer for processing.
A Classic CNN:
Contents of a classic Convolutional Neural Network: -
2.Activation operation following each convolutional layer.
3.Pooling layer especially Max Pooling layer and also others based on the requirement.
4.Finally Fully Connected Layer.
Convolution is the first layer to extract features from an input image. Convolution preserves the relationship between pixels by learning image features using small squares of input data. It is a mathematical operation that takes two inputs such as image matrix and a filter or kernel.
Consider a 5 x 5 whose image pixel values are 0, 1 and filter matrix 3 x 3 as shown in below
Then the convolution of 5 x 5 image matrix multiplies with 3 x 3 filter matrix which is called “Feature Map” as output shown in below
Figure : 3 x 3 Output matrix
Convolution of an image with different filters can perform operations such as edge detection, blur and sharpen by applying filters. The below example shows various convolution image after applying different types of filters (Kernels).
Figure : Some common filters
Data or imaged is convolved using filters or kernels. Filters are small units that we apply across the data through a sliding window. The depth of the image is the same as the input, for a color image that RGB value of depth is 4, a filter of depth 4 would also be applied to it. This process involves taking the element-wise product of filters in the image and then summing those specific values for every sliding action. The output of a convolution that has a 3d filter with color would be a 2d matrix.
Now, the best way to explain a convolutional layer is to imagine a flashlight that is shining over the top left of the image. In order to understand how this works, imagine as if a flashlight shines its light and covers a 5 x 5 area. And now, let’s imagine this flashlight sliding across all the areas of the input image. This flashlight is called a filter(or sometimes referred to as a neuron or a kernel) and the region that it is shining over is called the receptive field. This filter is also an array of numbers (the numbers are called weights or parameters).
In several cases, we incorporate techniques, including padding and strided convolutions, that affect the size of the output. As motivation, note that since kernels generally have width and height greater than , after applying many successive convolutions, we tend to wind up with outputs that are considerably smaller than our input. If we start with a pixel image, layers of convolutions reduce the image to pixels, slicing off of the image and with it obliterating any interesting information on the boundaries of the original image. Padding is the most popular tool for handling this issue.
In other cases, we may want to reduce the dimensionality drastically, e.g., if we find the original input resolution to be unwieldy. Strided convolutions are a popular technique that can help in these instances.
As described above, one tricky issue when applying convolutional layers is that we tend to lose pixels on the perimeter of our image. Since we typically use small kernels, for any given convolution, we might only lose a few pixels, but this can add up as we apply many successive convolutional layers. One straightforward solution to this problem is to add extra pixels of filler around the boundary of our input image, thus increasing the effective size of the image. Typically, we set the values of the extra pixels to zero. In Fig. 6.3.1, we pad a input, increasing its size to . The corresponding output then increases to a matrix. The shaded portions are the first output element as well as the input and kernel tensor elements used for the output computation: .
Sometimes filter does not fit perfectly fit the input image. We have two options:
- Pad the picture with zeros (zero-padding) so that it fits
- Drop the part of the image where the filter did not fit. This is called valid padding which keeps only valid part of the image.
Non Linearity (ReLU)
ReLU stands for Rectified Linear Unit for a non-linear operation. The output is ƒ(x) = max(0,x).
Why ReLU is important : ReLU’s purpose is to introduce non-linearity in our ConvNet. Since, the real world data would want our ConvNet to learn would be non-negative linear values.
There are other non linear functions such as tanh or sigmoid that can also be used instead of ReLU. Most of the data scientists use ReLU since performance wise ReLU is better than the other two.
Stride is the number of pixels shifts over the input matrix. When the stride is 1 then we move the filters to 1 pixel at a time. When the stride is 2 then we move the filters to 2 pixels at a time and so on. The below figure shows convolution would work with a stride of 2.
Pooling layers section would reduce the number of parameters when the images are too large. Spatial pooling also called subsampling or downsampling which reduces the dimensionality of each map but retains important information. Spatial pooling can be of different types:
- Max Pooling
- Average Pooling
- Sum Pooling
Max pooling takes the largest element from the rectified feature map. Taking the largest element could also take the average pooling. Sum of all elements in the feature map call as sum pooling.
Fully Connected Layer
The layer we call as FC layer, we flattened our matrix into vector and feed it into a fully connected layer like a neural network.
In the above diagram, the feature map matrix will be converted as vector (x1, x2, x3, …). With the fully connected layers, we combined these features together to create a model. Finally, we have an activation function such as softmax or sigmoid to classify the outputs as cat, dog, car, truck etc.,