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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CNN filters with different size using Keras

CNN can have multiple number of filters on raw input data. Normally I specify the number of filters needed as 'filters= 250 ' and the size of the filter as 'kernel_size= 3'. (This means I will make ...
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Using a CNN for person name format inference?

I had the idea of converting a name to a 2D one-hot encoded tensor in order to do format inference for people names so "abbie" would be encoded as [[1,0,...] [0,1,...] ... [0,0,0,0,1,...]] Then there'...
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Undesrtanding behind Keras CNN example

This question has been asked in many different forms, however the answers are still confusing due to the different terminology and/or understanding of people or lack of experience behind the actual ...
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Strange learning and validation curve by training a CNN

by training a cnn and plotting the learning and validation curve as well as the training loss and validation loss I get strange results. What could be the reason for such a break-in?
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12 views

How to prepare the multivariable 3D data for a CNN regression problem? [closed]

I would like to know how to apply CNN in the following dataset. I have daily gridded data of temperature (T), salinity (S), current velocities (V) and nitrate (N) in Gulf of Mexico. I have a whole ...
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126 views

What is the computational complexity of a 1D convolutional layer?

What is the complexity of a 1D convolutional layer?. I'm getting $\mathcal{O}(n \cdot k \cdot d)$, but in Attention Is All You Need, Vaswani et al. report that it is $\mathcal{O}(k \cdot n \cdot d^2 )$...
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9 views

Doesn't matter which epoch number I use during grid search?

I want to optimize the learning rate and dropout rate of a CNN through grid search. I wonder which number of epochs should I choose for grid search? Is this a problem when I use a constant number of ...
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1answer
47 views

When is EarlyStopping really neccessary?

I have trained a CNN with EarlyStopping and I wonder if I should not use EarlyStopping and waste 20% of Trainingsdata for Validation, because it looks like as that the validation loss doesn't increase ...
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backpropagation between fully connected layer and convolution layer?

This is a simple example of a network consisting of two convolutional layers and one fully connected layer. ...
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How is convolution process done with 4D kernel?

Applying convolution with kernel of shape (1, 1, 4, 6)to a tensor of shape (2, 3, 2, 4) the result will be (2, 3, 2, 6). ...
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An CNN seems like capturing specific range of input data. (Image Segmentation)

I'm trying to build a model to segment brain tumors. I trained a model, and the validation dice coefficient is disappointing(0.6). When i saw the predicted images with the ground truths, it seems ...
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Using dimensionality reduction techniques (other than resizing) in a Convolution Neural Network for image classification?

This question seems silly even to me (just months in Machine Learning) but can we use any dimensionality reduction or feature selection technique on image data. I built a CNN-Pooling network for multi ...
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Siamese Network: validation accuracy highly fluctuating

I am trying to train a Siamese Network with 1D CNN, where I calculate the absolute difference between the two latent vectors, and then pass it to a sigmoid neuron to determine if the two inputs belong ...
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1answer
24 views

What exactly is InstanceNormalization and BatchNormalization?

I know this is a question that has been asked a lot. I know there are many good explanations on this topic and videos. However, I still have a hard time to understand the relationship visually between ...
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1answer
35 views

Is reLu a good choice when output is negative?

I am working on a model which contains positive and negative outputs. Can i use ReLu for the problem? Problem:Its non-linear regression where my input is image-pixels while output is some scaler value....
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10 views

Conv network gets worse when using more data

I'm trying to train a car image classifier based on a smaller version of AlexNet. I'm trying to learn about conv nets and training models in general, and I've found something that, to me, seems ...
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22 views

is this way of applying data augmentation correct [closed]

I'm training a CNN and want to apply some data augmentation to my input images. I combined some code from tensorflow tutorials and have the following workflow: I have a dataset containing all ...
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How to design high quality encoder-decoder pairs for images?

In many of todays deep models for image processing you can find some sort of encoder-decoder structure, in the simplest form it is an Autoencoder, whenever we want to introduce some kind of bottleneck....
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How to accelerate CNN training using fit generator [closed]

I have a dataset of 60000 images which I split in train and validation set (80/20) and I use ImageDataGenerator to get the images from disk as batches of size 32. I ...
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1answer
26 views

Conv or regressions to generate newspapers clips coords

I'm new to this community and to machine learning/deep learning so this question might be very basic or maybe I'm barking to the wrong tree. I'm trying to automate a very manual process which is ...
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82 views

Multi label classification baseline model

I have a multi label image classification task with a large number of labels (7000) . I am using ImageDataGenerator to flow the dataset from a file. Before I start ...
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1answer
31 views

Can Convolutional layers be used as classification/output layer instead of dense layers? [closed]

I want to use convolutional layers as my output/prediction layer instead of dense layer. Is this feasible? if yes, please suggest how?
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How to understand my CNN's training results?

I created a multi-label classification CNN to classify chest X-ray images into zero or more possible lung diseases. I've been doing some configuration tests on it and analyzing its results and I'm ...
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Model loss stays the same for hours before dropping

I'm training a CNN to colorize images. The model I have is not incredibly deep, and should work fine on the card I'm training on (2080 TI). Initially, I suspected the model was flawed in some way ...
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How does one solve the premise selection problem with imitation learning?

I was reading the following two new papers (HOList, Graph Representations) applying Machine Learning to Theorem Proving in Higher Order Logic. The main thing that I am unsure about is the following ...
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2answers
27 views

Why does a colormap such as viridis give better results for spectrogram-based audio classification over greyscale?

I have been trying audio classification on the UrbanSound8k dataset and MPSSC snore classification dataset. I am using the approach of transfer learning by extracting features from AlexNet and VGG19 ...
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Is it wrong to use categorical_crossentropy instead of binary_crossentropy for binary classification? [duplicate]

I was trying to build a CNN model. Data: 1) Consists of time series data of minute-wise water temperature to predict if there is high level of bacteria growth(label Y) in the water or not(label N). ...
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1answer
27 views

Is CNN capable of extracting the descriptive statistics features

I was trying to build a CNN model. I used time series data of daily temperature to predict if there is risk of an event, say bacteria growth. I calculated the descriptive statistics of the time series,...
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32 views

What is the purpose of grids in YOLO?

Considering the YOLO algorithm. Assume: Input image is n x n x 3 Number of anchor boxes is m For each anchor box, we have 1 (pc = probability of object) + 4 (4 variables to predict the bounding ...
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Is it possible to determine a certain characteristic point in a picture based on existing pictures?

The problem I try to solve is actually something I want to apply to an existing use case. To give some background information on that, there's this site botb.com that works kinda like a lottery, ...
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1answer
62 views

EarlyStopping after GridSearchCV

I want to optimize the hyperparams for a CNN-architecture by using GridSearchCV. As hyperparameters to optimize, I would like to use the learning rate, dropout rate, number of neurons in den dense ...
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1answer
19 views

neural network timeseries binary classifier: simple way to use temporal context

The problem I am working on is binary classification of a time series. To be more specific, input data corresponds to the 0.2s worth of accelerometer readings, ...
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1answer
22 views

Why is the number of filter used in higher layers more than lower layers in CNN?

Note that the number of filters grows as we climb up the CNN toward the output layer (it is initially 64, then 128, then 256): it makes sense for it to grow, since the number of low-level features is ...
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1answer
117 views

What is the intuition behind what makes dice coefficient handle imbalanced data?

I am writing my master thesis right now doing a project in deep learning doing semantic segmentation of MRI-images. Me and my partner have been looking at using dice loss instead of categorical cross-...
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23 views

How do filters affect the training loss in a convolutional neural network?

I am training a model, I am trying to lower the training loss. While testing different architectures I increased the number of filters to 128 from 64 - this reduced the training loss. I do not ...
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6 views

Performance Statistics Of Each Class in ML Classification problem

I am comparing the performance of two neural network architectures for a classification problem with three possible outputs. I am able to track the overall progress (validation accuracy/loss) of ...
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1answer
32 views

Neural networks assume continuity, what does this mean?

I encountered the following paragraph by Pedro Domingos (mentioned in Gary F. Marcus paper): ANNs assume continuity, graphical models assume conditional independence, and instance-based learning ...
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1answer
51 views

Why does VGG16 model use 13 hidden layers and 3 full connected layers, rather than 12 hidden layers or 11 hidden layers? [closed]

Why does VGG16 use 13 hidden layers, rather 12 hidden layers, or maybe 10 hidden layers? What is the motivation of the architecture? I think that maybe if we use only 12 hidden layers, similar ...
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Identifying number of hidden units in a convolutional neural network from a model configuration? [duplicate]

I'm a bit confused on how to identify and manually compute the number of hidden units (neurons) in each layer for a CNN? For example I have the following sample CNN neural network architecture code in ...
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20 views

How do I learn one time signal given another?

I conducted an experiment which led me to believe that two sensors were time correlated somehow. Their signals do not show any obvious correlation, however their spectrograms show a strong similarity ...
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1answer
19 views

Reference: Analysis of neural network structure

I'm asking for good references on the structure analysis of neural networks, for example, is 100 layers convolutional neural network (CNN) better than a 10 layer CNN? When do we use more layers? When ...
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Neural network converges to bias in high multi-label scenario

I am trying to create a trigger word detection system by following the tutorial: https://github.com/Kulbear/deep-learning-coursera/blob/master/Sequence%20Models/Trigger%20word%20detection%20-%20v1....
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23 views

Validity of PU learning when using character-level encoding and CNNs for text data

I'm trying to classify a large set of documents (~100M) as valid or invalid, based upon a small given set of labeled valid documents (~3k). I'd like to know if the PU learning approach described in ...
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1answer
78 views

How CNN reduces number of feature maps/ number of classes?

How does the CNN reduce numbers of feature maps or shall we say classes? I have looked into some literature where they have this in their methodology: Encoding part: Convolve using ReLU x 2feature ...
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29 views

Does it make sense to combine Early Stopping with k-fold cross validation?

I have a CNN architecture for which I want to optimize the hyperparameters such as learning rate, dropout rate and number of epochs. I am thinking of a combination of k-fold cross validation and ...
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25 views

Hyper Parameter Tuning - Selecting Ranges of Values

I am working on tuning a machine learning model and want to perform a grid search / hyperparameter tuning on my model to find the best hyperparameters. The literature I have found it pretty good with ...
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17 views

How to pad skip connections when using transposed / deconvolutional layers

if you have a standard CNN architecture with convolutional layers there are 2 reasons why the identity of the skip connection can't be added with the current output. 1) There was pooling between the ...
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39 views

GoogLeNet structure [closed]

I have 2 questions about GoogLeNet architecture, appreciate any help: Even after reading this, I still don't understand where is the parameters reduction in the second convolutional layer: ...
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1answer
22 views

Softmax function makes my machine to train much slower

I have two machines: CNN without softmax function and CNN with softmax function. But softmax function makes my machine to learn much slower and less accurate. Does anyone know why this happens? Here's ...
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1answer
55 views

Why is training of extremely deep fully-connected NNs difficult?

Practitioners know that if we increase the number of full-connected layers in Neural Network (NN), then at some points the NN performance starts to degrade. The natural reason is that we have ...