Stack Exchange Network

Stack Exchange network consists of 174 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Visit Stack Exchange

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 ...

0
votes
1answer
7 views

Converting string_id to number_id

I have column with movie ids like this: tt0984332 tt0984332 tt0847742 ttnanana1 I need to convert in to numbers that can be inserted into neural network as features, like this: 0 0 1 2 How can I do ...
0
votes
1answer
10 views

Historical weather data with machine learning?

My company gave me a task to build some weather forecasting. I have now historical weather data for 10 years (temperature, precipitation in mm, humidity and etc. more than 30 features total). We need ...
0
votes
1answer
7 views

Number of parameters keras dense layer with a 2D input

I am using 2D data in a classification problem using keras. So I am defining a keras model as following: ...
0
votes
0answers
6 views

YOLO algorithm - understanding training data

I am taking "Convolutional Neural Networks" on Coursera and it is taught by Andrew Ng. I am in week 3 and confused about YOLO algorithm. I checked the course forums on coursera but I am still not ...
0
votes
0answers
5 views

With two deep learning models, how do I perform Bayesian Model Averaging for better prediction on a test set?

Given two deep learning models that can predict on a test set, what I want to do is use BMA (Bayesian Model Averaging) to average the models to better predict? What exactly is the procedure for this?...
0
votes
0answers
5 views

Refactor tf.while loop Tensorflow

Thanks in advance for considering my question. TL;DR: I have a while loop in tensorflow, that I think is causing extreme slowness during training, and would need to refactor. Much appreciated if any ...
1
vote
0answers
16 views

AttributeError: module 'torch.distributed' has no attribute 'init_process_group' [on hold]

AttributeError: module 'torch.distributed' has no attribute 'init_process_group' Still getting this error after installing latest pytorch-nightly and other stuff, when I tried to run the imagenet ...
0
votes
0answers
8 views

Hierarchical multi classification using Neural networks

I have a data set(non text) of 40 features which has 4 main classes and 2 sub classes. Assume, the 4 classes are AB, C, D and E. AB is further divided into class A and class B as sub classes. I want ...
1
vote
1answer
11 views

RBF neural network python library/implementation

I want to use a Radial Basis Function Neural Network for my thesis. Is there any library that implements it? And in the negative case, which is the best library to implement it?
0
votes
0answers
12 views

What is pre processing and what's the best pre processing for Speech recognition with CNN? [on hold]

I'm new to NN, and i know the basic of CNN, but idk what's pre processing method to use for speech recognition.
0
votes
1answer
11 views

Why can't the XOR linear inseparability problem be solved with one perceptron - like this?

Consider a perceptron where $w_0=1$ and $w_1=1$: Now, say we use an activation function $f(x)=1,~for~x=1$$~~~~~~~~~~~~~0, otherwise$ The output is then summarised as: $x_0~~~~~x_1~~~~~w_0*x_0 + ...
0
votes
0answers
8 views

Memory Neural Network to convert binary vector to string

I am using a mapping function that transforms each word into a binary vector, for example: ...
0
votes
1answer
15 views

LSTM : multi-step multidimensional multivariate multi-site timeseries forecasting

I'm working on a project in which i'm trying to do a pollution forecasting. I googled around and found that LSTM is a good candidate for this task, however, I'm still struggling at how to adapt it to ...
-1
votes
0answers
12 views

4 Dimension Input to NN

I am writing a neural network that takes an input of (?, 5, 19, 400) where ? is the batch, then we have a beam of 5 programs, 19 tokens, and 400 features per token. The idea is to have the neural ...
0
votes
1answer
37 views

Possible reasons for word2vec learning context words as most similar rather than words in similar contexts

I am observing my word2vec model learning context words as most similar rather than words in similar contexts. I don't understand why it (word2vec in general, not my model in particular) can behave ...
-1
votes
0answers
12 views

autoencoder loss not decreasing for epoch beyond dimensionality of input

I'm using autoencoder to reduce 14 dimensions data. It's a simple one layer. I noticed that using Adam optimizer, the loss function (mse, mae, cosine, msle, kullback_leibler_divergence, poisson, ...
0
votes
0answers
8 views

Why do we use Bayesian Error Function in Neural Network when there is less data?

I have read somewhere, not able to recall it, that Bayesian error function is used when there is lack of data to feed into neural networks. But I didn't see any proper reason there. If anyone can ...
0
votes
0answers
6 views

Manual Input Increase Probability In Machine Learning

I have been building a neural network using Tensorflow to predict the location of flying object. I have been able to successfully run the data through and get a predicted output, but I want to include ...
2
votes
1answer
18 views

Structure the dataset for financial machine learning

I am trying to construct a dataset to apply MLP in forecasting financial returns. The main idea is that I want to predict future equity returns (1 month ahead, but the horizon can vary, just to give ...
1
vote
2answers
22 views

Continous bag of words claimed to be unsupervised, how is it working?

I'm following these two lectures on CBOW and skip-gram word2vec models. The first is lec 12 and the next lec 13 of a deep learning series https://www.youtube.com/watch?v=syWB-YMYZvI https://www....
0
votes
0answers
4 views

Studies about computer vision with highly similar images

I'm looking for studies, academic publications, papers, blog post, or anything that relates to use cases in which image recognition have been used with highly similar inputs. When using the word "...
0
votes
0answers
8 views

Time Series Prediction for non-uniformly varying dependent variables

I have a dataset with the following properties: DATETIME: range from "01.01.2014 01:00:00" to "12.31.2016 23:00:00" (index) Units: Category (#53) Technology: Category (#5) Capacity: Continuous value ...
1
vote
1answer
24 views

RNN package and problems with “Predictr”

I have two questions about how to use R's RNN package, specifically the trainr and predictr functions. Let's suppose I have a time series of 4000 steps for 5 different variables. How should this be ...
0
votes
0answers
12 views

variational autoencoders

If I understand correctly, kl divergence is relative entropy, which measures destination between two distributions. in vae we want to measure distribution over latent space matrix and standard normal ...
0
votes
0answers
8 views

Implementation of Inception-Resnet V2 shapes does not match

I am trying to implement the Inception-Resnet V2. In the original paper the authors outlined the network as in the figure below: One can say that the output dimension of the earlier block is the ...
0
votes
0answers
7 views

Always getting the same Q-max in Q-learning algorithm

I'm trying to implement a Q-learning neural network by following this article: https://www.practicalai.io/teaching-a-neural-network-to-play-a-game-with-q-learning/ using http://caza.la/synaptic/#/ and ...
0
votes
0answers
19 views

backpropagation problem from university of Toronto midterm

Here is the question: Consider a 1-layer neural net with three input units, 1 output unit, no hidden units and no bias terms. Suppose that the output unit uses a sigmoid activation function, i.e., y =...
0
votes
0answers
12 views

TimeDistributed Layers vs. ConvLSTM-2D

Could anyone explains for me the differences between Time-Distributed Layers (from Keras Wrapper) and ConvLSTM-2D (Convolutional LSTM), for purposes, usage, etc.?
0
votes
1answer
40 views

One Hot Encoding of Age

My task is to predict how many years a person has left to live using an MLP. There is one specific feature I'd like to discuss: current age. Statistically, it's a conditional probability. Example: ...
0
votes
0answers
8 views

Convert generator to DirectoryIterator in Keras

I created a multi input deep learning model, and a lot of functions I could not use it because the testgenerator (in the code) is a generator not a DirectoryIterator. ...
1
vote
0answers
11 views

Transformer architecture not working on toy problem

My transformer is not working on a toy problem. Toy problem Input : Sequence of random integer, one-hot-encoded. Example : ...
0
votes
1answer
28 views

How can I increase the number of iterations per epoch in MATLAB?

I am training a deep learning network using MATLAB and would like to increase the number of iterations per epoch. Using trainingOptions ...
0
votes
1answer
28 views

What should be the requirement for training data in order to obtain a good regression model using neural network?

I have made a neural network regression model using the theory for the first time and would like to clarify some basic doubts, whose concrete answers I couldn't find yet. Data:- I have 3000 samples ...
0
votes
1answer
19 views

How can I create convolutions or linear layers that operate on vectors rather than scalars in pytorch?

Consider an nn.Linear(2,3) layer transform like the one below. It uses a 2x3 matrix of scalar weights to create a weighted sum for each scalar element in the ...
1
vote
0answers
28 views

Programming a Neural Network in Python

In order to get some understanding of machine learning (i'm super new to this) I'm programming a neural network and try to train a sinefunction with it. The set-up is as follows: Backpropagation ...
0
votes
0answers
5 views

Training neural networks with distant data points?

I did some experiments on a single perceptron (using a SGD optimizer with lr = 0.1, a MSE loss function and 100 training epochs. The function for the perceptron should be y = 3x + 0): If the training ...
0
votes
0answers
9 views

Conditional DCGAN mystery collapses [closed]

I forked DCGAN and modified it to generate conditional celebA faces. Currently this is my implementation, but my network collapses every time. Here are my results after 20 epochs. Note that only the ...
0
votes
0answers
15 views

Back propagation algorithm producing incorrect gradient in python

I am trying to implement a back propagation algorithm in python but I am finding that when I check this gradient against an approximated gradient the calculated gradient is wrong. I also found that ...
1
vote
0answers
23 views

Input explanatory categorical variables along with time series into neural network

I want an advise on the ways to enter time series along with additional variables into convolutional neural network. Story first: I have a dataset of time series with daily energy consumption data (...
0
votes
2answers
33 views

Deep Learning Network decreasing in accuracy

In order to familiarize myself with semantic segmentation and convolutional neural networks I am going through this tutorial by MathWorks: Semantic Segmentation Using Deep Learning I did not use the ...
1
vote
1answer
34 views

Sparse_categorical_crossentropy vs categorical_crossentropy (keras, accuracy)

Which is better for accuracy or are they the same? Of course, if you use categorical_crossentropy you use one hot encoding, and if you use sparse_categorical_crossentropy you encode as normal integers....
0
votes
0answers
9 views

What kind of layer can do a channel number reduction?

I have a tensor of (1, 1, 1000, 64), i.e. a vector of 1x1000 with depth=64 channels. I'd like to transform this into a vector with a single channel (1, 1, 1000, 1): Using: a ...
0
votes
2answers
37 views

How to train LSTM with daily timeseries?

I have for each day sensor timeseries data. I just ask myself how to train with that a LSTM eg. for classification? Since I would like to have the LSTM train on all examples and not just one? I just ...
1
vote
0answers
26 views

Skip-thought models applied to phrases instead of sentences

My goal is to build a statistical model with domain specific phrase embeddings. To do this, I am doing research on how to build a model using skip-thought vectors, where instead of using sentence ...
2
votes
1answer
18 views

Batch normalization vs batch size

I have noticed that my performance of VGG 16 network gets better if I increase the batch size from $64$ to $256$. I have also observed that, using batch size $64$, ...
1
vote
1answer
28 views

Activation Functions

What is the purpose of linear activation functions in keras, isn't the entire point of activation functions to introduce non-linearity?
1
vote
2answers
26 views

Running an LSTM with Music Data

I'm working on a project for a class where I'm trying to create an algorithm that learns music and creates its own music. I'm having trouble on how to set up the data for it to be inputted into the ...
-1
votes
2answers
33 views

How good will a neural network perform on an unusual data? [closed]

I want to make a simulation based on neural network that will estimate the situation label(not a discrete value) based on state values. Suppose I have data with 40 features/columns and one ...
0
votes
0answers
8 views