# Do inputs to a neural network need to be in [-1,1]?

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Those who are looking for an answer to the question Â«Do inputs to a neural network need to be in [-1,1]?Â» often ask the following questions:

### đ» Can neural network have multiple inputs?

Similarly, **Neural Networks can** be trained on **multiple inputs**, such as images, audio and text, processed accordingly (through CNN, NLP, etc.), to come up with an effective prediction of the target emotion.

- Must neural network inputs be numeric?
- Should i normalize neural network inputs?
- What inputs for a neural network?

### đ» Can neural network take multidimensional inputs?

So I search for some cases of how a pure Neural Network can process multidimensional data like a image or a sentence. A classical way for image processing in a neural network is first flatten a 2D inputs to a vector (if an image is 64*64 then the size of vector is 4096) and this vector is going to be feed into a neural network which means at this ...

- Can a neural network have integer inputs?
- How many inputs to a neural network?
- What are inputs outputs states neural network?

### đ» How many inputs for neural network?

In general more data is better and for most problems you'll need at least 1000 training inputs. But it could be wildly more than that depending on the problem at hand. I'd also like to argue against Alket's statement that you should avoid **neural networks**.

- How to build a neural network python multiple inputs?
- How to build neural network given inputs and outputs?
- How to code categorical inputs for a neural network?

8 other answers

I'm curious if neural networks (or neurolab in particular) needs the target/input data to be [-1:1]? I'm trying to train a network to predict water evaporation from my kitchen garden, given these ...

These neural nets with one or two layers are not of much use, but are helpful in understanding the inner working of the neural network. Knowing the theory of neural networks, how they work, and the significance of layers helps lay a foundation deep learning. Understanding how simple networks work will lay a foundation that will help us to easily understand how deep networks work. The first, and the most basic example of a neural network that we will learn is a âperceptronâ.

Input Layers, Neurons, and Weights â The basic unit in a neural network is called as the neuron or node. These units receive input from the external source or some other nodes. The idea here is to compute an output based associated weight.

Only one net input function and one transfer function block are required for each layer. Processing Blocks. Double-click the Processing Functions block in the Neural library window to open a window containing processing blocks and their corresponding reverse-processing blocks.

Yes, thatâs why there is a need to use big data in training neural networks. They work because they are trained on vast amounts of data to then recognize, classify and predict things.

An approach examined in this article is to train a hybrid network consisting of a MLP and an encoder with multiple output units; that is, a separate output unit for each of the various combinations of values of the categorical variables. Input to the feed forward subnetwork of the hybrid network is then restricted to truly numerical quantities.

Machine learning algorithms that use neural networks generally do not need to be programmed with specific rules that define what to expect from the input. The neural net learning algorithm instead learns from processing many labeled examples (i.e. data with with "answers") that are supplied during training and using this answer key to learn what characteristics of the input are needed to construct the correct output.

Artificial Neural Network - 2 inputs, 11 outputs. Learn more about ann, neural network, deep learning, neural networks

We've handpicked 21 related questions for you, similar to Â«Do inputs to a neural network need to be in [-1,1]?Â» so you can surely find the answer!

Does it matter of inputs are ordered for neural network?An input neuron receives inputs from the original data. Hidden neurons and output neurons receive inputs from the output of other neurons in the neural network. Inputs establish relationships between neurons, and the relationships serve as a path of analysis for a specific set of cases.

Is it possible to have multiple inputs in a neural network?Yep, but you would have to create weight matrices on your own as those are not standard fully connected layers and would have to multiply those by hand with the inputs appropriately. â Szymon Maszke Mar 5 '19 at 20:38

Tensorflow - can neural networks handle redundant inputs?I have a fully connected neural network with the following number of neurons in each layer [4, 20, 20, 20, ..., 1]. I am using TensorFlow and the 4 real-valued inputs correspond to a particular poi...

Does neural network need normalization?Standardizing **Neural Network** DataâŠ In theory, it's not necessary to normalize numeric x-data (also called independent data). However, practice has shown that when numeric x-data values are normalized, neural network training is often more efficient, which leads to a better predictor.

To define and train a deep learning network with multiple inputs, specify the network architecture using a layerGraph object and train using the trainNetwork function with datastore input.

How to transform inputs and extract useful outputs in a neural network?Subtracting the mean centers your data at the origin, and dividing by the standard deviation makes sure most of it is between -1 and 1, where the neuron's output is most sensitive to its input. This is called z-score normalization because each input value is replaced by its z-score. Do the above for each input variable.

Can convolutional neural networks have inputs as numbers?Convolutional Neural Network. So, the only difference is that in the case of FCNN, we consider all the inputs to compute the value of any of the neurons whereas, in the case of CNN, we consider only a neighbor of the inputs(we can consider this situation as that the weights of the other inputs are 0).

Can convolutional neural networks have inputs as text?Now, a convolutional neural network is different from that of a neural network because it operates over a volume of inputs. Each layer tries to find a pattern or useful information of the data. An...

How to use neural networks with vector inputs?Wrapping the Inputs of the Neural Network With NumPy. Youâll use NumPy to represent the input vectors of the network as arrays. But before you use NumPy, itâs a good idea to play with the vectors in pure Python to better understand whatâs going on. In this first example, you have an input vector and the other two weight vectors.

Does neural network need feature engineering?A neural network takes a group of input features and creates interactions between them that help best predict the output. As mentioned above, we can force the model to consider certain combinations by engineering them.

Neural networks - how does relu deal with negative inputs?So the inputs are (mostly) continuous variables that can be negative and positive. Outputs are stock returns which can be [-1, inf). The paper is rather vague in their methodology but mentioned that they are using ReLU for their hidden layers and linear activation for output.

Does a neural network need continuous variables?A **neural net can** , at least theoretically, approximate any continuous function. It is called the Universal approximation theorem. Of course it might still be hard to learn but in practice it generally works quite well even if you don't find the optimal solution.

An artificial neural network (ANN) is the piece of **a computing system designed to simulate the way the human brain analyzes and processes information**. It is the foundation of artificial intelligence (AI) and solves problems that would prove impossible or difficult by human or statistical standards.

- In computational neuroscience,
**neural**network models, at various levels**of**biological detail, have been essential to understanding dynamics in biological**neural networks**and elementary computational functions27,28.

#### Hidden layers and neurons

They allow you to model complex data thanks to their nodes/neurons. They are âhiddenâ because the true values of their nodes are unknown in the training dataset. In fact, we only know the input and output. Each**neural network**has at least one

**hidden layer**. Why we need bias in neural network?

This weight allows the model to move up and down if itâs needed to fit the data. With bias, line doesnât need to cross origin (image by Author). Thatâs the reason why we need bias neurons in neural networks. Without these spare bias weights, our model has quite limited âmovementâ while searching through solution space.

Neural network: what is a neural network?Neural Network Defined Neural networks consist of thousands and millions of artificial "brain cells" or computational units that behave and learn in an incredibly similar way to the human brain.

Are neural networks able to deal with non-normalised inputs?As far as I can tell, neural networks have a fixed number of neurons in the input layer. If neural networks are used in a context like NLP, sentences or blocks of text of varying sizes are fed to ...

Which are the inputs of neural networks with genetic algorithm?For each layer, there is an associated weights matrix. Just multiply the inputs matrix by the parameters matrix of a given layer to return the outputs in such layer. Chromosomes in GA are 1D vectors and thus we have to convert the weights matrices into 1D vectors.

Is deep neural network an artificial neural network?A deep neural network (DNN) is **an artificial neural network (ANN) with multiple layers between the input and output layers**. There are different types of neural networks but they always consist of the same components: neurons, synapses, weights, biases, and functions.

Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.