All Questions
Tagged with neural-network convolution
103 questions
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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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80
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Why keras Conv2D makes convolution over volume?
I have a very basic question, but I couldn't get the idea about 2D convolution in Keras.
If I would create a model like this :
...
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25
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How do convolutional layers in a CNN feed forward when there is multiple input feature maps?
I've been trying to recreate LeNet 1(LeNet 1 architecture is pictured in the top diagram) in python using NumPy. I am unsure of how the forward pass works when there is multiple Input feature maps in ...
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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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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 ...
- 109
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What will be the input_shape of tf.keras.layers.Conv3D be for these inputs
I have many videos, and each video is made up of 37 images (there are 37 frames in the whole video). And the dimension of each image is (100, 100, 3).... So the ...
user113403
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Padding in Convolution Formula
Why is it that the formula for each element in a convolution between an image $I$ and a $k \times k$ sized kernel $K$ is
$$ (I*K)_{ij}=\sum_{m=0}^{k-1}\sum_{n=0}^{k-1}I_{(i-m),(j-n)}K_{mn}=\sum_{m=0}^{...
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Are 3D kernels in convolutions summed over their channels?
Say for example that I have a 28x28x1 grey scale image and I will perform two consecutive convolutions. The first convolution has 2 3x3x1 filters and the second has 3 3x3x2 filters. Each convolution ...
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Can I say that a trained neural network model with less parameters requires less resources during real world inference?
Let us imagine that we have two trained neural network models with different architectures (e.g., type of layers). The first model (a) uses 1D convolutional layers with fully-connected layers and has ...
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Can anyone recommend me a very good pre-trained model for face or head detection?
I really need to know the best pre-trained models to detect faces and/or peoples' head. Not a face recognition model, but only to classify whether an object is a person's head/face or not.
I'm ...
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Can convolutional network learn structural properties of one feature w.r.t to other?
I'm going through the literature on pose-estimation ( DeeperCut, OpenPose, MultiPersonPosetrack).
I'm interested in knowing whether these networks/ generally a CNN can learn properties (geometrical) ...
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168
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What does it mean to say convolution implementation is based on GEMM (matrix multiply) or it is based on 1x1 kernels?
I have been trying to understand (but miserably failing) how convolutions on images (with height, width, channels) are implemented in software.
I've heard people say their convolution implementation ...
- 123
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Strategy for improving performance of 3D convolutional GAN
Others working with neural nets and GAN's might find this question interesting.
Background:
I've been working with data from Berkeleys PEER Ground Motion Database to generate new novel seismic traces. ...
- 121
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Can I tune a model after training it? (Convolutional Neural Network & Classification)
I am relatively new to Data Science and I've recently embarked on a project. Long story short, I've trained a CNN model to distinguish between Male and Female genders. However, I wish to tune my model....
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203
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How to perform upsampling (and NOT interpolation) process theoretically modelled?
As an example, I know that sampling a signal $s$ is modelled by multiplication of s by a dirac comb, which has the effect of convolving the Fourier Transform (FT) of $s$ by the FT of the dirac comb ...
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Why is the accuracy on the test dataset very low when training a neural network on an IMU dataset?
I am trying to train an IMU (Inertial Measurement Unit) dataset. The dataset contain 6 features (3-gyro, 3-accelerometer) and 1 label column. I have build a neural network via Conv1D, LSTM and Dense ...
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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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29
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Up to which layer can we consider the encoder to be?
I'm trying to extract the encoder from a U-Net network.
Given its architecture:
And its summary:
...
- 229
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2
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6k
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Convolutional neural network with 1 channel images/1 input channel
I'm following a tutorial on tensorflow using a convolutional neural network for images, but I'm looking to do it with grayscale images. How would the code posted there be different if it was for ...
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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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Help required in understanding how the error of a convolutional layer is calculated when filter and delta of next layer have differing dimensions
I am trying to implement a CNN in NumPy so as to better understand its inner workings
My architecture is as follows
10 images with 1 channel and with 28-pixel rows and columns (Dimension : (...
- 121
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3
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How does " Sparsity of connections" in CNNs causes the network to have less parameters?
I am studying Andrew NG's lectures on Convolutional Neural Network and he had provided two reasons for CNNs having less parameters compared to Non-Convolutional networks . They are :
Parameter ...
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CNN Multi-Class Model Only Predicts 1 class for all test images
I am trying to build a CNN model to predict 42 classes. I used pre-trained models for this. I used Xception.
This is how I have imported my dataset:
...
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Training a CNN on a large dataset
I am currently trying to build a CNN for around 100,000 images. There are 42 classes. I have used the default batch size of 32. This is how my model looks like:
...
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337
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What is an optimal local sparse structure of a convolutional vision network?
I was reading the InceptionNet Paper, where I found quite a few references to developing a sparse network structure, but I am not clear on what this means.
An ...
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Image classification using cnn [closed]
I did image classification using CNN and it successfully classified the images but How to save predicted images to separate folder for example i have two classes cat and dog after prediction how to ...
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36
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How to make sense of the input and output for a speech-generation model like WaveNet?
I am currently studying this model speech generation known as WaveNet model by Google. https://arxiv.org/pdf/1609.03499.pdf using the linked original paper and this implementation.
I find the model ...
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59
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best NN architecture for point prediction
I'm training to predict a single value y (continuos in [0,1]) based on a number of variables ...
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365
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How many computations in a CNN
I have not been able to find an answer, so if it is out there, please let me know.
I would like to calculate the amount of time, that a uC needs to give me an Output of an CNN. Therefore, I would ...
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553
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Combining convolution operations
Reading an article about 1x1 convolution, I found this:
It should be noted that a two step convolution operation can always be combined into one, but in this case [GoogLeNet] and in most other deep ...
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In CNN, how the weights are retained for filters for a particular class [closed]
I am new to CNN, What I have learned so far about the filters is that when we are giving a training example to our model, our model updates the weights by gradient descent to minimize the loss ...
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3
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581
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Convolutional neural network block notation
The paper by He et al. "Deep Residual Learning for Image Recognition" illustrates their residual network in Figure 3 as follows:
I am not a neural network expert, so could somebody please explain to ...
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Skip Connections in Residual Modules
I am a beginner in CNN theory and would like to understand the usage of residual modules better.
As far as I understand residual modules can be skipped, only the activation function must be computed ...
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326
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i have trained a model using fer2013 dataset using CNN for Emotion detection. Now i want to use it in a image
I have a trained model and saved the weights in fer.h5. Now i want to use the pre trained model in another set of images and save it to a excel file
...
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3
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2k
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Manual way to draw accuracy/loss graphs
During the training process of the convolutional neural network, the network outputs the training/validation accuracy/loss after each epoch as shown below:
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Creating deformed convolution using attention mask in Keras
I wanted to create deformable convolution network in Keras and compare its performance with standard convolution in Keras.
I tried on MNIST fashion data set.
Code for Standard convolution in its ...
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1
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Convolutional layers without pooling
I am studying the CNN architecture of the AlexNet, and I have seen that it has convolutional layers without pooling in between:
but I don' understand why this is done. Wouldn't be better to have ...
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How CNN applies backpropagation to update its weights and biases?
I understand that the 3 main layers for CNN are convolutional layer, ReLU layer and pooling layer.
However, I do not understand how CNN updates its weights and biases using backpropagation.
I ...
- 101
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160
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Winograd Convolution
For https://www.intel.ai/winograd-2/ , why use stride = 2 ?
Why need to transform input image pixels ?
Why this C++ implementation of winograd convolution does not require any input tensors ...
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356
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Forward and backward pass in Conv2D transpose Layer
I’ve several questions regarding the transposed convolution 2d layer. I’ve not been able to find a proper resource explaining the forward and backward pass. What I know (but not for sure) is, that ...
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334
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Do CNN convolution and pooling layers get backpropogated?
I can't find a simple answer to this by Googling which leads me to think the answer is no, but I want to be sure...
In a feed forward network, all of the layers of weights get backpropogated, but ...
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Unable to understand the meaning of following lines of the research paper for image segmentation
I am implementing a paper on image segmentation. It is based on the slight modification of the u-net architecture.
The paper is based on encoder and decoder steps
Following are the lines of the paper ...
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In CNN, why do we increase the number of filters in deeper Convolution layers for complex images?
I have been doing this online course Introduction to TensorFlow for AI, ML and DL. Here in one part, they were showing a CNN model for classifying human and horses. In this model, the first ...
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Making sense of indices in 2D convolution operations in convolutional neural networks
Referring to the answer here: https://www.quora.com/Why-are-convolutional-nets-called-so-when-they-are-actually-doing-correlations, the equation for a discrete 2D convolution is specified as:
$$C(x,y)...
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Resize instead of transposed convolutions
I'm trying to build a decoder version of ResNet, i.e. one that goes from the prelogits layer and attempts to recreate the image. I can get it working by using transposed convolutions, but the quality ...
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How to approach the PlantVillage dataset?
I'm working on the PlantVillage dataset and i want to predict the type of the disease from the image of a leaf. The dataset is labeled in pairs (Type of the plant,Healthy/name of the disease). I'm ...
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218
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wavenet structure explanation
I am a beginner in deep learning and recently I am trying to understand the structure of Wavenet. (for more information, please refer to the paper http://sergeiturukin.com/2017/03/02/wavenet.html) ...
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168
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Understanding Faster R-CNN
I'm having some trouble understanding the way the Faster R-CNN algorithm works. Specifically, the way the authors describe the concept of anchors.
In their paper from here they describe anchors in ...
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Anyone have fruit disease dataset? [closed]
I am doing a project on fruit disease recognition and classification. Anyone have an existing dataset of fruit diseases? Can you help me to find one?
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753
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Struggling to understand GCNNs (Graph Convolutional Neural Networks)
Although I've worked with CNN's for over a year, I am struggling to understand how GCNNs work paper on their simplification. I've read several papers, and I find myself out of my depth when they talk ...
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