Questions tagged [deep-learning]

a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hidden layers), but also with some sort of Probabilistic Graphical Models.

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23 views

Detect if my ANN model is overfitted

I've been trying the kaggle dataset of Credit card fraud detection Dataset . I've used ANN using keras and tensorflow. You can find the code in the screenshot. The only problem is im getting accuracy ...
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11 views

How to convert photo to a vector drawing

I am using the following code to convert photo to a drawing: ...
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14 views

Generative Adversarial Networks - The simplest possible examples

I'm looking for the simplest possible examples of GANs. What would be simple yet illustrative examples with, say, univariate data and in which both the generator and the discriminator are as simple as ...
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Adapting Pytorch tutorial “NMT from Scratch…” for dynamic RNN

I have taken the code from the tutorial and attempted to modify it to include bi-directionality, any arbitrary numbers of layers and to accept either GRU or LSTM as method type. Link to the tutorial ...
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AlphaGo Zero loss function

As far as I understood from the AlphaGo Zero system: During the self-play part, the MCTS algorithm stores a tuple ($s$, $\pi$, $z$) where $s$ is the state, $\pi$ is the distribution probability over ...
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Do I need different CNN architectures to detect the same objects for the same dataset with higher fps and higher resolution?

I am planning to do object detection on a dataset that is 5 fps with a resolution of 720 x 320. After training that CNN on that dataset, how significantly should I modify the CNN architecture to ...
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What is “style normalization” referred to in the Adaptive Instance Normalization paper?

In "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization", the authors argue that the significant performance boost from instance normalization is not only due to contrast ...
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5 views

BatchNorm vs InstNorm from the perspective of feature distributions

What I understand so far... The main purpose of BatchNorm is to overcome covariance shift -- more specifically what the authors of BatchNorm coined "internal covariance shift". Covariance shift is ...
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1answer
26 views

Learning rate Scheduler

A very important aspect in deep learning is the learning rate. Can someone tell me, how to initialize the lr and how to choose ...
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15 views

Different convolutions in CNN

I have a simple question. Why only convolution is used in CNN? There are a lot of possible rules for combining a filter and an image. Why is pixel-wise convolution the standard? For example, dropout ...
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1answer
19 views

Large amount of Sigmoid outputs are ones and zeros

I have Keras neural network for binary classification with final layer having one output with Sigmoid activation. I have noticed that large amount of output numbers are strictly one or zero (rather ...
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Research on explaining generalizability of deep learning methods

I've read a few classic papers on different architectures of deep CNNs used to solve varied image-related problems. I'm aware there's some paradox in how deep networks generalize well despite ...
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1answer
29 views

How to draw neural network diagrams with this particular style?

I would like to draw a neural network architecture with the follow style. Do you know which tool can be used to do this? The paper is Operation-aware Neural Networks for User Response Prediction.
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what is the difference between euclidean distance and RMSE?

I'm searching for a loss function that fits my Project. Actually I have two question but they are in the same direction. I take a look at the definition of the root mean squared error and the ...
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1answer
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How would keras model learn at every epoch?

I know concept of Epochs, batch size and iteration. let's say, Total_data = 6400 Batch_size = 64 Iteration = 100 In this, basically we are taking in 64 data points to computer memory and ...
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How bit precision while training can impact DNN's accuracy - Libraries that would support inference with quantized types

I would like to check how bit precision impacts DNN's accuracy. Do you know any C/C++/Python libraries that wouldn't require huge rework for supporting inference with quantized types? For example, I ...
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18 views

How can I increase the speed and performance of my implementation of an AI for Reversi?

I made an AI for Reversi, aka Othello (8×8), like Alpha Zero, using this book. This book is written in Japanese. The source code of the AI I implemented can be found in this Github repository. There ...
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1answer
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Can the same CNN architecture be used for different data sets?

I have a CNN architecture that works well on 32x32x3 images. Can I use that same architecture for a data set made up of 28x28x1 images? (Both data sets have 10 classes). If this is possible, what ...
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14 views

Mixing unsupervised and supervised algorithms in image classification model

I am trying to replicate the general image classification model used in a paper that I cite later below. The following image is an extract from a paper that proposes a novel method of performing image ...
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1answer
27 views

Why in this case are gradient steps not perpendicular to contour lines?

There is a theorem that gradient at point is perpendicular to tangent line to contour line at given point. Why in this picture it seems that this rule is not respected? source: http://www....
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Activation Functions in Neural network

I have a set of questions related to the usage of various activation functions used in neural networks. I would highly appreciate if someone could give explanatory answers. Why is ReLU is used only ...
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20 views

Running LSTM model on a big data sample using pyspark

I was wondering how does one run an LSTM model on a big dataframe in pyspark. Ideally, one wants to run the model parallelly on different nodes of a spark cluster. But how does one do that?
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34 views

Accuracy of CNN on images taken under different conditions

I have a dataset containing images taken under 4 different conditions. When training the model, I use the same proportion of images (25%) from each condition. Then, I'm testing on 4 different test ...
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38 views

Combining 2D Detection with Disparity Maps to Learn 3D Object Geometry

Since the disparity map above is a representation of the object's distance from the camera's origin, is it reasonable to assume that a network (perhaps a convolutional LSTM) could be trained to ...
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9 views

VGG style transfer fails with pre-computed `vgg19.preprocessing()` for the content, transfer, style inputs

I'm working through the style transfer tutorial on tensorflow, see: style transfer I made a few adjustments to my notebook, but it works fine for the base case: [ content_image | transfer_image | ...
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24 views

Deep Learning for Video Classification

Which Deep Learning architecture is best for classifying short videos of variable length? I would like to classify videos that last from 1 up to 3 seconds.
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41 views

Error loading a model (.h5 file) after training yolo-keras classification model

I am working on realtime object detection using my laptop's camera with Yolo and Keras. I have trained a model and the resulting output is a .h5 file containing (from my understanding) the model and ...
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1answer
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How to calculate accuracy, precision and recall, and F1 score for a keras sequential model?

I want to calculate accuracy, precision and recall, and F1 score for multi-class classification problem. I am using these lines of code mentioned below. ...
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saving a model during training of an RL agent

I am training an RL agent using PPO2 algorithm. Iam using stable-baselines library. During the training process, my rewards are slowly increasing and stabilizing, but are falling down suddenly. I ...
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21 views

What does “factor computation” mean in this context?

I'm reading the paper Attention is all you need here and came along the following sentence: "Recurrent models typically factor computation along the symbol positions of the input and output ...
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1answer
28 views

Training with many CPU cores doesn't improve performance

I ran my job on a computing cluster: first with 1 node / 4 cores, then with 2 nodes / 32 cores. But the training time is pretty much exactly the same for both of them! 67 seconds per step. I am ...
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Implementation of the paper 'A Comprehensive Study for Center Loss'

I have studied the research paper A Comprehensive Study for Center Loss. The implementation in Caffe also exists in this github repo. In the paper, the author talks about a generalized implementation ...
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46 views

Stochastic gradient descent and its variations

As I understood, SGDW and SGD + momentum is two different optimizer techniques and SGDWR is SGDW + scheduler in a form of cosine annealing with warm restart. Am I right? If not, please correct me. So,...
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1answer
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Doubt in Derivation of Backpropagation

I was going through the derivation of backpropagation algorithm provided in this document (adding just for reference). I have doubt at one specific point in this derivation. The derivation goes as ...
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Fully-Configured Deep Learning Virtual Machines in Python (VirtualBox or VMware)

Fully-Configured environment setup for Deep Learning Virtual Machines in Python (VirtualBox or VMware) Often when we start working on any new technology, the most common challenge that we face is "...
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How should I retrain the CNN for text extraction

I am working on a text extraction problem from Invoices. I want to detect various fields in the invoice like the following. I am struggling to find any dataset for invoices. I have a dataset of 150 ...
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In layperson's terms, how much does deep learning performance scale with training examples?

Baidu has answered this question empirically, but I don't have a good background in math so I don't understand the answer: Many studies theoretically predict that generalization error "learning ...
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What kind of loss function should be used for a problem like this?

My dataset consists of hierarchical timeseries. One could imagine it as "total sales" and segmentation per product. Something like this: ...
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1answer
24 views

Different results every time I train a reinforcement learning agent

I am training an RL agent for a control problem using PPO algorithm. I am using stable-baselines library for it. The objective of an agent is to maintain a temperature of 24 deg in a zone and it ...
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10 views

Gym Cartpole not solving with Cross Entropy Method?

Cross Entropy Method is considered as one of the simplest optimization algorithm which can be used for training an agent. I tried to train an agent to solve gym's cartpole environment and I have used ...
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8 views

NL2SQL task, if we have enough data, what will the model achieve for hard SQL?

We are afraid that the hard SQL like TABLE JOIN is the limit for industrial application. Addition info: https://yale-lily.github.io/spider Thank you very much.
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Why does my Deep Q Model only take a single action?

I don't know if this is the proper place to ask code-based questions on but I've been struggling with this issue for a while. Basically I am training a Deep Q Model using Keras and Google Colab (for ...
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The model only improves Precision/Recall AUC

I have a CNN model for an imbalanced image classification problem. I'm experimenting with a theory that is supposed to improve the accuracy of the model. Since I'm dealing with imbalanced data, I'm ...
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20 views

Should I remove the background of my training images?

The images in my dataset look are as below: The images have either a purple background or a white one. But the trained model (cnn) will be tested on images from the field ,that is, they will most ...
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18 views

Convolutional Neural Network for Structured Data

I am having a student dataset which is a record of student academic details I know that that CNN is mostly used in computer vision and image processing for analyzing visual imagery But here it is ...
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1answer
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What is difference between “cv2.filter2D” vs Keras “Conv2D” function

When I have to sharpen an image using opencv, I use: ...
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8 views

How to use deep reinforcement learning to learn how to play Checkers?

I am a student new in reinforcement learning and I'm trying to implement an AI able to play Checkers. I want to implement a deep learning solution. However, I am confused on how to do that. I ...
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15 views

What's the input for the cost function?

I'm trying to implement deep Q-learning, but I do not know what to put into the cost function. My net has 8 scalar inputs, 4 scalar outputs (from 0-1) and no hidden layers. To calculate the cost I ...
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Yolo-Algorithm-Training-Data Labels

I was reading many blogs about Yolo Algorithm but I have a bit confused about how we label the training data, I will write my explanation and want to know if it's right or not. In yolo algorithm, if ...