Questions tagged [pytorch]

For conceptual questions that somehow involve the PyTorch library, but note that programming questions are off-topic here.

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Is there any closed form analytical expression to represent fractional max pooling?

There are Nineteen types of pooling layers in PyTorch. Almost all of the layers are provided with corresponding analytical formulae. But analytical formulae are not provided for the fractional max-...
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56 views

How will the filter size affect the transpose convolution operation?

After a series of convolutions, I am up-sampling a compressed representation, I was curious what is the methodology I should follow to choose an optimum kernel size for up-sampling. How will the ...
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1answer
94 views

What is the difference between FC and MLP in as used in PointNet?

I am trying to understand the PointNet network for dealing with point clouds and struggling with understanding the difference between FC and MLP: "FC is fully connected layer operating on each ...
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94 views

Why does GAN loss converge to log(2) and not -log(2)?

In Goodfellow's paper, he says: Hence, by inspecting Eq. 4 at $D^*_G (\mathbf{x}) = \frac{1}{2}$, we find $C(G) = \log \frac{1}{2}+ \log \frac{1}{2} = − \log 4$. To see that this is the best ...
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149 views

What is the equivalent PyTorch version of tensorflow lite

Update Checked the PyTorch Mobile which is designed to Android and iOS. Although according to the document, it says it can build for ARM CPUs, but there isn't any documentation mention about how to ...
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66 views

Why does the BatchNormalization layer produce different outputs during training and inference?

I modified resnet50 architecture to get a regression network. I just add batchnorm1d and ReLU layers just before the fully connected layer. During the training, the output of batchnorm1d layer is ...
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422 views

How can I train YOLO with the COCO dataset?

I am trying to implement the original YOLO architecture for object detection, but I am using the COCO dataset. However, I am a bit confused about the image sizes of COCO. The original YOLO was trained ...
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1k views

How to transfer learn Darknet YOLOv3

I've started getting into object detection in image. I have YOLOv3 neural network with Darknet framework. The network is pre-trained from COCO data set. Now I need to do some transfer learning in ...
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1answer
27 views

What is the Intermediate (dense) layer in between attention-output and encoder-output dense layers within transformer block in PyTorch implementation?

In PyTorch, transformer (BERT) models have an intermediate dense layer in between attention and output layers whereas the BERT and Transformer papers just mention the attention connected directly to ...
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26 views

Are there any benefits of adding attention to linear layers?

Is attention useful only in transformer/convolution layers? Can I add it to linear layers? If yes, how (on a conceptual level, not necessarily the code to implement the layers)?
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18 views

Non-determinism with mixed precision?

Currently, we're trying to improve failure analysis capability when using neural nets. One thing we want to resolve is output variation between batched runs and non-batched runs. For example, we wrote ...
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17 views

What is an "additional channel dimension" contain in batch normalization?

Consider the following explanations regarding a batch normalization layers in PyTorch #1: one dimensional batch normalization class torch.nn.BatchNorm1d(.........) Applies Batch Normalization over a ...
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26 views

Positional Encoding in Transformer on multi-variate time series data hurts performance

I set up a transformer model that embeds positional encodings in the encoder. The data is multi-variate time series-based data. As I just experiment with the positional encoding portion of the code I ...
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46 views

Batch normalization for multiple datasets?

I am working on a task of generating synthetic data to help the training of my model. This means that the training is performed on synthetic + real data, and tested on real data. I was told that batch ...
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72 views

What is the fundamental difference between max pooling and adaptive max pooling used in PyTorch

PyTorch provides max pooling and adaptive max pooling. Both, max pooling and adaptive max pooling, is defined in three dimensions: 1d, 2d and 3d. For simplicity, I am discussing about 1d in this ...
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30 views

Is there any animation that illustrates the "fold" and "unfold" operations of convolutional layers?

There are fourteen convolution layers in PyTorch. Among them six are related to convolution, another six are related to transposed convolution. The remaining two are fold and unfold operations. The ...
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46 views

Is there any gain by lazy initialization of weights, biases and number of input channels for a convolution operation?

The basic layers for performing convolution operation in PyTorch are ...
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53 views

How can I add a Sequential CNN layer on top of BERT model?

Information I'm working on a binary classification task and used BERT model from transformers library to do it using the custom model below: ...
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3answers
66 views

How can I model any structure for a neural network?

Hello I am currently doing research on the effect of altering a neural network's structure. Particularly I am investigating what affect would putting a random DAG (directed acyclic graph) in the ...
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49 views

What's the best way to take a list of lists as DQN input?

I have my own environment for the DQN algorithm. In my environment, the state space is represented by a list of lists, where each sublist can be of different lengths. In my case, the length of the ...
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105 views

Why does the VAE using a KL-divergence with a non-standard mean does not produce good images?

I know I can make a VAE do generation with a mean of 0 and std-dev of 1. I tested it with the following loss function: ...
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35 views

variational auto encoder loss goes down but does not reconstruct input. out of debugging ideas

My variational autoencoder seems to work for MNIST, but fails on slightly "harder" data. By "fails" I mean there are at least two apparent problems: Very poor reconstruction, for ...
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17 views

is it ok to take random actions while training a3c as in below code

i am trying to train an A3C algorithm but I am getting same output in the multinomial function. can I train the A3C with random actions as in below code. can someone expert comment. ...
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76 views

Is it good practice to save NLP Transformer based pre-trained models into file system in production environment

I have developed a multi label classifier using BERT. I'm leveraging Hugging Face Pytorch implementation for transformers. I have saved the pretrained model into the file directory in dev environment. ...
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148 views

Classification or regression for deep Q learning

DQN implemented at https://github.com/PacktPublishing/PyTorch-1.x-Reinforcement-Learning-Cookbook/blob/master/Chapter07/chapter7/dqn.py uses the mean square error loss function for the neural network ...
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52 views

In layman's terms, what is stochastic computation graph?

I'm going through the distributions package on PyTorch's documentation and came across the term stochastic computation graph. In layman's terms, what is it?
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58 views

Benchmarking SAC on Pybullet

So far I have seen TD3 and DDPG benchmarks on Pybullet environments, but I am looking for SAC benchmarks on Pybullet too, anyone can help?
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84 views

How can I use GPT-2 to modify seed text of one form into a different form (LENGTH INVARIANT) whilst retaining meaning?

I am currently starting a research project whereby I am trying to convert text of one form into another. i.e. If I were to write a seed sentance of the form "Scientists have finally achieved the ...
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165 views

Replace epsilon greedy action selection and the standard DQN by an Independent Gaussian Noise Network Model

Here is my code Recently, I solved the game of Atari Breakout using a classic DQN model. The convergence of the mean reward slowly improved during three days. I was interested in learning a method ...
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89 views

Is it possible to do token classification using a model such as GPT-2?

I am trying to use PyTorch's transformers as a part of a research project to do sentiment analysis of several types of review data (laptop and restaurant). To do this, my team is taking a token-...
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1answer
67 views

time-series prediction : loss going down, then stagnates with very high variance

I am trying to design a model based on LSTM cells to do time-series prediction. The ouput value is an integer in [0,13]. I have noticed that one-hot encoding it and ...
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82 views

What are the pros and cons of Keras, PyTorch and Caffe for computer vision?

I have tried to get the basic grasp of the following deep learning frameworks with python: Keras Pytorch Caffe However, I have lately noticed that people in the computer vision community care less ...
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34 views

Pytorch deep learning models and tabular data representation

I have quite a naive question regarding Pytorch deep learning models and tabular data representation. So, assume I have a dictionary of tables. Each table has some number of columns: categorical and ...
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1answer
143 views

RealNVP gives wrong probabilities

I am trying to use RealNVP with some data I have (the input size is a 1D vector of size 22). Here is the link to the RealNVP paper and here is a nice, short explanation of it (the paper is pretty long)...
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87 views

FastAi How to turn off crop in ImageDataBunch

I just trained my birds model. It works fine when I was testing it with close pictures. But when I moved the pictures further away my camera, the model was not able to detect birds. My guess is in ...
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79 views

Why is my variational auto-encoder generating random noise?

This is my first variational autoencoder. Background info: I am using the MNIST digits dataset. The model is created and trained in PyTorch. The model is able to get a reasonably low loss, but the ...
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27 views

How can I train a neural network to find the hyper-parameters with which the data was generated?

I have 10000 tuples of numbers (x1, x2, y) generated from the equation: y = np.cos(0.583 * x1) + np.exp(0.112 * x2). I want to ...
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60 views

Why are the current means and the old ones the same in this implementation of Elastic Weight Consolidation?

I'm trying to re-implement Elastic Weight Consolidation (EWC) as outlined in this paper. As a reference, I am also using this Github repository (another implementation). My model/idea is pretty ...
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14 views

Can Grad CAM feature maps be used for Training?

I am trying to recreate the architecture of the following paper: https://arxiv.org/pdf/1807.03058.pdf Can someone help me in explaining how are the feature maps coming out of the output of GradCam ...
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93 views

Initial LSTM hidden state and cell

If we use LSTMCell from torch: The initial hidden and cell layers should be CONSTANT (from the first time you run the program) and saved right? Like random seeds? ...
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98 views

Super Resolution CNN generates black dots on output images

I have been trying to train a CNN for the super-resolution task based on the work of Dong et al., 2015 [1]. The network structure built in PyTorch is as follows: ...
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17 views

Limits for a bottleneck

I have some 64x64 pixels frames from a (simulated) video, with a spaceship moving on a fixed background. The spaceship moves in a straight line with constant velocity from left to right (along the x-...
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37 views

Add a layer derivative in the loss function

I am writing a NN in pytorch and I want to add the derivative of the output with respect to one of the inner layers in the loss. Here is a simple example of what I mean: ...
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1answer
107 views

How to properly optimize shared network between actor and critic?

I'm building an actor-critic reinforcment learning algorithm to solve environments. I want to use a single encoder to find representation of my environment. When I share the encoder with the actor ...
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95 views

How to make episode ending "good" in reinforcement learning?

TL;DR: read the bold. The rest are details I am trying to implement Reinforcement Learning:An Introduction, section 13.5 myself: on OpenAi's cartpole The algorithm seems to be learning something ...
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1answer
14 views

Multi label classification on non binary labels with pytorch

I am working on a project consisting of medical images and a huge dataset of multi-label and non-binary labels/outcomes ( sex, blood pressure, age and 40 more ). Would be the best approach to hard ...
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19 views

Without using data augmentation gives results better than using data augmentation

I am a beginner to deep learning, I'm doing the image classification problem on a small self plant disease imaging dataset (400 images). I am doing transfer learning (pre-trained ...
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15 views

specifying a hybrid Bayesian network in pyro

I am trying to learn about Bayesian networks and am really having a hard time to figure out how to setup some simple models. Say, I have a model as: ...
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1answer
30 views

How to pass multiple vectors and numeric features as input to the neural network?

I need help in a regression scenario. I have 12 input features. 4 of which are coordinates (each is a vector) in XYZ plane ...
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33 views

How to increase accuracy of image orientation classification (Left, Right, Center)?

I am working on classifying images in "Left", "Right", "Center", "Back". Training and Validation images look like this: The images are "Left", "...