Questions tagged [convolutional-neural-network]
A convolutional neural network is a form of neural network with an additional convolutional layer, typically used in image & audio analysis. The convolutional layer is essentially a filtering stage defined by the kernel which is used. For example, a convolutional layer could have a kernel which extracts edges from an image towards the goal of learning which objects are in a scene.
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Number of feature extraction layers in CNN
In a course I took about machine learning, we normally used about 2 feature extraction layers for image classification tasks, using MNIST or ...
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Is it bad to average several MAEs calculated from chunks of a big test dataset?
In my regression problem, I am using Mean Absolute Error (MAE) as a metric for my network. My test dataset is too big to fit in memory, so I am reading the test dataset in chunks and then Keras' ...
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Accuracy Drop in ViT with Patch Embedding: Investigating the Impact of Added Convolutional Layers
I'm currently working on incorporating a patch embedding layer into my Vision Transformer (ViT). I've defined this layer using four 2D convolutional and initialized it with a normal distribution. The ...
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Graph Clustering algorithms when both nodes *and* edges have features (numerical, categorical and potentially even temporal!)
I'm trying to figure out how much complexity I can get away with and am looking for model recommendations.
I have transactional data on hand - the features being customer id, customer balance, ...
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How to Findout if a neural network is invariant
In object recognition, translating an image by a few pixels in some direction
should not affect the category recognized. Suppose we consider images with an object in the
foreground on top of a uniform ...
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How can you add additional features/attributes while doing instance segmentation?
I want to do an instance segmentation of objects in images.
Usually I would stick to something like an Mask R CNN and let it run. However additionally to the image itself and the pre-labeled images, I ...
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Generator loss not decreasing while training GAN
I’ve been attempting to create a basic GAN to generate images using this database of flowers (https://www.robots.ox.ac.uk/~vgg/data/flowers/102/).
I’ve spent a few days on this, and largely based my ...
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Has someone designed a neural network which can select its own activation functions and/or have multiple activation functions in one model?
I'm wonder if there are any papers or implementations where a neural network has multiple activation functions in a single model (and layer), and preferably also where such activation functions ...
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Training ResNet50 model for binary classification
I want to use ResNet50 model to perform binary classification on a dataset spectrogram dataset. In order to do that I had to make a couple of modifications to the model's architecture:
Modified the ...
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Convolutional networks: remove useless features?
I'm new to convolutional neural networks and have two related questions:
If all the filters would have the same weights initially, they would all detect the very same feature - so it would be useless ...
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3D CNN accuracy is too low, how to improve it?
I have just started learning image processing and this is my first time working on video classification. I am trying to develop a model that recognizes hand gestures using the EgoGesture dataset(more ...
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Why do I keep on getting ResourceExhaustedError while training on video data using CONV3D on tensorflow?
I'm encountering a memory allocation problem while training a deep learning model on my computer, which has a Core i9 10th Gen CPU, 64 GB of RAM, and an NVIDIA GTX 1660 Super with 6GB of VRAM. Despite ...
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Image classification of centered objects with convolutional neural networks
Given that I have a set of images that contain multiple objects for which labels exist and the object the image label refers to is always in the center. The objects vary in size. I want to train a ...
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What are the general rules or principles for finding matrix operations that are used as filters in convolutional neural networks?
Is there a set of rules or guidelines for designing filters for convolutional neural networks? For example, a 3 x 3 layer with ones in the first column, zeroes in the second, and negative ones in the ...
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Need help designing conv-lstm in TensorFlow for longitudinal disease prediction
I am currently trying to develop a conv-lstm to predict disease progression in eye photos of patients. I have a folder of images with a total of 263 images of 144 different patients. I also have an ...
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Train CNN weights by using FFT - Reinforcement Learning?
Assume that you are doing convolution inside a CNN network, by using FFT because FFT is much way faster than using 4-5 for-loops etc.
But how should I train the weights if I know the output of my CNN ...
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Loading large raster dataset in to tensorflow
I am building a convolutional neural network for processing air quality concentration fields and meteorological parameter distribution. The input data are in Geotiff and NetCDF formats, which I load ...
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How do I create an Image Dataset for a CNN?
I'm currently working on assembling a CNN for image classification with tensorflow.keras. I have all my images in a file which I already uploaded to my program. Also I have CSV-Files for training and ...
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Dealing with noise in softmax output
I have a device with an accelerometer and gyroscope (6-axis). The device sends live raw telemetry data to the model 40 samples for each input, 6 values per sample (accelerometer xyz, gyroscope xyz). ...
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Shape of Flattened Layer in CNN
If I have a convolutional layer with dimension (5,5,4), (i.e, 4 no. of 5x5x1 feature maps), what will be the dimension of the flattened layer, if I apply flattening ...
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Why do I get such a low accuracy despite having good metrics?
I'm working on an image segmentation problem. I'm fine-tuning the network, and when displaying the metrics I obtain, I have some doubts.
I am going to provide some details about the network:
Solver: ...
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How to align the description of a convolutional neural network in keras with wikipedia's conceptual model?
I was going through the introductory guide to convolutional neural networks in tensor flow here
And I was trying to logically map some of the code I saw to my actual understanding of how convolutional ...
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What kind of learning do I need ? (use-case specific)
Consider a scenario where I have a model trained on gesture videos (say a 3D ResNet). I am looking for a technique (or a combination) that allows me to further train the model every time I have a new ...
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How to backpropagate transposed convolution with stride and padding
Please, help! I have deadlines and I do not have time to figure out the topic on my own.
And now about the problem. I'm currently trying to figure out back propagation in transposed convolution. I ...
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Does one need a understanding in Convolutional Neural Networks before learning about Quantum Convolutional Neural Networks
I have a degree in Physcis and want to learn about Quantum Convolutional Neural Networks.
Is it recommeneded that i known about Convolutional Neural Networks before learning about Quantum ...
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Conv1d() input and output dimensions?
I'm unclear how does the PyTorch Conv1d() work. Consider the following model which takes raw mono audio samples at input:
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precision and recall is zero
Why my model shows metrics like this? While my model was training recall and precision was equal to zero?
I trying to do binary classification of mushrooms [edible, poisonous]. I have CNN model with ...
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How do I implement convolution partially on my dataset?
I'm training a neural network on the results of a CFD simulation (or rather, 300-ish simulations with different initial conditions). The dataset contains the values for temperature, density, velocity, ...
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Convolution neural network loss increasing instead of decreasing
I am working on a binary image classification task in which I have greyscale images of size (1, 224, 224) (all normalized between 0 and 1) and a set of labels (0 or 1). I have around 2.6k images with ...
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How do I interpret GRAD-CAM's feature attribution to time series zero-padding in a CNN classifier?
Problem setting: MTS Classification with CNN architecture
I have a multivariate time series (MTS) dataset that contains 30 features. The goal is to solve a classification problem on this MTS dataset. ...
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How to implement CNN crop area as learnable parameter?
I'm currently implementing a 1D CNN to forecast a time series for an industrial process. Essentially, I give the model 30 time steps (1 time step = 1 minute) of input data captured from 7 different ...
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Questions about receptive field in the context of a practical CNN
I'm trying to understand the concept of receptive field better in the context of a practical CNN. All of the online info I can find on receptive field seems to be in a non-practical context so I'll ...
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Given a 4d tensor for time series predicition
I have multiple time series datasets, which i want to train to an lstm model.
The shape of the training data is (735,2,5,4). 735 are the time steps, 2 are the two time Series datasets, 5 are the ...
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How to correct ValueError about incompatible dimensions of training data set in locallyconnected1D layer NN
I'm training this simple network with a few points, but it can't train. The model looks okay, but when training it raises a ValueError about the dimension of the training data sets. Could someone help?...
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Image based ML paradigms where input vector integrates pixel intensity with pixel coordinates
Are there image based machine learning formulations where the input is not just the plain 2D image grid but one where pixel intensity $I(x,y)$, at position $(x,y)$ is coupled with the actual position ...
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Keras CNN model is throwing as error message as 'ValueError: Layer 'conv1d_12' expected 2 variables, but received 0 variables during loading'
Hope you're in good health and doing great.
I am trying to implement a CNN model to help predict kidney stones. Now, this model is running as expected on my local machine, but when I try deploying the ...
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How to correctly calculate the complexity of convolutional neural networks and compare that to classical neural network?
In my research, I need to compare the neural networks I have built, consisting mainly of perceptron and normalization layers, with networks from other publications that have convolution, pulling, ...
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How to copy and crop feature map in Unet?
I am confused about the principle of copy and crop in U-net, like the grey line shown above.
For example, the first grey line, how to convert a (64, 568, 568)(C,W,H) to a (128, 392, 392), did the ...
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Getting fitted outouts from hideen layer in CNN
I have a fitted CNN model through keras, with a convolution layer and pooling layer before 2 hidden layers. What I would like to see is the training data which is transformed through the final hidden ...
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How should I design my model architecture?
I have a dataset consisting of N presence points of a crop in a certain region. And I have 19 bioclimatic variables (temperature, precipitation, etc) which I extracted from .tif files. I have an input ...
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Final Model Training Problem - Overfitting
I am working on a CNN project for multiclass classification. I implemented hyperparameter optimization to find the most suitable model, during which I got a best accuracy of 97.38%. I then took this ...
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Siamese neural network - 100% accuracy when normalising data/50% when unnormalised
I am a currently trying to create a siamese neural network to classify my data. When I normalised the input images (img/255) i achieve 100% accuracy whereas when I dont I achieve 50% accuracy.
I ...
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Pixelated output of an autoencoder network
I have an autoencder that encodes an input size of (76, 400, 1) in a 2D convolutional layer, and decodes it an output size of (125, 400, 1). Both downsampling and upsampling are performed using a '...
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How can I separate and follow the plants that are very similar to each other with the YOLOv5 algorithm?
I want to differentiate between fern and mint using the YOLOv5 algorithm. Now I can take pictures of fern and mint, mark them on LabelImg, and train them in collaboration with Google. However, since ...
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Is it possible to encourage a constitutional network to reduce the range of activations
I am trying to implement a statically-quantized convolutional network. A problem I am having is that the convolutional layers all tend to produce an activation tensor with a large range (roughly -10.0 ...
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Python Tensorflow - Predict human vs horses images always same value
I'm trying to follow a tutorial about Tensorflow in Python and computer vision.
In this exercise I'm using a pre-trained model (InceptionV3) and some image augmentation about a datasets of humans vs ...
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Why not decrease the number of filters in each 1-dimensional convolutional layer for spectrogram processing?
In convolutional neural networks, the number of filters usually increases with every convolutional layer. Why is this common practice?
Nonetheless, these networks typically process images. When ...
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Fast Fourier Transform in computer vision
Can someone explain me how does FFT works in computer vision, please. I know something about FFT as an algorithm of competitive programming but I can't understand how it perform an image in computer ...
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Training and Validation accuracy decreases after using more data
I have a binary classification project, I use a neural network with the following architecture:
The shape of the input is 64×64×4. This input was fed to a Conv2D layer with 32(5×5) filters followed by ...
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Siamese Neural network inputs
A currently task involves the classification of bacteria as antibiotic susceptible and antibiotic resistant.
I have 4 data sets: treated resistant, treated susceptible, untreated resistant and ...