Questions tagged [neural-network]

Artificial neural networks (ANN), are composed of 'neurons' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network designer having had a model of a real system.

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Which activation function of the output layer and which loss function are advised to be used for bounded regression?

I want my (deep) neural network to produce an output from a certain range, in my case between 0 and 255. I have scaled the labels from [0..255] to [0..1]. For the neural network, I have tried a ...
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Artificially expanding the datasets through rotation of images in MNIST

I came across this question from the 3rd chapter of the book Neural Networks and Deep Learning by Michael Nielsen, this is a question given in his exercise. One way of expanding the MNIST training ...
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Why does the Neuralnet package predict constant values when a second hidden layer is added?

I'm trying to use a multi-layered neural network to predict concrete strength, using R's neuralnet package. Everything works great with a single layer, but when a second layer is added, the ...
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Efficient scanning method on images/volumes when applying neural network

I am a newbie in neural network. I am using this for one of my physics problems. So, please forgive my sheer lack of knowledge in this field. My neural network is a convolutional neural network with ...
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Boolean Classification of Binary Strings using Neural Networks

Suppose that I have a language $L\subseteq \{0,1\}^*$. For each $n\in \mathbb{N}$, let $D_n$ be a dataset containing pairs of the form $(x_i,b_i)$ where $x_i\in \{0,1\}^n$, $b_i\in \{0,1\}$, and $b_i =...
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Free dataset to train a neural network in order to extract text from image

I'm building a custom OCR to recognize and get text from png image. In order to dealing this task, i'm using python with tensorflow library (1.14.0) to develop a Convolutional Neural Network that ...
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What's wrong with my backpropagation through time (BTT) calculation or how to multiple a scaled vector and a matrix without matching dimensions?

I am trying to make a pretty simple RNN from scracth, using only Numpy library of Python. At this moment I am having troubles with BTT as I do not know how to proceed with situation when a ...
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Linear and logistic regression output with same neural network

it seems like this should be a very common task, but I have not found anything useful on my research: How can I do linear and logistic regression with the same neural network? By example, what I mean ...
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Is Difference Transformation required for LSTM?

I am Working on uni variate multiple step LSTM sequence prediction .My LSTM model is failing to give a good prediction on My Data.From some online blogs I saw that Difference Transform may reduce Data ...
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Creating ANN's (LSTM) for multiple datasets

I have three datasets, each representing time-series water quality data from three different regions (upper, middle, lower regions) of the same geographic area. I want to create different types of ...
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Do I need to adjust frequencies or weights of rows so the right weight is given to each sample (data mining)?

The general problem type is as follows. I have about 2,500 rows of data. Each row contains data about an individual sample with sizes from around 10,000 to 200,000 (a known attribute / column), and ...
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NEAT keeps telling me “Exception: No such config file:”

I am trying to make the code from this tutorial run on my computer: https://github.com/Vedant-Gupta523/sonicNEAT I have an exact copy of the config-feedforward file in a .txt document on my desktop. ...
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What type of Machine Learning to use?

I have a beam loaded with random loads, for each timestep I obtain accelerations and maximum moments on the beam. My prediction should receive a timeseries of accelerometer data and output the ...
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computer science student - asking for some machine learning guiding (voice cloning)

So I have chosen my synopsis topic for my second last semester. I want to make a text-to-speech program, that speaks with the voice of a game character. I have worked with machine learning in my ...
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Network Size in neural network

What is the danger to having too many hidden units in your network?will it take more memory,and does it take longer to train.
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Gradient for starting Backpropagation

I was reading this nice tutorial about Pytorch's basics: https://pytorch.org/tutorials/beginner/pytorch_with_examples.html In the first example (pure Numpy), the author starts the backward phase by ...
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I want to use a neural network for a control block in MATLAB, but without Simulink

I have a physical model that produces an error value after running, which I want to minimize by a Neural Network. I can not find a MATLAB function however that lets me run the program and use the RMSE ...
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Is there a way to attach a VM instance from Azure as compute power to train your model locally?

I have a python folder locally which does the training of the model. I am wondering, is there a way, for example to bring some GPUs locally and just "attach them" to your script or do you need to do ...
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Predictions of a Deep Learning Model

I’ve built a model that classifies digits from 0-9. My dataset is tf.keras.datasets.mnist. I use softmax as the activation function for the output layer. Q1: The output layer should consist of 10 ...
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Is there a way to train pre-trained speech recognition models?

I want to use a pre-trained speech to text recognition model and train the model for audios that are conducted by the always same person having some dialect. If I correct the words in the transcript, ...
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Combining convolution operations

Reading an article about 1x1 convolution, I found this: It should be noted that a two step convolution operation can always be combined into one, but in this case [GoogLeNet] and in most other deep ...
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Should you perform feature scaling/mean normalization when predicting with a model? [duplicate]

Should you also perform feature scaling/mean normalization when predicting with a model, that was trained and tested on with feature scaling/mean normalization?
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Where does C++ come into play for Machine Learning?

As a newbie, I am wondering where exactly is C++ used for exactly in Machine Learning and its subspaces? I know that libraries like TensorFlow, Pytorch and others enable you to do most of the work in ...
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Validation loss is remain same even-though training loss is decreasing

My model Validation loss is remain same even-though training loss is decreasing.I understood it is a case of Over fitting So, I applied Regularization methods like Dropout and L2 regularization. But ...
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What is a TensorSpec? (Tensorflow 2.0)

what is a tf.TensorSpec() and what is it used for?
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Multitask learning model with variable number of tasks

This is my basic multitask learning model, and it has 2 tasks. Since there are only 2 tasks, maybe I can duplicate the code for each task as self.tower1 and self.tower2. ...
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Coding backpropagation in python

I understand maths better than Python but I would like to progress in Python. It would be amazing if someone would like to talk about it with me. I have a feedforward neuron network with only one ...
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Can we compute the jacobian for a CNN? [closed]

for each layer in the CNN can we compute jacobian?
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How do I force my neural network to train on GPU instead of CPU? [closed]

My neural network algorithms have been running on CPU instead of GPU. I have checked online for answers and I was fortunate to get some here. However, the script on this webpage did not run on my ...
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low error, high CV(RMSE)?

I am comparing 2 neural network models. I have used the model to make predictions on unseen data. One model returns an error of 20.9% for y1, 36.6% for y2, 4.53% for y3 on unseen data, and a CV(RMSE)...
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Why GPU doesn't utilise System memory?

I have noticed that more often when training huge Deep Learning models on consumer GPUs (like GTX 1050ti) The network often doesn't work. The reason is that the GPU just doesn't have enough ...
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Does save_best_only in Keras prevents overfitting?

I'm training a CNN and using: model_checkpoint = ModelCheckpoint(os.path.join(output_artifacts,'weights.h5'), monitor='val_acc', save_best_only=True) I trained ...
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the size of training data set in the context of computer vision

Generally speaking, for training a machine learning model, the size of training data set should be bigger than the number of predictors. For a neural network, or even a deep learning model, the number ...
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Keras CNN model gives no gradients error during training

I’m trying to create a Convolutional Neural Network model, using an 824 image dataset, for predicting an output value. Problem is that the dataset is quite unstructured, as there are plenty of RGB and ...
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Traditional ML Model or Deep Learning for ~200-300 samples?

Good morning all! I'm working on a resume parser that is integrated with an RPA (robotic process automation) platform. The robot has OCR to extract text from a PDF resume, and it supplies the ...
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Keras loaded model output is different from the training model output

When I train my model it has a two-dimension output - it is (none, 1) - corresponding to the time series I'm trying to predict. But whenever I load the saved model in order to make predictions, it has ...
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Understanding depthwise convolution vs convolution with group parameters in pytorch

So in the mobilenet-v1 network, depthwise conv layers are used. And I understand that as follows. For a input feature map of (C_in, F_in, F_in), we take only 1 ...
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How to train a Model which can check whether image is from existing classes or not

I have given with a image dataset of 1000 classes , each class has 100 images. Now My requirement is to train a model which will take a image as input, and it should answer whether the image is ...
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1answer
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Is Neural Network Architecture independent of Data?

If I change my dataset (let's say it is always images), should I change the architecture of my neural network?
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In CNN, how the weights are retained for filters for a particular class [closed]

I am new to CNN, What I have learned so far about the filters is that when we are giving a training example to our model, our model updates the weights by gradient descent to minimize the loss ...
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how to get the classe names in an image classifer after predection?

I made an image classifier of 80 classes of handwritten numbers then I tested my model and it worked pretty fine, the only problem that I have now is the display of the correct names of these classes. ...
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32 views

Writing a piecewise linear function as a sum of ReLU functions

Suppose I have a piecewise linear function $f(x) = \sum^n_{i=1}a_i\phi_i(x)$, where $\{\phi_i\}_{i=1}^n$ is a finite dimension space of dimension $n-1$, in particular I am interested in the functions ...
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Time Series prediction using R

I have a dataset which contains data related to the exchange rate in a certain time period(2013-2015). The dataset has a column date with YYYY/MM/DD format and USD/EUR which contains the exchange rate....
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If mean absolute loss is not differentiable, how it can be used in neural networks? which majorly are trained using back-propagation

If Mean Absolute Error (MAE) loss is not differentiable, how can it be used in neural networks? which majorly are trained using back-propagation I am wondering if MAE is not differentiable how they ...
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1answer
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A Loss of 55.2164 with a sparse_categorical_crossentropy in a sequential neural network?

I'm following Aurélion Géron's book on Machine Learning. The following code tries to evaluate a neural network with a sparse categorical cross entropy loss function, on the Fashion Mnist data set. ...
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1answer
15 views

Convolutional neural network block notation

The paper by He et al. "Deep Residual Learning for Image Recognition" illustrates their residual network in Figure 3 as follows: I am not a neural network expert, so could somebody please explain to ...
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Recursive Transfer Learning

Is there any methodology called Recursive Transfer Learning? For example, let's consider a situation that we have a lack of data while training a convolution neural network (CNN) for object detection ...
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LSTM features classification output

I am very new at this, so I might be wrong about my choice of model, but my problem is the following. I am trying to generate music, hence the reason I am using an LSTM. I have the following sequence ...
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How can I change the loss function when the shape of my data changes?

Since my data is too large, I use pd.read_csv('',chunksize=). I am using categorical_crossentropy as my loss function, however, ...
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14 views

Training Models with 1 Hidden Layer

for a multi-layer neural network is stochastic gradient descent(SGD) guaranteed to reach a global optimum?

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