Questions tagged [convolutional-neural-network]

Filter by
Sorted by
Tagged with
0
votes
0answers
20 views

I am trying to score CNN for packet data [closed]

The current error I am getting is:Shape mismatch: The shape of labels (received (32,)) should equal the shape of logits except for the last dimension (received (192, 3)).
0
votes
1answer
49 views

Problem with the input shape of CNN [closed]

I am currently facing the following error while trying to get my CNN working. I have attached the error and my input variables. Thanks in advance!! Model: ...
0
votes
2answers
44 views

CNN for Intrusion Detection [closed]

I hope you are alright. I am new to Deep Learning and I am assigned a task to find out how to use CNN for Intrusion Detection. After reading about I find out that CNN is used mostly for computer ...
1
vote
0answers
9 views

How to prove Separable Convolution layer is theoretically identical to traditional Convolution?

I have seen the saying that Separable Convolution layer is theoretically identical to traditional Convolution for so many times, but yet no one has pointed out where the proof is. God, I have google ...
0
votes
1answer
25 views

Conv1D layer input and output

Consider the following code for Conv1D layer ...
1
vote
0answers
16 views

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}^{...
0
votes
0answers
13 views

Understanding the convolution formula

According to several sources this formula, or the center originated version of it, is used to calculate an element of a convolution between an image $I$ and a kernel $K$ of size $k \times k$: $$ (I*K)...
4
votes
0answers
40 views

1D CNN Variational Autoencoder Conv1D Size

I am trying to create a 1D variational autoencoder to take in a 931x1 vector as input, but I have been having trouble with two things: Getting the output size of 931, since maxpooling and upsampling ...
0
votes
1answer
19 views

Issues in plotting Images using Keras

I am trying to visualize Skin Cancer Images using Keras. I have imported the images in my notebook and have created batch datasets using Keras.image_dataset_from_directory. The code is as follows: <...
0
votes
1answer
21 views

Autoencoder implementation using ImageDataGenerator

I'm using the concept demonstrated in this paper. Their training data consists of "GOOD" images and "BAD" images. They train the AE using "BAD" images (X) to make it ...
0
votes
0answers
15 views

DCGAN: why does my generator has less loss then my discriminator?

I have constructed a DCGAN (deep convolutional generative adversarial network) inspired by this github repository. It is written in a more low level Tensorflow code that I tried transforming into ...
0
votes
0answers
18 views

Use convolutional variational autoencoders for time series prediction

I want to use convolutional variational autoencoders for time series prediction. For example, here is the dimension of my data. ...
3
votes
1answer
25 views

Does a Convolutional Layer in a Neural Network learn the correlation between its input signals via its kernel?

I am interested in the theory behing what a convolutional neural network learns with its convolutional operations. I think it learns (useful) kernels which measure the correlation between its input ...
2
votes
0answers
14 views

What is the difference in computational cost at inference time between object detection and semantic segmentation?

I am aware that YOLO (v1-5) is a real-time object detection model with moderately good overall prediction performance. I know that UNet and variants are efficient semantic segmentation models that are ...
1
vote
0answers
16 views

How to train my CNN using AMD GPU on Windows?

I only have a laptop running Windows 10 with AMD Radeon Graphics Card at my disposal right now. I am aware that this is likely to make things tougher but I need to stick with this as long as I can't ...
1
vote
1answer
35 views

What is a sliding-window convolutional neural network?

In the abstract of "U-Net: Convolutional Networks for Biomedical Image Segmentation", the authors mention a sliding-window convolutional neural network. I've found several other articles ...
1
vote
2answers
44 views

What are the activation functions in Convolutional Layers for?

I read a lot about CNNs but I didn't quite understand some things: What are the activation function in CLayers for? If I understood it right, the only weights in these layers are the ones in Filters, ...
1
vote
0answers
17 views

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 ...
0
votes
1answer
14 views

Why are mini-batches degrading my conv net MNIST classifier?

I've made a conv net from scratch in python to classify the MNIST handwritten digits (centralized). It's composed of a single convolutional network with 8 3x3 kernels, a 2x2 maxpool layer and a 10 ...
2
votes
1answer
10 views

Architectures that take inputs of mixed sampling rates

Let's say a model is trained on multiple datasets of 1D time series. These datasets have been gathered with different sampling rates. I plan to use a convolution neural network to process these time ...
0
votes
0answers
6 views

Why can't SPPnet update convolutional weights according to the fast R-CNN paper?

While discussing the drawbacks of SPPnet in the fast R-CNN paper, the authors state "But unlike R-CNN, the fine-tuning algorithm proposed in [SPPnet] cannot update the convolutional layers that ...
0
votes
1answer
36 views

How to Connect Convolutional layer to Fully Connected layer in Pytorch while Implementing SRGAN

I was implementing the SRGAN in PyTorch but while implementing the discriminator I was confused about how to add a fully connected layer of 1024 units after the final convolutional layer My input ...
0
votes
0answers
9 views

Theoretically Speaking, How Do Squeeze-and-Excitation Blocks Help?

A SE block works by assigning a weight to each channel, contrary to a vanilla filter, which gives equal importance to all channels. My question is, theoretically speaking, shouldn't a regular filter ...
0
votes
0answers
21 views

About the relevance and interprertability of convolutional filters?

Convolution filters are known to perform very well in tasks, concerning some work with the image or video data, due to their ability to preserve some spatial information and equivariance property ...
1
vote
2answers
42 views

Do smaller neural nets always converge faster than larger ones?

In your experience, do smaller CNN models (fewer params) converge faster than larger models? I would think yes, naturally, because there are fewer parameters to optimize. However, I am training a a ...
0
votes
0answers
182 views

Keras ValueError: Shapes (64,) and (32,) are incompatible

I am trying to run my first CNN model on the Fashion MNIST dataset. I am using kerastuner to tune the hyperparameters. The below code gave me an accuracy of 90.4% on test, 92.2% on validation and 94....
0
votes
1answer
19 views

How to build a classification pipeline that will pass to another model?

Not sure if the title explained it, but I am trying to build a pipeline where it's like a decision tree, but also not. Say for example, I had a picture. The model classified the picture, but now I ...
0
votes
1answer
54 views

Are there any control-flow/conditional statements in AI/ML models?

I was recently asked this during an interview. When we write a C program, it has a control-flow in the form of conditional statements like if, ...
0
votes
0answers
28 views

Understanding the network structure of a multiple timeseries fusion model

Please don't mark this question as duplicated to Can I create a layer with multiple rnn cell ? [question about a paper] It has already been marked 2 times , I admit they do refer to a same paper, but ...
1
vote
1answer
64 views

TensorFlow 2 one-hot encoding of labels

I was following this basic TensorFlow Image Classification problem, where images of flowers have to be classified into one of 5 possible classes. The labels in the training set are not one-hot encoded,...
0
votes
0answers
11 views

torch.save(the_model, PATH) vs torch.save(the_model.state_dict(), PATH) - model loading incorrectly for one method

I just now noticed that the model does not get loaded correctly if I use the the_model.state_dict() method to save it. On the other hand, using ...
0
votes
0answers
10 views

Nan loss for one Image dataset but finite fraction loss for another dataset?

I am training neural network using chainer library on MNIST dataset and one other dataset. As MNIST dataset is greyscale, hence I converted the other colored dataset to greyscale using cv2 library. I ...
1
vote
0answers
57 views

How do you deal with variable input sizes with an encoder-decoder net with skip connections in Keras?

I am currently getting into image segmentation with Keras, and I am using an encoder-decoder type as in the image below. My problem is that applying a MaxPooling2D ...
2
votes
1answer
51 views

Where are the 60 million params of AlexNet?

On the abstract of the AlexNet paper, they claimed to have 60 million parameters: The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some ...
1
vote
1answer
66 views

How do I deal with additional input information other than images in a convolutional neural network?

I try to convert a game state of a board game into the input for a convolutional neural network. A convolutional neural network is useful because the players have to place items on the board, and the ...
0
votes
0answers
20 views

Training CNN for object detection

I have an idea for build object detection model and I would like to share it with community in order to see if it makes sense. Dataset: I've gathered images with different shapes and annotated them ...
0
votes
0answers
12 views

Intuition behind CNN Training Accuracy being Different than Loaded Model predicted on same exact data

I am trying to display some metrics in my final evaluation of several CNN models that I have trained using Tensorflow/Keras. I want to list the training accuracy for demonstration sake. However, I am ...
1
vote
0answers
10 views

Can we use CNN to effectively learn from a table whose various rows are permuted in the testing dataset, output for each remains same?

I have a set of classes, 37 to be precise. Each class is represented by a feature vector of size [1,10]. A single input sample has the dimensions of ...
1
vote
1answer
25 views

EfficientNet function composition or Hadamard

In the page 3 of the paper of EfficientNet, there is a equation $$\mathcal{N} = \bigodot_{i=1...s} \mathcal{F}_{i}^{L_i} \big(X_{\langle H_i, W_i, C_i \rangle}\big)$$ where $\mathcal{N}$ is the conv ...
0
votes
1answer
155 views

PyTorchs ConvTranspose2d padding parameter

Im confused about what PyTorchs padding parameter does when using torch.nn.ConvTranspose2d. The docs say that: "The padding argument effectively adds dilation * (kernel_size - 1) - padding ...
0
votes
1answer
31 views

Debugging a simple 1-D CNN for solving a simple classification problem

I have a rather simple classification problem that I am trying to solve. Each instance in my problem is a list of 1024 bytes (each byte is represented by a digit between 0 and 255). There are 2 ...
6
votes
1answer
193 views

Convolution backpropagation

I'm in the progress to learn, and understand different neural networks. I pretty much understand now feed-forward neural networks, and back-propagation of them, and now learning convolutional neural ...
1
vote
2answers
1k views

Is Flatten() layer in keras necessary?

In CNN transfer learning, after applying convolution and pooling,is Flatten() layer necessary? I have seen an example where after removing top layer of a vgg16 ,first applied layer was ...
0
votes
1answer
17 views

Understanding declared parameters in my Conv2d layer of my convolutional neural network

I am trying to understand the architecture of my keras model implemented by the sequential model. Here is a piece of the code : ...
0
votes
0answers
14 views

Is it possible to create a 3d to 2d U-net?

I was curious if it is possible to create a U-net type architecture that takes in a 3d image and outputs a 2d image? Or, alternatively, would some other architecture be better suited for this problem? ...
4
votes
2answers
352 views

Why are results without Transfer Learning better than with Transfer Learning?

I developed a neural network for license plate recognition and used the EfficientNet architecture (https://keras.io/api/applications/efficientnet/#efficientnetb0-function) with and without pretrained ...
0
votes
0answers
43 views

ConvNet - What to improve regarding architecture, procedure and technique?

I have a dataset of 180k images of license plates (so, not necessary to localize the license plate at first) for which I try to recognize the characters on the images (License plate recognition). All ...
0
votes
0answers
41 views

Plotting scikit-learn confusion matrix returns no values in the last class

I am attempting to create a confusion matrix using Scikit-Learn for a multiclass classification CNN, and it works well except for the fact that it does not provide ...
1
vote
1answer
71 views

pytorch convolution with 0-stride along one dimension

For some square images, I'd like to use torch.nn.Conv2d with the kernel as a vertical block. As in, the kernel size is defined as max value of the first dimension by 1. Since the first dimension has ...
0
votes
1answer
30 views

Is always converting a input vector into matrix and apply cnn's good idea?

I know the benefits of using cnns(reduced size weight matrices). Is it a good idea to convert a input vector(which is not a image) into a matrix and apply cnn's. What I understand is that it should ...

1
2 3 4 5
10