Questions tagged [conv-neural-network]

Convolutional Neural Networks are a type of neural network in which only subsets of possible connections between layers exist to create overlapping regions. They are commonly used for visual tasks.

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spatiotemporal/geophysical forecasting

I am wondering what models to use for geophysical forecasting? I am looking at historical sea surface temperatures over the globe with one datapoint per month, so an input of (lat, lon, # of months) ...
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In the case of YOLO, how does the network assign a box in it's grid based on the midpoint of the object?

My question is that how in YOLO, the networks does the midpoint of grid cell think ? I'm not completely sure I understand it. How can we know the midpoint of any object before actually knowing where ...
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Did I go wrong somewhere with my ANN?

Data for this is available here: https://tmpfiles.org/download/51656/Data.csv This is my ANN model: ...
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Is it possible and if so how to build a neural network such that it doesn't backpropagate in certain regions of the NN?

I had an idea to improve a neural network I'm currently using, but I'm quite new to machine learning so I don't know if it's possible to implement or how difficult it is or simply if isn't worth. The ...
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Validation Error less than training error? custom metrics affected from droputs?

I have a neuronal network trained on some data. My testing loss is less than my training loss. As this question is well answered regarding some points here I ask myself, if a custom metric that is ...
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Can we express CNNs in terms of a MLP?

I have been wondering whether a convolution can be represented in terms of an MLP. We can say that in convolution we have shared parameters between different neurons. But how to express this ...
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ML topology wrong prediction on Japan Crossword puzzle

I’m trying to study machine learning in hands-on way. I found exercise for myself to create neural network that solves “Japan crosswords” for fixed size images (128*128). Very simple example (4*4) ...
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Components and Purposes of CNN Architecture

I am trying to learn about Convolutional Neural Networks. I have begun to study the components of the architecture and I think I have a good idea of the primary components that make up most ...
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Input gradient in convolutional backpropagation

(Note: this is distinct from my previous question) My 2D convolution is defined as follows: $$y_{i,j} = \sum_{m=0}^{f_w-1}\sum_{n=0}^{f_h-1}x_{si+m,sj+n}f_{m, n}$$ where $s$ is the stride (this ...
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Relationship between cross entropy and focal loss function

If the multiple categorical cross entropy loss function is given by $$ L_{CE} = -\frac{1}{N}\sum_{i=1}^N\sum_{k=1}^K X_{ik}log(Y_{ik})$$ where $X$ is the labelled data and $Y$ is model ...
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Light direction classifier

I have a dataset with light coming from 8 different directions, evenly distributed around the target object. I am looking into designing a classifier for determining the light direction. I tried ...
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If features are always positives why do we use RELU activation functions?

Sorry I'm a beginer. I understand the nature of non-linear vs linear activation functions, I know RELU basically filter the negatives inputs and only respond to the positive, but When does it happen ...
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Validation loss diverging when training a simple CNN for text classification

I'm training a CNN for text classification on the IMDb movie reviews dataset. The dataset contains 25000 training and 25000 testing samples of movie reviews, each half positive and half negative. The ...
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When designing a convolutional neural network, what do you actually have to calculate?

One thing that confuses me about CNN is that I cannot tell when something is designed based on calculation versus when something is (arbitrary) design choice (no need for calculation). My question ...
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Gradient in Convolutional Layer

I have a convolutional neural network that operates on $4$-tensors. I'm trying to calculate the gradient w.r.t. the 4-dimensional filter, i.e. $\frac{\partial E}{\partial f}$, given the gradient w.r.t....
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Machine Learning: Why do I have this pattern of train and validation accuracy?

I am trying to understand what would generate this pattern of accuracy in train and validation dataset (second and third plot below). I am training a network to recognize 6 types of faces (they are ...
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1answer
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How to understand network neural network architecture from a research paper

Hello everyone I have the following architecture from the DELP-DAR research paper (https://www.sciencedirect.com/science/article/pii/S0167865519303216) and I dont really understand two things, first ...
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Binary classification using texture image

I'm trying to classify gender (male or female) from writer's handwriting, I used as input to CNN a texture image of size 100*100px. I generate this texture blocks images from writer's handwiting. I ...
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why does my model fluctuate on validation set and is smooth on the training set?

I use the below architecture in keras for dog-vs-cat dataset ...
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Backpropagation through time for RNN: how to deal with recursively defined gradient updates?

A simplified RNN architecture basically involves the following update \begin{equation} \begin{cases} h_t & = \phi(w h_{t-1} + v x_t )\\ \hat y_t & = \theta(h_t ) \end{cases} \end{...
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Is there a way to figure out using filter sizes (manually) how many operations Batchnorm, Conv, and Relu layers take during backprop? [duplicate]

I'm working with a basic resnet model. I want to understand how to compute by-hand the number of ops for a specific layer during backward pass (backprop) during Training (not inference). This involves ...
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1answer
121 views

Why is my DQN agent not learning to eat its food? (Simple snake game)

I am trying to solve the snake game below with a DQN agent. Actually I was originally trying to solve a much larger grid problem, however have made it simple to essentially an 8x8 RGB state ...
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Algorithms for sonar segmentation

I am looking at trying to create an autonomous vehicle that relies on sonar for awareness. I’ve got a rig fitted out with some servos and ultrasonic sensors. Now I need to try and devise an algorithm ...
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How to manually compute number of ops in backward pass of a CNN? [duplicate]

I've been trying to figure out how to compute the number of Flops in backward pass of ResNet. For forward pass, it seems straightforward: apply the conv filters to the input for each layer. But how ...
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When training a Neural network, how important it is to finish a training such that the epoch is an integer?

When training an object detection model, I am wondering how important it is to choose the number of iterations such that the training completes "full" epoch? Here is an example that, I hope, will make ...
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Combine ReLU with TanH is a good idea?

I have a CNN implementation for the Generator of a GAN, internally, the architecture is using ReLU for non-linearities, but at the output, the paper of the architecture specifies Tanh must be used. ...
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BackPropagation and Flatten layer in CNN

everybody. I'm trying to create CNN(Convolutional Neural Network) without frameworks(such as PyTorch,TensorFlow,Keras and so on) on Python. Who don't know or forgot what is exactly CNN is: To ...
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Are neural networks smart enough to overcome unbalanced data sets?

From what I have read, many models have issues with unbalanced data sets in classification problems. Are neural networks smart enough to overcome this flaw or should I still look into creating a ...
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19 views

How to calculate number-of-ops used in the backward pass of the neural net in training phase?

I'm trying to study a basic model like ResNet and how many operations it does and memory usage during backward-pass. For forward pass for layer like 1x1 conv or 3x3 conv, i was able to easily compute ...
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1answer
19 views

Is there a ML model that can determine the sequence that leads to an outcome?

I have a problem where a certain sequence of events leads to a certain outcome. The problem is, I am not sure exactly what the sequence is. I am hoping to get some possible sequences to test as ...
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Bottleneck block in pytorch ResNet

I was trying to understand the output the pytorch resnet model and can't seem to figure out the following issue with what printing the model shows. Why is the following only there in Bottleneck-0 and ...
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1answer
58 views

Use CNN to forecast time series value accuracy problem [closed]

I would like to use a CNN to predict a value based on some historical data. The concept is easy: I have a numerical value (label) the depends on some other numerical values (features). Each set of ...
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Selecting training images for object verification(siamese network), different number of examples per object

I'm trying to build a model for object verification (my first not tutorial-guided project of this kind). I saw an approach using a siamese network in the coursera deep learning course by Andrew Ng. ...
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Feed Forward vs Convolutional Neural Network on Periodic function

This question is related to a homework assignment, and I am trying to get a more broad view on the question. At first glance, I find this question a little strange and I'm wondering if there is some ...
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How to estimate the convolutional representation of a graph from its similarity to other graph convolutional representation?

Suppose we have two graphs A and B disconnected to each other (let's say 2-hops each), within a larger graph. If the Convolutional representation of graph A is known, is it possible to estimate the ...
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1answer
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Finding patterns in binary files using deep learning

I am a newbie in deep learning and wanted to know if the problem I have at hand is a suitable fit for deep learning algorithms. I have thousands of fragments each of about 1000 bytes size (i.e. ...
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1answer
23 views

How is the network connected in the following introductory tutorial of Pytorch?

I am going over the tutorial by Pytorch Here, they initialize a random input matrix $$x \in \mathbb{R}^{64 \times 1000}$$ which I am assuming each row of this matrix represents a $1 \times 1000$ ...
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50 views

what is the best activation function for binary classification?

i'm beginner in cnn and i want to detect which one is genuine image and which one is spoof image. i got really confused to choose my activation function. for binary classifiers, should i choose ...
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How does applying a 1-by-1 convolution (bottleneck layer) between conv. layers change the output?

A 1-by-1 convolutional layer can (e.g.) be used to reduce the number of operations between two conv. layers. Example: applying a $5 \times 5 \times 32$ conv. with same padding onto a $28 \times 28 \...
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Does feature detector(filter) has to be a sqaure matrix?

I am going through a course on Convolutional neural networks, where in the convolution step, the feature detector matrix was square shaped. Is there any mathematical significance that Feature ...
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Can a neural network whose output is uniformly equal to zero learn its way out of it?

I am performing a regression task on sparse images. The images are a result of a physical process with meaningful parameters (actually, they are a superposition of cone-like shapes), and I am trying ...
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Dealing with batch size and step size in 1D CNN

I have a batch generator which gives me data in the shape of (500, 1, 12) where (batch size, time steps, features). ...
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1answer
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Where is BatchNorm performed in ResNeXT https://github.com/facebookresearch/ResNeXt neural network?

In the original paper that described ResNeXT (variation of Resnet) at https://arxiv.org/pdf/1611.05431.pdf. On Page-5 top right column, it says: ReLU is performed right after eachBN, expect for ...
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What is time complexity big O for 2D filters and 1D filters in image convolution Neural networks.?

I went through this link to understand, but was not able to grasp the concept. What is the computational complexity of a 1D convolutional layer? Consider a more general case: ...
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14 views

3D image segmentation: 2D slice-wise vs full 3D model?

I need to segment a volume of $N\times N \times N$ pixels. I can do it in two ways, using a fully 3D convolutional neural network (e.g.: Conv3D in Keras), or I can segment $N$ 2D slices (e.g. Conv2D) ...
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Clustering documents of text sequences (not in plain English) using 1D CNN without pre-trained word embedding

I have a long sequence of hex (or integer) numbers, each of which corresponding to an event. There are thousands of events per document, and I have several hundreds of documents. I’d like to do the ...
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Interpretation of Tensor Flow CNN results with big dips in accuracy while training

I am trying to classify images using a CNN in tensor flow. I am doing 10 fold cross validation. At each fold, the training set is 900+ images and the validation set is 100 images. It is only two ...
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2answers
31 views

Optimizing hyperparameters of network with extremely long training time

As an example, let's say i am using a very deep fully convolutional autoencoder to segment lung scans. Input image resolutions will be large, since the features i hope to segment (things like early ...
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1answer
67 views

Real noise modeling/ noise map generation (image processing, deep learning)

I am working on a project with really noisy images. I have trained a detector that can detect the characters but fails in some cases (noise is high). So far I have gone through many denoising, ...
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1answer
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how is this Bottleneck design the same as original residual block in resnet?

This paper/link talks about resnet's bottleneck design. It's totally not clear to me how the bottleneck design on the right is equivalent to the left-diagram and how is it reducing the parameters? ...

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