Questions tagged [neural-networks]

For questions about a artificial networks, such as MLPs, CNNs, RNNs, LSTM, and GRU networks, their variants or any other AI system components that qualify as a neural networks in that they are, in part, inspired by biological neural networks.

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More Relevant and Compact Learning Material For Artificial Intelligence [closed]

I'm an Intermediate Machine Learning student and want to get more detailed and specific intuition about Artificial Intelligence. I have made a couple of searches over the well-observed learning sites ...
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How can I train a neural network if I don't have enough data?

I have created a neural network that is able to recognize images with the numbers 1-5. The issue is that I have a database of 16x5 images which ,unfortunately, is not proving enough as the neural ...
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NN for card game evaluation function

I've written an Monte Carlo Tree Search player for the game of Castle (AKA Shithead, Shed, Palace...). I have set this MCTS player to play against a basic rule based AI for ~30000 games and collected ~...
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What is the difference between using dense layers vs convolutional layers when dealing with images/image data?

I'm working on building a GAN and wondering what is the difference between using dense layers vs convolutional layers in my networks.
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1answer
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Alternatives to U-Net for biomedical image segmentation

Soon I will be working on biomedical image segmentation (microscopy images). There will be a small amount of data (a few dozens at best). My question is really straightforward: is there a neural ...
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tf.data.Dataset: The `batch_size` argument must not be specified for the given input type [migrated]

I'm using Talos and Google colab TPU to run hyperparameter tuning of a Keras model. Note that I'm using Tensorflow 1.15.0 and Keras 2.2.4-tf. ...
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1answer
50 views

Is my backpropagation code correct? [closed]

I am trying to implement the back-propagation algorithm for the following neural network. ...
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What does it mean to have epochs=30 in Keras' fit method given certain data?

I have read a lot of information about several notions, like batch_size, epochs, iterations, but because of explanation was without numerical examples and I am not native speaker, I have some kind of ...
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Do I have to downsample the input and upsample the output of the neural network when implementing the NICE algorithm?

Consider that my input is an RGB image. The size of my image is $N\times N$. I'm trying to implement NICE algorithm presented by Dinh. The bijective function $f: \mathbb{R}^d \to \mathbb{R}^d$ maps $X$...
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Atari Breakout Infrastructure

This is how they describe their infrastructure in https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf. I want to implement the game of Atari Breakout. ...
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What are examples of commonly used feature and readout maps?

It is well-known that deep feedforward networks can approximate any continuous function from $\mathbb{R}^k$ to $\mathbb{R}^l$, (uniformly on compacts). However, in practice feature maps are ...
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Are there examples of neural networks (used for control) implemented on a FPGA or on a neurochip?

Greetings to all respected colleagues! I want to consult on the use of FPGAs and neurochips. I plan to use it in my laboratory project for programming control systems on neural networks. In my work,...
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How to estimate the accuracy upper limit of any CNN model over a computer vision classification task

We are given a computer vision classification task, that is, a task that asks us to predict the category of an image over $n$ predefined classes (the so-called closed set classification problem). ...
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Interpreting I/O Transformation Matrix in Convolution

I've been reading this article on convolutional neural networks (I'm a beginner) - and I'm stuck at a point. What I understand: We have a 4x4 input, and want to transform it to a 2x2 grid. I'm ...
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Why do DeconvNet use ReLU in the backward pass?

Why does DeconvNet (Zeiler, 2014) use ReLU in the backward pass (after unpooling)? Are not the feature maps values already positive due to the ReLU in the forward pass? So, why do the authors apply ...
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How can I process neural network with 25000 input nodes?

I'm trying to build a neural network between protein sequence and its drug fingerprint. My input size is 20000. The output size is 881. The sample size is 610. Can I process this huge neural network? ...
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How do we minimize loss for a single neuron with a feedback?

Suppose we had a series of single-dimensional data points $X = \{x_1, x_2, \dots, x_n \}$, where $n$ is the number of data points and there corresponding output values $T = \{t_1, t_2, \dots, t_n \}$. ...
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Is ReLU a non-linear activation function?

According to this blog post The purpose of an activation function is to add some kind of non-linear property to the function The sigmoid is typically used as an activation function of a unit of a ...
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1answer
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Is text preprocessing really all that necessary for NLP?

As a first step in many NLP courses, we learn about text preprocessing. The steps include lemmatization, removal of rare words, correcting typos etc. But I am not so sure about the actual ...
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1answer
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How should I deal with variable-length inputs for neural networks?

I am a very beginner in the field of AI. I am basically a Pharma Professional without much coding experience. I use GUI based tools for the neural network. I am trying to develop an ANN that receives ...
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How do weights changes handles during back-propagation when there are unknown labels

I have a question about how weights are updated during back-propagation for some of my samples that have unknown labels (please note, unknown, not missing). The reason they are unknown is because this ...
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Are No Free Lunch theorem and Universal Approximation theorem contradictory in the context of neural networks?

To my understanding NFL states that, we cannot have an hypothesis (let's assume it is an approximator like NN in this case) class that can't achieve certain accuracy parameters $\leq \epsilon$ with ...
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Is there any way of generating fixed-length sequences with RNNs?

Is there any way of generating fixed-length sequences with RNNs? I want to tell my character level RNN to generate a name of length 3, 4, 5 and so on. I haven't found anything online like this, but my ...
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1answer
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Can neurons in MLP and filters in CNN be compared?

I know they are not the same in working, but an input layer sends the input to x neurons with a set of weights, based of these weights and the activation layer, it produces an output that can be fed ...
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What could cause a big fluctuation of the loss in the last epochs of training an AlexNet?

I am training an AlexNet neural network, with about 12000 images which 80% is for training, 10% is for validation and another 10% is for testing. I have a problem in my plots. There is a big ...
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Non-linear regression with a neural network

I have to perform a regression on three curves as shown in the following plot. Here, accA (y-axis) is the dependent variable, and ...
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How can I implement a simple memory augmented neural network as provided in this PyTorch implementation with Tensorflow 2 and Keras? [migrated]

I found this repo (https://github.com/donggong1/memae-anomaly-detection) with a PyTorch implementation of a simple memory for a neural network. I think that this memory can be really easily ...
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How can I implement a custom loss function that takes into account multiple predictions of the network? [migrated]

I am currently implementing a CNN with a custom error function. The problem I am trying to solve is physics-based, so I can calculate the maximal achievable precision, or to put it another way, I know ...
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Which one is better: multivariate regression with basis expansion or neural networks?

Assume we are given a training dataset $D = \{ (x_i, y_i)\}_{i=1}^{N}$. My question is: which is better? A multivariate regression with basis expansion with independent matrix $X$ and dependent ...
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1answer
41 views

Simple three layer neural network with backpropagation is not approximating tanh function

I have this simple neural network in Python which I'm trying to use to aproximation tanh function. As inputs I have x - inputs to the function, and as outputs I want tanh(x) = y. I'm using sigmoid ...
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1answer
436 views

Can neural networks with a sigmoid as the activation function of the output layer approximate continuous functions?

Neural networks are commonly used for classification tasks, in fact from this post it seems like that's where they shine brightest. However, when we want to classify using neural networks, we often ...
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Why is the loss associated with my neural network increasing?

I am currently learning neural networks. Using data from http://www.mariofrank.net/touchalytics/index.html, I am trying to predict "User ID" by training the neural network model shown below. However, ...
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1answer
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How should the neural network deal with unexpected inputs?

I recently wrote an application using a deep learning model designed to classify inputs. There are plenty of examples of this using images of irises, cats, and other objects. If I trained a data ...
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1answer
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How can I learn a graph given nodes with features in a supervised fashion?

I have a dataset and want to be able to construct a graph from it in a supervised fashion. Let's assume I have a dataset with N nodes, each node has e.g. 10 features. Out of these N nodes, I want to ...
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How can I get to a final output of shape $224 \times 224$, without FC layers, from a tensor of specific shape, in OpenPose?

I am approaching the implementation of the OpenPose algorithm for realtime human body pose estimation. According to the official paper OpenPose: Realtime Multi-Person 2D Pose Estimation using Part ...
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1answer
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How to perform insect classification given two images of the same insect?

I'm relatively new to image classification. Currently, I am trying to classify insect images, using a convolutional neural network (CNN). When I ask a human expert to identify an insect, I usually ...
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Error when checking input: expected dense_203_input to have shape (1202,) but got array with shape (1,) [migrated]

I've made a very simple neural network, which is meant to do reinforcement learning. However, I cannot predict anything as I get an error when trying to predict. Error in question: Error when ...
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Why does model complexity increase my validation score by a lot?

I learned that when creating neural networks the go to was to overfit and then to regularize. However I am now in a situation where, when I make the model more complex (more layers, more filters, ...) ...
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Are there principled ways of tuning a neural network in case of overfitting and underfitting?

Whenever I tune my neural network, I usually take the common approach of defining some layers with some neurons. If it overfits, I reduce the layers, neurons, add dropout, utilize regularisation. ...
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0answers
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Can the addition of low-quality images to the training dataset increase the network performance?

I already trained a deep neural network called YOLO (You Only Look Once) with high-quality images (1920 by 1080 pixels) for a detection task. The result for mAP and IOU were 93% and 89% respectively. ...
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1answer
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In a single neuron output layer should the output be a scalar?

Given a neural network with 3 inputs, 4 hidden layers, and 1 output, should the output neuron be a vector or a scalar? I thought that at the end of the summation only one number between 0 and 1 would ...
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Neural network to extract correlated columns

I want to use a neural network to find correlated columns in a .csv file and give them as a output. The input .csv file has ...
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2answers
159 views

How to calculate the number of parameters of a convolutional layer?

I was recently asked at an interview to calculate the number of parameters for a convolutional layer. I am deeply ashamed to admit I didn't know how to do that, even though I've been working and using ...
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34 views

Recent algorithms for correcting mislabeled data using multilayer perceptrons

I am doing literature research on algorithms for correcting mislabeled data using multilayer perceptrons. Found an "old" paper An algorithm for correcting mislabeled data (2001) by Xinchuan Zeng et al....
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1answer
50 views

Are the labels updated during training in the algorithm presented in “An algorithm for correcting mislabeled data”?

I am trying to understand an algorithm for correcting mislabeled data in the paper An algorithm for correcting mislabeled data (2001) by Xinchuan Zeng et al. The authors are suggesting to update the ...
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2answers
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Why are reinforcement learning methods sample inefficient?

Reinforcement learning methods are considered to be extremely sample ineffcient. For example, in a recent Deepmind paper by Hessel et al, they showed that in order to reach human level performance on ...
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1answer
34 views

How should I deal with variable input sizes for a neural network classifier?

I am currently working on a project, where I have a sensor in a shoe that records the $X, Y, Z$ axes, from an acceleration and gyroscope sensor. Every millisecond, I get 6 data points. Now, the goal ...
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0answers
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Can denoising auto-encoders be convolutional and fully connected?

I have been reading lately on autoencoders a lot. I just wanted to summarize my understanding of denoising autoencoders. As far as I understand they can be Fully connected (in which case, they will ...
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I created a snake game and fitted the NEAT algorithm and there's issues

Below are my Inputs Outputs and fitness function. The snake is learning at a slow rate, and seems to be stagnant, additionally when the snake collides with the food, it gets deleted from the genome, ...
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1answer
41 views

Medical diagnosis systems based on artificial neural networks

Are there any medical diagnosis systems that are already used somewhere that are based on artificial neural networks?

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