I'm trying to improve a CNN I made by implementing a weighted loss method described in this paper. To do this, I looked into this notebook which implements the pseudo-code of the method described i...

I'm learning image classification using PyTorch (using CIFAR-10 dataset) following this link. I'm trying to understand the input & output parameters for the given Conv2d code: import torch.nn a...

I saw a sudoku solver CNN uses a sparse categorical cross-entropy as a loss function using the TensorFlow framework, I am wondering if there is a similar function for Pytorch? if not could how coul...

This question could seem difficult but I need to know how tom import ResNeXt model into Keras Tensor-flow, I have tried but there was no use from keras.applications.resnext import ResNeXt50 ------...

I'm trying to train a CNN to categorize text by topic. When I use binary cross-entropy I get ~80% accuracy, with categorical cross-entropy I get ~50% accuracy. I don't understand why this is. It's...

The DeepFace paper from Facebook uses a Siamese network to learn a metric. They say that the DNN that extracts the 4096 dimensional face embedding has to be duplicated in a Siamese network, but bot...

After checking all the existing answers on Stackoverflow here: Checkpointing keras model: TypeError: can't pickle _thread.lock objects and here: TypeError: can't pickle _thread.lock objects...

I am attempting to implement a CNN-LSTM that classifies mel-spectrogram images representing the speech of people with Parkinson's Disease/Healthy Controls. I am trying to implement a pre-existing m...

I have a data-frame of size (140000,22) dimensions. i have to create 2d array of equal dimensions to pass it into convolution neural network . Can you please guide how to transform on this datafr...

From the PyTorch documentation for Convolution, I see the function torch.nn.Conv1d requires users to pass the parameters in_channels and out_channels. I know these refer to "input channels&quo...

I have checked all the solutions, but still, I am facing the same error. My training images shape is (26721, 32, 32, 1), which I believe it is 4 dimension, but I don't know why error shows it is 5 ...

I have already trained a network and I have saved it in the form of mynetwork.model. I want to apply gradcam using my own model and not VGG16 or ResNet etc. apply_gradcam.py # import the necessary ...

I have a fine-tuned network that I created which uses vgg16 as it's base. I am following section 5.4.2 Visualizing CovNet Filters in Deep Learning With Python (which is very similar to the guide on...

I've been reading about convolutional nets and I've programmed a few models myself. When I see visual diagrams of other models it shows each layer being smaller and deeper than the last ones. Layer...

Pooling can be considered as convolution whether it's max/average, right? The difference is that conv has parameters for optimization, but pooling doesn't, right? - e.g. the weights that filter in...

I am trying to train a CNN in pytorch,but I meet some problems. The RuntimeError: RuntimeError: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 2.00 GiB total capacity; 584.97 MiB alre...

How do I calculate the output size in a convolution layer? For example, I have a 2D convolution layer that takes a 3x128x128 input and has 40 filters of size 5x5.

What are the default Kernel-Size, Zero-Padding, and Stride arguments in Conv2D (keras.layers.Conv2D)? What happens if these arguments are not specified?

I have the following architecture: Conv1 Relu1 Pooling1 Conv2 Relu2 Pooling3 FullyConnect1 FullyConnect2 My question is, where do I apply batch normalization? And what would be the best function...

I'm working on very sparse vectors as input. I started working with simple Linear (dense/fully connected layers) and my network yielded pretty good results (let's take accuracy as my metric here, 9...

May I ask if the following understanding of batch normalization in convolutional neural network is correct? As shown in the diagram below, the mean and variance are calculated using all the cells o...

As much as I know that Triplet Loss is a Loss Function which decrease the distance between anchor and positive but decrease between anchor and negative. Also, there is a margin added to it. So for ...

I think I read somewhere that convolutional neural networks do not suffer from the vanishing gradient problem as much as standard sigmoid neural networks with increasing number of layers. But I hav...

I understand that bias are required in small networks, to shift the activation function. But in the case of Deep network that has multiple layers of CNN, pooling, dropout and other non -linear acti...

Here, I am getting the data as [0 1 0 0] or [0 0 0 1],--- I get it that it is telling me that [0 1 0 0] is label2,[0 0 0 1] is label4, [1 0 0 0] is label1, [0 0 1 0] is label3. import pickle from ...