Questions tagged [activation-function]
Activation function is a non-linear transformation, usually applied in neural networks to the output of the linear or convolutional layer. Common activation functions: sigmoid, tanh, ReLU, etc.
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What is a proper activation function with simulated-annealing trainer for neural network?
I'm developing a gpu-accelerated simulated annealing based neural network trainer library. Currently its stuck on how to converge on "array sorting by neural network 3:10:20:10:3 topology".
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Why would multiple activation layers be used in a row?
I'm learning about ML and I was looking at
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Alternative to ELU and Leaky ReLU?
I was talking with a friend about different activation functions (we are still new to ML). One thing that I didn't like about ELU was the vanishing gradient, and about Leaky ReLU that it's not ...
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Relu derivative value
I have a stupid question on the derivative of relu activation function. After the finding the difference of the true output $t_k$ and predicted output $a_k$, why is the value of the $d_{a3}$ \ $d_{z3}$...
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Query about Sigmoid activation function calculation
While applying sigmoid activation function (in finding y label), I have calculated it as below:
y = 0.35 + (0.8 * 0.1) + (0.3 * 0.6) + (-0.2 * 0.4) = 0.53
sigmoid_y = 0.625
how do we take threshold ...
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How to implement a custom loss with a non-mathematical operation (simulation) that backpropagates with PyTorch?
I am writing a Neural Network, which output is not used directly for the loss-function, but rather as the input for a simulation model. After the simulation ran, I am using the simulated_value and the ...
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How ReLU is bringing non linearity and why it is not an alternative to dropout?
The differentiation of ReLU function is 1 when input is greater than 0, and 0, when input is less than or equal to 0. In the backpropagation process it doesn’t change the value of d(error)/d(weight) ...
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Why use tanh (or any other activation function)?
In machine learning, it is common to use activation functions like
tanh, sigmoid, or ReLU to introduce non-linearity into a neural
network. These non-linearities help the network learn complex
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Regression model doesn't handle negative values
I'm trying to create a model that, given a feature $x_i$, predicts $y_i$ such that $y_i=ax^2_i+bx_i+c$ by using backpropagation.
To do this, I'm using the ReLU activation function for each layer.
The ...
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Is bias nothing but perceptron threhold value?
I was revisiting neural network basics from this post. The perceptron follows below equation:
$$\begin{align}
y & = 1 & \text{if } \sum_{i=1}^n w_i\times x_i \geq \theta \\
& = 0 & \...
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How do I implement recurrent activations for LSTM/GRU cells in Pytorch?
Although Tensorflow has simple parameters with which I can initialize the recurrent activation of a GRU or LSTM cell, Pytorch does not.
What is the best way to add recurrent activation in pytorch?
<...
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Whats the advantage of He Intialization over Xavier Intialization?
For Weights initialization, I read that He doesn't consider linear activation of neurons as Xavier Initialization; in this context, what does linear initialization mean?
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Why is the optimal output out of domain in A2C?
If each state has an optimal action, then the optimal actions distribution vector is a one-hot vector kind of like [0,0,1,0,0,0].
But with algorithms like A2C, we ...
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Sigmoid Activation Function (Output layer) Alternative
I have a Convolutional-VAE architecture where the target images are in the range [0, 1], their pixel values. To synthesize/reconstruct images in this scale, I am using a sigmoid activation function in ...
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Is it possible to tell if one activation function is better than the other one based on their graphs?
I am attempting to formulate my own activation function. However, I'm new to neural networks, am not yet ready to test it, but would want to know if I already landed on a better activation function ...
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CNN model why is ReLu used in Conv1D layer and in the first Dense Layer?
I have a problem. I have a CNN model which is used for an NLP problem. This is written in Python. I have questions about this, which I can't find an answer to.
Why is ReLu used inside the Conv1D ...
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Question about non linearity of activation function
I have a basic question about activation functions. It is told that they are added to the network to introduce non linearity.
However, the neural network itself is non linear. Isn' it? If we see any ...
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What is the advantage of using Euler's number (e^x) instead of another base in the softmax equation?
I understand the softmax equation is
$\boldsymbol{P}(y=j \mid x)=\frac{e^{x_{j}}}{\sum_{k=1}^{K} e^{x_{k}}}$
My question is: why use $e^x$ instead of say, $3^x$. I understand $e^x$ is it's own ...
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If sigmoid activation function is prone to vanishing and exploding gradients can we not use it in final layer of binary classfication?
Many paper and books say that sigmoid activation function with random intialization is prone to vanishing/exploding gradients therefore it is better to use LeakyRelu, Elu, or Relu. Does this mean that ...
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Training deep neural networks with ReLU output layer for verification
Most algorithms for verification of deep neural network require ReLU activation functions in each layer (e.g. Reluplex).
I have a binary classification task with classes 0 and 1. The main problem I ...
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Performance comparison of different activation functions
I was trying to find a way to compare the test accuracy and test loss of different activation functions (such as tanh, sigmoid, relu), so I came up with this script:
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Intuitively, why do Non-monotonic Activations Work?
The swish/SiLU activation is very popular, and many would argue it has dethroned ReLU. However, it is non-monotonic, which seems to go against popular intuition (at least on this site: example 1, ...
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Activation Functions in Haykins Neural Networks a comprehensive foundation
In Haykins Neural Network a comprehensive foundation, the piecwise-linear funtion is one of the described activation functions.
It is described with:
The corresponding shown plot is
I don't really ...
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Why does using tanh worsen accuracy so much?
I was testing how different hyperparameters would change the output of my multilayer perceptron for a regression problem
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Neural Network not Deep
I have found this image link I would like to know what NNs are not deep neural? The first three?
Also what kind of functional activations do they use?
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Why using the hyperbolic tangent or the sigmoid as activation function on the last layer gaves the same result in accuracy?
The problem
I'm making a simple Multilayer Perceptron (MLP), in Keras, that has to do the binary classification from some float type of data. Each single data is a group of three float values (e.g. 32....
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How to prove Softmax Numerical Stability?
I was playing around with the softmax function and tried around with the numerical stability of softmax. If we increase the exponent in the numerator and denominator with the same value, the output of ...
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What happens if you don't include any activation function on hidden classification layers?
What happens if we don't apply an activation function to the classification hidden layers and apply it only for the final output layer (Sigmoid, Softmax)?
I'm asking this because I have trained a CNN ...
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Is there any point in having layers in a neural network for regression problems?
In my textbook I read that an MLP and linear activation functions for the hidden layers can be reduced to a simple input-output system, i.e. no hidden layers. This makes sense to me. Later on I read ...
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Activation and Loss Function not chosen correctly when use Neural Network
I have three classes for my text dataset before.
These are my classes:
0 = Cat
1 = Not Both
2 = Dog
Then I use this code:
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Derive backpropagation for PreLU
I want to derive the back propagation functions for the Parametric Relu activation function which is defined as follows:
$$
h_a(x) = \text{max}(ax, x)
$$
I want to derive $ \frac{\partial L}{\partial ...
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What meaning does exp function have? [closed]
Is there any problem that solution(or algorithm) would be exp function?
Let's say f(x)=2^x.
It's an answer of a problem when you would like to know how many pieces would be made when you fold a paper ...
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The case of (1,478) dim and parameters of neural network to find out
colleagues, actually I am kind'a new to NN, but hard trying..
I have data:
Index: 40073 entries (excluded from training, UID)
Columns: 484 entries
dtypes: bool(468), float64(2), int64(13), object(1)
I ...
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Why deep learning models still use RELU instead of SELU, as their activation function?
I am a trying to understand the SELU activation function and I was wondering why deep learning practitioners keep using RELU, with all its issues, instead of SELU, which enables a neural network to ...
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Difference between ReLU, ELU and Leaky ReLU. Their pros and cons majorly
I am unable to understand when to use ReLU, ELU and Leaky ReLU.
How do they compare to other activation functions(like the sigmoid and the tanh) and their pros and cons.
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Word embedding autoencoder
I'm trying to train a word embedding autoencoder, but it either doesn't train, or trains but doesn't make predictions. I know I'm doing something wrong, so any help is greatly appreciated.
Here is my ...
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Activation Function
I am very new to machine learning and made an experiment myself. I have a few questions:
Can I use $Y = sin(x)$ or $Y = 2x$ as an activation function for a neural network?
Is it necessary to increase ...
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How can the ReLU function lead to convergence?
The gradient descent algorithm is based on the fact that the gradient decreases as we move towards the optimum point. However, in the activations by the ReLU ...
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If the input to the autoencoder is normalized, do we need to use sigmoid on the last layer?
According to: https://stackoverflow.com/questions/65307833/why-is-the-decoder-in-an-autoencoder-uses-a-sigmoid-on-the-last-layer
The last layer activation function contains sigmoid in order to the ...
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Is there comprehensive list of activation functions and their applications for a Neural Network?
I am aware of common activation functions like sigmoid, tanh,ReLu, Leaky ReLu. Even heard about a function called Swish. Now is there any detailed information on other activations functions and some ...
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Can this dataset be separated linearly?
Is this dataset linearly separable? If not, can it be converted into one by applying some function as it seems to follow the same pattern?
Also, which classification algorithms could be used to fit ...
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How batch normalization layer resolve the vanishing gradient problem?
According to this article:
https://towardsdatascience.com/the-vanishing-gradient-problem-69bf08b15484
The vanishing gradient problem occurs when using the sigmoid ...
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Why the gradient of a ReLU for X>0 is 1?
Gradient is derivative of several variables.
I can't understand why is the gradient of a ReLU for X>0 is 1 ? and 0 for x < 0 ?
I tried to search for proof and examples but didn't found any good ...
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Training set Distribution and Activation function/Loss function correlation
How should the probability distribution of the training set influence the choice of the activation function / loss function?
For instance if I have a Multinoulli distribution, which activation ...
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Do Activation Functions map to Higher Dimensions?:
I just started learning tensorflow and I have a question regarding activation functions used in neural networks, I watched a 3b1b video a while ago and it seems it squished the value into an interval ...
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Numerically stable hyperbolic tangent
The hyperbolic tangent is commonly used as an activation function:
$$
tanh(x) = \frac{e^x - e^{-x}}{e^x + e^{-x}}
$$
Although, it is unclear how this function is implemented to be numerically stable ...
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Applying activation on part of the layer in Keras
Context
I am trying to implement the YOLO algorithm in Keras. What I have so far is the following network:
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Is it possible to implement a vectorized version of a Maxout activation function?
I want to implement an efficient and vectorized Maxout activation function using python numpy.
Here is the paper in which "Maxout Network" was introduced (by Goodfellow et al).
For example, ...
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Input and output layer activation functions of neurons in Orange
What are the activation functions of the neurons in the input and output layer of a neural network model from the Orange machine learning application?
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Can there be in-active neuron in output layer
I am new to deep learning, and was studying about it. I know that input from input layer is multiplied with weights and then added with bias. And output of this is passed to a activation function of a ...
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