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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One hot vector output in classification task

I'm working on CNN model and I used one hot vector type of labels. The number of classes is 3: [1,0,0], [0,1,0], [0,0,1]. net(x) I'm getting such an output: [0....
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Checking trained CNN on the images

I trained my CNN (model) classifier and want to check it on some new images. I have image x, so this syntax works for me for one image: torch.argmax(model(x)) What ...
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UNet Pytorch Audio Super-Resolution - Upsampling block problems

I am trying to reproduce Audio super-resolution. At the bottom is architecture in PyTorch from this paper (https://github.com/dsgiitr/Audio-Super-Resolution). It is supposed to accept downsamples/...
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TF/keras implement residual block

I read several papers, where they propose to implement residual blocks of ResNet as follows $$ u^{k+1} = u^k - \tau K^T \sigma(K u^k), $$ where $u^{k}$ denotes output on k-th layer, $\tau$ is ...
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Keras: apply multiple filters to each feature map in CNN

I am new to Keras, and I want to do the following: take a 2D image, and apply four 2D convolution kernels to it, giving four 2D feature maps. I could accomplish this. But then I want to apply two ...
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Convolutional neural network with Deep Q learning in games

What does "the average magnitude of maximal action value output by the network" tell us? I mean if we plot this graph, is it good to start as low value and then increase until it goes in a ...
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Improving a CNNs accuracy - help & advice

So I have created a CNN for image classification, and I train and test it with two datasets. One contains 9,339 images and the other 9,100 images. The first model which I designed gave an accuracy of ...
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Sequence multi-class classification only learns a few outputs

I have a multi-event delineation problem, where given a signal, I have an output with the same signal length. Something like 0011002200, where each unique number ...
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Validation Accuracy, Validation Loss and Training Loss Remain Constant

Background Hello, I'm new to deep learning and I recently trained a simple convolutional neural network from Francois Chollet's Deep Learning with Python book. The network was trained on 12500 images ...
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Why is the kernel of a Convolutional layer a 4D-tensor and not a 3D one?

I am doing my final degree project on Convolutional Networks and trying to understand the explanation shown in Deep Learning book by Ian Goodfellow et al. When defining convolution for 2D images, the ...
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Loading pretrained model with Pytorch

I saved my model with this code: from google.colab import files torch.save(net, 'model.pth') # download checkpoint file files.download('model.pth') Then uploaded ...
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Testing trained model on the image from the test set

I trained my EfficientNet (CNN) and got accuracy=0.73. The question is how to check it on one concrete image from the testing set? How to write a code in python for it? I described the testing set ...
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Adding Validation PyTorch

First of all, I'm new in this field and it's my first this kind of work. I'm trying to train EfficientNet (CNN), the code below is working fine, but I can't succeed to add also validation set to the ...
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ROC and AUC curve for CNN multi-class classification problem

I have produced a convolutional neural network to classify images (malware images) into different classes/families. I have managed to produce a confusion matrix and classification report. My ...
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ValueError: Classification metrics can't handle a mix of multilabel-indicator and multiclass targets in CNN

I am fairly new to ML and CNN, and this is my first attempt. I have managed to get my model to run, and now I am trying to produce a confusion matrix and classification report, but I am receiving an ...
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How to interpret stagnant validation curve

I'm new to deep learning, so I'm just learning how to interpret my models. I'm creating a mixed-convolutional neural net to classify melanoma images. Here's the model structure: ...
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Train only Region Proposal Network in faster RCNN architecture

I am looking for a way to used my pretrained EfficientNetv2 model and turn it into an object detection. Is there anyway, I can put my pretrained model as a backbone and only train the region proposal ...
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How to use grad cam for ECG data?

I am building a classification model of heart disease from 12-lead ECG data. I would like to implement Grad-CAM (Paper) to visualize the results. Any way to implement Grad-CAM on time series data?
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Forecasting with Neural network and understanding which underlying model is favoured

If I have a very large set of data (~ 1TB). How can I use Neural Network on this data to understand which underlying distribution (eg. let's say a Gaussian or a Poissonian with a certain mean, sd) is ...
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CNN can't predict images outside the dataset

I am using celeba dataset to train my CNN face landmark detection model. Here is my model ...
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Can the performance of a CNN be dependent on the train-test-val split random seed?

I am doing multi-class classification and comparing the effects of 2 image enhancement techniques (IET). IET 1 performs better than IET 2 at random seed x (for train-test-val split) IET 2 performs ...
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Does not using more filters in deeper CNN creates more images?

For example, we have applied 32 filters to a single image. Then it created 32 different images (stack of convolutional values). And in the second layer, if we apply 64 filters, are all these filters ...
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why transpose convolution is called “transpose”

https://d2l.ai/chapter_computer-vision/transposed-conv.html https://en.wikipedia.org/wiki/Transpose I understand what transpose convolution does, but I am confused about the name of 'transpose'. In ...
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Training a neural network with TWO possible correct outputs for one input

I have a system as a black box that has two correct outputs for a single input sample. now I want to train a neural network to generate at least one of the correct outputs for that input sample. what ...
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Correct way of computing dice score for image segmentation?

In binary image segmentation, for given a set of images, it's true mask and predicted mask. How do you compute dice score? Should I compute the dice score for each image separately and then find mean ...
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Representing multi-channel input signals with a single signal

I am working on an EEG signal classification problem. My dataset consists of EEG signals stored as 19X30000 NumPy arrays. Each row represents a single channel. For now, I am converting each of the ...
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66 views

Testing accuracy very low, while training and validation accuracy ~ 85%

I have a training dataset of 10000 pictures and a test dataset of 15000 pictures. There are 23 types of birds. First of all, I imported the necessary ...
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How to calculate convolution for 2nd conv Layer in CNN, Do we need to average across all feature maps?

I understand that for the first layer (assuming we have a grayscale image) we calculate the convolution of 3*3 receptive field as a weighted sum of receptive weights with pixels $ x1 · w1 + x2 · w2 + ...
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Adding layer to a trained CNN to process higher resolution images. Tried 2 schemes, 1 works fine, 1 fails completely

I'm working with images coming from a sensor, for which 1 pixel corresponds to 2 mm in the real world. I've built and trained a CNN that does semantic segmentation of the image (128x128 pixels) and it ...
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Using softmax for multilabel classification (as per Facebook paper)

I came across this paper by some Facebook researchers where they found that using a softmax and CE loss function during training led to improved results over sigmoid + BCE. They do this by changing ...
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Pytorch: how to pass the hidden state between the samples in LSTM?

I am trying to boost the performance of a object detection task with sequential information, using ConvLSTM. A typical ConvLSTM model takes a 5D tensor with shape ...
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Best 5-layer pretrained CNN model

I am doing a visualization project on convolutional neural nets to aid learning and need a simple to display but complex enough pretrained CNN model so I can visualize feature maps for each layer. I ...
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Getting constant accuracies for training and validation sets despite their losses are changing during CNN training?

As the title clearly describes the issue I've been experiencing during the training of my CNN model, the accuracies of training and validation sets are constant despite the losses of them are changing....
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Training loss = 0, training accuracy =1, validation and test around 85%

I have created different CNNs for doing image classification. The dataset is this: https://www.kaggle.com/crowww/a-large-scale-fish-dataset There are 9 classes, and each class contains 1000 images of ...
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21 views

Max Pooling in first Layer of CNN

I am seeing, in all the notebooks that I found, that Max Pooling is never used in the first layer of a CNN. Why this? Is it a convention among data scientist to do not use max pooling in the first ...
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How to deal with a small dataset for image classification using CNN?

I have a dataset consisting of characters(lowercase and uppercase) and numbers, totalling about 62 classes. The data I have are about 45 images per class and no test data. The data is a subset of the ...
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106 views

Improve Convolutional Autoencoder

I just built a Convolutional Autoencoder to try to reconstruct a time series with shape (4000, 10, 30). This is the code, I used a batch size of 32, but I think it ...
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1answer
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3 images as one input in CNN (U-Net) [closed]

I have been advised by my supervisor that if my U-Net segmentation network has RGB images at the input then I could use the channels for different images - median filter for R, normalization for G, ...
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Parallel programming in Python

I have the next code that I am trying to run in parallel: ...
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28 views

How to use data generator for regression keras?

I am using the Keras data generator to load data from a directory. I am basically dealing with a regression i.e there is a numerical value for each of my images in the range ...
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Compare distance between embeddings in different dimensions

I am working on a problem with CNNs. After the convolutional layers, comes a "flatten". One could interpret that as a representation of the input image in some high-dimensional continuous ...
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Using Wasserstein loss function for image-to-image-regression

The context I have a 3D array (representing a grayscale 3D image) and want to turn this into another 3D array of the same size. In this output array the value of each pixel is a number that measures ...
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26 views

Which applications can not be handled by very Deep CNN models?

I wanted to know what challenges very deep models can face even if the accuracy is good. Would they be not suitable for any application given that my model is very very deep? I wanted to know if ...
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Multi-object detection within single image

Given an image with multiple objects within it, I would like to train a CNN to output vector of labels corresponding to the presence/absence of objects within the image. I would like to know whether ...
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Calculate the Convolutional Autoencoder sizes - Conv1D

I'm approaching the Conv1D for the first time and I do not understand how to calculate the parameters in each layer. I have an input of (3000, 10, 30), but I ...
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Understanding scipy.signal.convolve2d full convolution and backpropagation between convolutional layers

I'm learning about convolutional neural networks. The convolution operation in order to extract features that is described in literature and posts used for this is quite intuitive and easy to ...
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27 views

Create sequence for a Conv1D layer

Im studying the following tutorial on the Keras website and I'm trying to understand how to create a sequence for a Conv1D layer. This is their method: ...
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How to modify a Convolutional Neural Network architecture built for a univariate time series to multivariate time series?

I have built a CNN (in combination with a LSTM cell) that takes 1D time series-like data as an input and performs classification. I am obtaining a good performance, but the complete data has actually ...
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Convnet with peculiar loss function not learning!

Im using this loss function: ...
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Methods to visualize the filters in the later layers of a CNN?

I've extracted the weights from the filters of a pretrained model (AlexNet). I wish to represent these weights visually, this works fine for the first layer as there is only 3 input channels so I can ...

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