Questions tagged [neural]

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Does Multicollinearity affect Neural Networks?

Can someone explain to me like I'm five on why multicollinearity does not affect neural networks? I've done some research and neural networks are basically linear functions being stacked with ...
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1 vote
0 answers
185 views

LSTM with variable time steps

I'm reading this post that describes how to train LSTMs with variable time step lengths. But does that have repercussions? Should I preprocess the time series in to varying permutations? e.g. Should ...
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-1 votes
2 answers
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Optimisation of neural networks

Do neural networks get optimized by trial and error, by data scientists, or is there some way of optimizing values through accurate mathematical equations?
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2 votes
2 answers
2k views

Metric MAP@k for what

What is the MAP@K metric for? What are you measuring? And where does it make sense to use it? Unfortunately, I can't find much about this on the Internet. Could ...
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4 votes
1 answer
79 views

What ML architecture fits fixed length signal regression?

My problem is of regression type - How to estimate a fish weight using a fixed-length signal (80 data points) of the change in resistance when the fish swim through a gate with electrodes (basically 4 ...
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1 answer
104 views

weight sharing among neurons at same depth

I'm trying to understand some visual illustrations about the wight sharing in the Convolutional Neural Network as following: In this picture we see that for different outputs different inputs share ...
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  • 157
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0 answers
59 views

Regarding Keras mean_squared_error losses

I am trying to do a RandomizedSearchCV for a simple regression task. Essentially two inputs to one output. Upon inspecting the resulting models, it appears that the 'best' model has a ...
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  • 109
1 vote
1 answer
65 views

Can anyone verify my NN diagram if it is properly drawn?

I am working on a Neural Network that can estimate building's carbon footprint based on the set of features and an image of urban surroundings (via CNN). I have used Netron to visualize the network (...
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1 vote
2 answers
95 views

Network Size in neural network

What are the limitations of having too many hidden units in the Neural Network Does it take more memory or takes longer time to train the model
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2 votes
2 answers
443 views

How to interpret predicted data from a keras model

I tried building a keras model to classify leaves from the leaf classification dataset on kaggle. After i compiled and trained the model, i used it to predict the name of the leaves in the testing ...
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1 vote
1 answer
405 views

Counting Number of Parameters in Neural Networks [closed]

Note: This is an academics based problem. So in a recent in-class quiz, we were asked that if we have an input layer consisting of 20 nodes along with 2 hidden layers (one of size 10 and the other of ...
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  • 181
0 votes
1 answer
132 views

images from training set are different from images of test set

I am doing image classification with CNN and I have a training set and a test set with different distributions. To try to overcome this problem I am thinking about doing a standardization using ...
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1 vote
1 answer
641 views

Relationship between Sigmoid and Gaussing Distribution

I was reading this article where I came across the following statement in the context of "Why do we use sigmoid activation function in Neural Nets?": The assumption of a dependent variable to follow ...
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1 vote
1 answer
731 views

Data Augmentation Multi Outputs

This question is asked several times here on SE, but I havent been able to find the right answer. I'm trying to build a network with 1 input and 2 outputs. I don't have a lot of data so I would like ...
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1 answer
102 views

why do we operate with graphical models in VAE, if there are no probabilites involved?

In the variational autoencoder, I often see graphical models e.g. $P(X|Z)$ for the decoder, but when I looked at code, I don't see any random variables, I see just deterministic network, with ...
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  • 165
1 vote
1 answer
14k views

Keras error "Failed to find data adapter that can handle input" while trying to train a model

I've been following a tutorial on training a model and I've stumbled across an error that I've been struggling to find a solution for. The code for the model training is bellow: ...
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1 vote
1 answer
42 views

Application of Machine learning in modelling experimental system as a PhD topic

I am working as a control Engineer in a high energy physics lab. I am looking for a PhD thesis topic. Our experimental system is highly non linear and can't be modeled using standard white box ...
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1 vote
0 answers
145 views

Serious doubts on Categorical embedding

I am still having many doubts about the working of categorical embedding. In particular I have 2 points not clear: 1. Are 1-Hot variables converted to a lower dim vector? 2. What target are neural ...
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  • 531
2 votes
0 answers
3k views

Binary Classification of Numeric Sequences with Keras and LSTMs

I'm attempting to use a sequence of numbers (of fixed length) in order to predict a binary output (either 1 or 0) using Keras and a recurrent neural network. Each training example/sequence has 10 ...
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0 votes
1 answer
808 views

ValueError: Error when checking input: expected conv2d_13_input to have shape (3, 150, 150) but got array with shape (150, 150, 3)

I am trying to train the model, I keep ending up with this ValueError: ValueError: Error when checking input: expected conv2d_13_input to have shape (3, 150, 150) but got array with shape (150, 150,...
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1 vote
1 answer
123 views

Transfer learning - small database

I am trying to use transfer learning in medical (ultrasound pictures). The problem is - I have very limited picture database = 400 (360+40). I am using resnet50 (I don't think this is important but ...
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0 votes
1 answer
33 views

Adapting Neural Network to new domain without labels

Is there an approach for the following problem: Lets say, I trained a neural network on a big dataset for categorizing different fruits in $k$ classes. Afterwards I got a nice model, which performs ...
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