# Use of activation function in cnn. What should be my activation function for last layer of neural network? 2019-02-18

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## Difference Between Softmax Function and Sigmoid Function Try out different things and see what combinations lead to the best performance. It means the network will learn specific patterns within the picture and will be able to recognize it everywhere in the picture. Note, in the picture below; the Kernel is a synonym of the filter. So, if the input is an image of the digit 4, the output unit corresponding to 4 would be activated, and so on for the rest of the units. Each filter detects a particular feature at every location on the input.

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## Activation Functions in Neural Networks (Sigmoid, ReLU, tanh, softmax) If the stride is equal to two, the windows will jump by 2 pixels. The above equation represents a sigmoid function. Recap: Convolutional Neural network compiles different layers before making a prediction. Based on our discussion above, it seems that smaller neural networks can be preferred if the data is not complex enough to prevent overfitting. For this reason, we can build neural networks models that can classify more than 2 classes instead of binary class solution. The thresold is set to 0.

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## TensorFlow Image Classification: CNN(Convolutional Neural Network) You mean that y consists of single item? What is Convolutional Neural Network? This is followed by accumulation i. Sigmoid Function In mathematical definition way of saying the sigmoid function take any range real number and returns the output value which falls in the range of 0 to 1. Filed Under: , , Tagged With: , , , , , I am an entrepreneur with a love for Computer Vision and Machine Learning with a dozen years of experience and a Ph. It turns out that Neural Networks with at least one hidden layer are universal approximators. In this tutorial, you will use a grayscale image with only one channel. To learn more, see our.

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## What should be my activation function for last layer of neural network? Most activation functions are monotonic, i. Computing the gradient is much faster, and it induces sparsity by setting a minimum bound at 0. Required Python Packages import numpy as np import matplotlib. The same thing goes for the case where all neurons have affine activation functions i. The pooling or downsampling layer is responsible for reducing the spacial size of the activation maps. Choosing the right activation function depends on the problem that we are facing, there is no activation function which yields perfect results in all the models.

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## Difference Between Softmax Function and Sigmoid Function The material below is taken from a Udacity course. A typical neuron has a physical structure that consists of a cell body, an axon that sends messages to other neurons, and dendrites that receives signals or information from other neurons. The loss is high when the neural network makes a lot of mistakes, and it is low when it makes fewer mistakes. Another aspect of this operation is that it reduces the number of weights significantly. Over the years, various functions have been used, and it is still an active area of research to find a proper activation function that makes the neural network learn better and faster. Depending on this condition, a pooling layer is named overlapping or non-overlapping pooling. Some company's profit or loss for a quarter could be unbounded on either side.

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## CS231n Convolutional Neural Networks for Visual Recognition Similar to sigmoid, tanh also takes a real-valued number but squashes it into a range between -1 and 1. In other words, the activation is simply thresholded at zero see image above on the left. With this interpretation, we can formulate the cross-entropy loss as we have seen in the Linear Classification section, and optimizing it would lead to a binary Softmax classifier also known as logistic regression. The final interpretation is that if you don't use non-linear activations, you would have linear local features extracted from the input. Some of the works use different notations for their layer and architecture description.

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## Difference Between Softmax Function and Sigmoid Function This creates new connections among neurons making the brain learn new things. This is undesirable since neurons in later layers of processing in a Neural Network more on this soon would be receiving data that is not zero-centered. It takes a real-valued number and squashes it into a range between 0 and 1. Therefore, this is an inconvenience but it has less severe consequences compared to the saturated activation problem above. Think about the possible maximum value of the derivative of a sigmoid function.

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## ReLU and Softmax Activation Functions · Kulbear/deep Convolution is an element-wise multiplication. When you have more than two classes, however, you can't use a scalar function like the logistic function as you need more than one output to know the probabilities for all the classes, hence you use softmax. A natural question that arises is: What is the representational power of this family of functions? You can find this article and source code at Refresher: The Sigmoid Function The sigmoid function has been widely used in machine learning intro materials, especially for the logistic regression and some basic neural network implementations. But as in the second figure below linear function will not produce the desired results: Middle figure. However, you may need to know that the sigmoid function is not your only choice for the activation function and it does have drawbacks.

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## CS231n Convolutional Neural Networks for Visual Recognition The weight strength associated with a dendrite, called synaptic weights, gets multiplied by the incoming signal. Apart from the reduction method, the hyperparameter selections determine whether the pooling windows overlap or not. Nowadays, Facebook uses convnet to tag your friend in the picture automatically. You can see that each filter has a specific purpose. We will go into more details about different activation functions at the end of this section. In other words, the output is not a probability distribution does not need to sum to 1.

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## math The main function of it is to introduce non-linear properties into the network. It's also not differentiable so the gradient function isn't fully computable either, but in practice these technicalities are easy to overcome. Also, sum of the results are equal to 0. This output, when fed to the next layer neuron without modification, can be transformed to even larger numbers thus making the process computationally intractable. . You connect all neurons from the previous layer to the next layer. However, notice that once these gradients are added up across a batch of data the final update for the weights can have variable signs, somewhat mitigating this issue.

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