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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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0answers
55 views

Keras model with second to last sigmoid activated Conv1D layer followed by globalMaxPool outputs values outside [0,1]. Why?

I am trying to train a binary classifier. It is a residual network with skip layers etc. but ultimately, the bottom two layers are a 1D convolution with sigmoid activation followed by a global max ...
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4answers
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Model Validation accuracy stuck at 0.65671 Keras

I am using conv1d to classify EEG signals, but my val_accuracy stuck at 0.65671. No matter what changes i do, it never go beyond 0.65671. Here is the architecture ...
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1answer
67 views

When do I use Multiply and Add

I want to know the effect of Add and Multiply in keras by functionality. The dumb way of thinking is that they are meant to add ...
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2answers
890 views

Finding Feature Importance in CNN's?

Let's say I have images of cars. For each image in the dataset, I have let's say 3 pictures of the same car but in different angles. 1) The first image is the picture of the car from the front. 2) ...
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1answer
496 views

Tensorboard with pytorch dont display a graph

I am trying to visualize a model I created using Tensorboard with Pytorch but when running tensorboard and going to the graph tab nothing is shown, im adding my code for reference, also im adding a ...
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2answers
4k views

Transfer learning on new image size

Transfer learning: Take a trained neural network and use it for a new classification task. When we want to use transfer learning with a convolutional neural network, we don't have to use the same ...
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41 views

How to backpropogate error from convolutional layer with respect to the input when using multiple channels

I have been attempting to implement a Convolutional Neural Network in python and have run into a bit of a roadblock. When backpropogating the error in a convolutional layer let us say that we receive ...
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1answer
220 views

Why is training and validation loss steadily rising (eventually to NaN) in this CNN of mine?

Dear ML and data scientists: I have 4 layers of gray scale images for every single biological specimen in my dataset. I am trying to train a 4-convolution CNN (see pytorch architecture below) to ...
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2answers
57 views

ConvNet with concatenated data

I have a basic question regarding convolutional neural network. Assume I have a set of 1000 RGB images and I train a CNN from this set. I can obviously split each of my RGB images into 3 different ...
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2answers
170 views

CNN for unsupervised anomaly detection

I'm wondering if the following strategy has been already used and could work Let's says you have a CNN which work well to classify image data, dog and cat. You only have cat and dog image as training ...
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1answer
700 views

How to use PCA in CNN for image recognition using Keras?

I created a CNN model for image classification and I want to use Principal Component Analysis (PCA) but when I run pca.fit() code, the code still running for hours ...
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0answers
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Computing derivatives for backpropagation across a convolution step

This will be a long post, but I hope it'll be instructive to anyone else in my position. I'm trying to find how the derivatives of the loss function are calculated with respect to the kernels and ...
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2answers
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What is the benefit of using Max pooling in convnets as opposed to just using convolution layers? (from Francois Chollet's Deep Learning with Python)

I am reading Francois Chollet's Deep learning with python, and I came across a section about max pooling that's really giving me trouble. I simply don't understand what he means when he talks about "...
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2answers
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How to merge two CNN deep learning model using weighted sum and weighted product in Keras?

I am using Keras to create a deep learning model and I want to merge two CNNs by using weighted sum or weighted product. How can I merge two CNNs using weighted sum and weighted product?
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1answer
3k views

What is the difference between multiply and dot functions that is used to merge layer in Keras?

I want to merge two CNN deep learning model using Keras and would like to know what is the difference multiply and dot functions that is used to merge layer? ...
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1answer
109 views

Triplet loss function for face recognition?

In the Andrew-NG coursera course on Convnets he talked about triplet loss function for one shot face recognition. The formula given in the video is, $$\to \small \small \small ||f(A)-f(P)||^2 \;+\;\...
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3answers
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How many parameters in a Conv2d Layer?

I was following andrew-ng coursera course on deep learning and there's a question that has been asked there which I couldn't figure out the answer for? Suppose your input is a 300 by 300 color (RGB) ...
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37 views

How to design my own keras layer?

I am implementing the paper Perceptual GAN for small object detection. The design is described by the picture given below. I need to design my own keras layer. I have described my code below: The ...
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1answer
114 views

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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2answers
637 views

Keras Conv1D model Input_shape value error

I am not sure why I am receiving this value error. Additionally, I haven't found a tutorial that explicitly talks about the appropriateness of size of filters and kernel. I would appreciate some input ...
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1answer
41 views

conv net data retrieval on unseen class

I have build a conv net for image classification which work "well" Now I extract features from last fully connected layer and use it for image retrieval (find image most similar to my target image) ...
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1answer
108 views

Reason for Training and test loss sudden increment after some epochs keras

We know that if training and test loss are different from each other, our model is over-fitting. However, if both get high after some epochs, how can we justify it? One way to solve it is to reduce ...
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1answer
215 views

Strange binary classification result with a model that indicate it has been well-trained

The problem : I am trying to build a model for binary classification for melanoma 'MEL' and nevus 'NV' the dataset is from ISIC archive ISIC 2019 but for 8 different type of skin lesion, I am using ...
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3answers
349 views

How can I know if my conv1D model is overfitted or underfitted from loss curve?

I am working on classification of time series multivariate data. By doing PCA, I converted multivariate to uni-variate and fed it into a conv1d in keras. However, I am getting a very high accuracy ...
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0answers
310 views

TensorFlow: how to restore pre-trained meta model and pass it's weights and biases to the optimizer?

I trained a model on a specific dataset and saved it as a meta, I want to restore the model and use its weights and biases on another dataset the code isn't mine but I'm trying to restore the ...
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1answer
311 views

NCHW vs NHWC in Machine Learning

As I've been introducing myself to the various deep learning frameworks, I've noticed a difference in the default placement of channels for images. Is there a substantial difference between NCHW vs ...
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2answers
318 views

Overfitting CNN models

I tried to develop a number of CNN architectures to train on a 1000-point subset of the "cat-dog" Kaggle training set (meaning, by the way, that all 1000 data points were labeled). I used a 700-150-...
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2answers
156 views

Unstable results when I train a CNN

I'm currently training a CNN to do a binary classification. I'm getting fairly good results, but unfortunately the training is very unstable. Just by changing the seed the relative error changes by 20-...
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1answer
248 views

Calculating the Number of Parameters of a 2D CNN Layer

How can I calculate the number of parameters for a 2D CNN layer? I usually use the equation: $output \ width= ((W-F+2*P )/S)+1 = (x)$ The same answer will be valid for the output height considering ...
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1answer
87 views

How to perform polynomial landmark detection with deep learning

I am trying to build a system to segment vehicles using a deep convolutional neural network. I am familiar with predicting a set amount of points (i.e. ending a neural architecture with a Dense layer ...
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1answer
2k views

What is the best architecture for Auto-Encoder for image reconstruction?

I am trying to use Convultional Auto-Encoder for its latent space (embedding layer), specifically, I want to use the embedding for K-nearest neighbor search in the latent space (similar idea to ...
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1answer
532 views

Accuracy improving but, val_acc oscillating in ConvNet. What does it mean?

In my ConvNet model, i'm trying to classify some images. It is malware images and it doesn't contain complex features (i think), as expected model learn to classify images easily. You can see my ...
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1answer
6k views

What is fractionally-strided convolution layer?

In paper Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs, in Section 3.4, it said Since, the aim of this work is to estimate high-resolution and high-quality density maps,...
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488 views

Character segmentation using deep learning

I'm developing a character segmentation algorithm for license plate OCR. My algorithm includes two steps: segmentation and recognition. There is almost no problem for recognition thanks to CNN. My ...
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1answer
331 views

Why can't I use data augmentation with a pretrained convnet?

Reading Deep Learning with Python by François Chollet. In section 5.3.1, we've instantiated a pretrained convnet, VGG16, and are given two options to proceed: A) Running the convolutional base over ...
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0answers
24 views

Paper about AE-CNN is unclear. Deriving layers of dense blocks?

I am implementing the algorithm called Automatically Evolving CNN (AE-CNN). Some things aren't specified which makes it a bit hard to understand what the paper actually means to say. In the chapter 3....
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1answer
505 views

Setting input shape for an NLP task in R(Rstudio) using keras 1D convolution layer, when it expects 3 dimensional input (a tensor)

I am using R programming language and using Keras API to build a functional 1D CNN. I have a matrix of my dataset of the following shape rows*features (6000*1024). The input layer is set using the ...
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1answer
1k views

Weighted samples in Tensorflow for convolutional neural networks

For my binary classification problem (A vs B), each image in either class has its individual weight. This means, for example, if I have 10000 images for A, not all of the images are equally important....
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0answers
908 views

Multi label classification and sigmoid function

I'm new to neural networks so this may be silly question. I have build standard CNN network for image classification. I want multi-label classification network so I use binary_crossentropy as loss ...
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1answer
94 views

Application of Deep Reinforcement Learning

I'm new to deep learning, and especially to reinforcement learning. I would like to know if it's possible to predict which combination of hashtags (from a subset of chosen hashtags) would produce the ...
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2answers
923 views

Conv bias or not with Instance Normalization?

It is well known that Conv layers that are followed by BatchNorm ones should not have bias due to BatchNorm having a bias term. Using InstanceNorm however, the statistics are instance-specific rather ...
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0answers
310 views

Siamese network using VGG16 to verify the similarity images

I want to create a deep learning model which verify the similarity of the images. So, I will use Siamese network. My dataset is images dataset not CSV file. How can I create a Siamese network using ...
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1answer
726 views

CNN computing time on good CPU vs cheap GPU

I am a researcher working on my first deep learning project, which consists of using a CNN (pre-trained VGG16+2 densely connected layers) to classify drone imagery of vegetation. In trying to hack ...
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1answer
862 views

1x1 convolutions, equivalence with fully connected layer

I'm confused by the concept of equating a 1x1 convolution with a fully connected layer. Take the following simple example of a 1x1 convolution of 2 input channels each of size 2x2, and a single output ...
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1answer
4k views

Can I use the Softmax function with a binary classification in deep learning?

I want to create a deep learning model (CNN) for binary classification, can I used the softmax function instead of the sigmoid function in binary classification? Adding the classification layer to ...
3
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1answer
303 views

LeNet-5 - combining feature maps in C3 layer

Famous LeNet-5 architecture looks like this: The output of layer S2 has dimension: 10x10x6 - so basically an image with 6 convultions applied to it to derive features. If each dimension was again ...
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215 views

How to train two neural networks together

This could be considered as an extension of my previous question "How to make a region of interest proposal from convolutional feature maps?". Network 1: I have a multi-input neural network, it ...
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2answers
2k views

Can pooling ever increase accuracy in convolutional neural networks?

In ConvNets, pooling is used to downsize the input volume, leading to fewer parameters, leading to computational efficiency and possibly helping with overfitting. But can pooling ever increase the ...
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1answer
971 views

How to make a region of interest proposal from convolutional feature maps?

Problem Keras does not have any direct implementation of region of interest pooling. I am aware of how to perform maxpooling, but I don't know how to get bounding boxes from feature maps passed from ...
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1answer
77 views

What is exactly meant by neural network that can take different types of input?

There is a scientific document that implements a convolutional neural network to classify 3 different types of data, although how exactly, is unknown to me. Here's the explanation of network ...

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