What is CNN?
The neural nets exists and in addition to that an image is convoluted, converted in pixel level and studied, converted and a max pooling, this entire thing is known as convolution + pooling layers. A fully connected layers of flattened structure of numpy array and a hidden layer is then classified into various classes as binary or based on no.of outcomes. In the first layer we try to understand what each image pixel tries to account for using various filter and then we flatten them and pass it to neural network, then it goes to a classifier. Want to become master in Artificial Intelligence check out this Artificial Intelligence Training.
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The visual context will go through each and every part of image and try to understand what is present in each area of the image. The output should be in the form of the class. If you have any technical doubts & queries related to Artificial Intelligence, post the same on Intellipaat Community.
Convolutional Neural Network Architecture:
Convolution layer: Here we try to decompose RGB to multidimensional layer, and apply filter to each layer. A filter tries to learn all the combinations present in the RGB layer. A strider is used to stride to each matrix in the image. We try to understand these image using convolution strider.
CNN – Arch :
Given an input image, it goes to convolution+Relu, each area has a 3D, RGB, then it goes to next pooling layer where it shrinks the max value and this cycle keep repeating. This is the learning process. We try to classify the values and then we have to apply neural nets and try to figure out what the actual image is. Given that it is a car, softmax gives a value of 0 to 1, the probability of the maximum is identified as the car.
Important aspects of CNN:
The important aspects of CNN are filters, receptive field, stride, padding, pooling and ReLU layers.
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The image is strided one bit by bit and it end up till the last window. We have to mostly str ide one by one, if we do it double, there might occur a padding problem. Padding size should be nearly half of slider size. Otherwise we might miss some information or read other unwanted information. We go for max pooling always to avoid any such problem. Doing max pooling will cause the matrix size to shrink, from 4×4 to 3×3 and so on.
Activation function :
Whatever negative values comes, it clips off to zero. Learn more about how IBM Watson Artificial Intelligence Supercomputer is redefining our world in this insightful blog now!
- Forward Pass
- Loss function
- Backward pass
- Weight update
Learning Rate :
If it is very high, it might not reach Global minima. If it is very low, it will be time consuming, but the learning is efficient and reached global minima. The formula for learning rate is
where w is the weight, is the initial weight, is the learning rate and is the change in error factor.
The layer in CNN consists of convolution, ReLU and pooling together.
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Steps to run a CNN :
- Creating a model with mLP
- Convolutional layer
- Activation layer
- Pooling layer
- Dense (fully connected layer)
- Model compile and train