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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6 views

LSTM - unable to get a 3D output

I have an array with shape (55834, 250, 30) and I'd like to get an output of the same shape from this LSTM model. ...
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Error during the compilation of a neural network in Vitis AI, "Not found op in super_const_dict: name: Decoder_Section_1_UpConv_1/kernel"

I'm following a Xilinx Tutorial about the implementation of a Neural Network in a System on Chip (ARM Processor + Xilinx FPGA) and I have come up with an error during the compilation step. I've ...
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18 views

How do I feed my keras model in batches?

I am trying to feed a Sequential model in batches. To be reproducible my example, suppose my data is: X=np.random.rand(24,432) Y=np.random.rand(24,432) My goal is ...
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How to decide number of hidden layers and number of neurons for Autoencoder for dimensionality reduction function?

I have been looking into deep learning and what caught my attention is the implementation of Autoencoder as a dimensionality reduction function for anomaly detection. I found out about it through the ...
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What effect repetitive data will have on the performance of the model

I understand that my question is very broad and that the correct answer may depend on various things. I want to get an idea in general what we may expect if we have repetitive data in our dataset. ...
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Huggingface - TypeError: 'TensorSliceDataset' object is not subscriptable

I'm trying to make my own model for translate a language to another with T5ForConditionalGeneration and Huggingface using no pretrained model (I need to use my own dataset and tokenizer because no ...
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5 views

Soft-NMS during Inference

I have read the paper about the soft-NMS and the problem that tries to solve. Does it make sense to use this algorithm during inference apart from training? It smooths the predictions on the bounding ...
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How does Attention work in LSTM and Autoencoder

So can someone explain me concept of Attention in LSTM and also Autoencoder how it works and where it's used. I saw multiple video but it's still super confusing to me. PS: I'm grad school student so ...
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Predict value from array (Assisted Sequence Prediction)

TL; DR How to develop a machine learning implementation (in python) that given an array of values is able to predict its maximum percentage of increase? [11.082, 11.052, 11.052, ... 11.202, 11.202, 11....
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Custom vocabulary with Transformer model in Trax

I'm currently working on a programming language translation problem (NMT) using a transformer model with Trax. So I basically need to convert a language A to a language B. In order to tokenize the ...
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Data Art/ Data Visualization Art/ Information Art

Few days ago, I learned about data art/ data visualization art/ information art. I think I have interest in it. I want to see how I can use my data science skills in this area. However, I don't know ...
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Can a reformer model really handle long-range dependency?

I read this article about new attention model called Reformer. Here is the main strength of this model: The Reformer pushes the limit of longe sequence modeling by its ability to process up to half a ...
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Is the explained variance a good metric for autoencoders?

I want to evaluate how an autoencoder will perform on my data. Now, I can do this with the mean squared error of the decoded data compared to the original data, and this is fine when comparing this ...
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How to use Softmax and CrossEntropyLoss in a classification problem?

I am new in the deep learning field and I would like to go in to understand some concepts. I started learn with pytorch and I have the next question: I constructed my own dataset that contains images ...
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1answer
22 views

Robustness vs Generalization

I don't quite understand the difference between robustness and generalisability in relation to image processing (CNN). If my model generalises well, it is also robust to changes in the image material. ...
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Should I reshuffle the training set when benchmarking neural networks?

I'm trying to set up a fair benchmarking between various RNN models, where each of them is trained until convergence with a fixed random seed. Because the task is very costly, I am only able to run ...
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18 views

Derivatives in Backpropagation [closed]

https://campus.datacamp.com/courses/introduction-to-deep-learning-with-pytorch/introduction-to-pytorch?ex=6 OR https://www.youtube.com/watch?v=J-g9mf3WgVA&ab_channel=DataCamp At 2:48, can anyone ...
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10 views

How to represent source code in NLP for tasks like code retrieval?

How to represent code features in NLP? Can code be treated like a language and pour into the neural network? Are there some work, paper or material around this topic?
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how to in enhance A3C entropy?

I'm trying to implement this A3C code in my custom environment, and I have a basic understanding of the algorithm. The algorithm worked, but it did not give me a good performance. I looked into ...
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11 views

How many layers/ convolutions does DensNet-169 have?

I am a little confused by the numbers. From the Digram in the paper I count 312 convolutions without the transition layers. Can anybody help understand how the 169 in the name is calculated?
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How can I create an XOR Gate using two hidden layers and one output layer (besides the or gate)?

Here is my nueron class with activation function (sigmoid), fn for printing truth table and inputs Mathematically and_gate + nor_gate + nor_gate are giving me the correct output. But the function is ...
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High accuracy in mode.fit but low precision and recall. Overfit? Unbalanced? Error?

Hello ive been training a CNN with keras. A binnary clasificator where it says if a depth image has a manhole or not. Ive labeled manually the datasets with 0 (no manhole) and 1(it has a manhole). I ...
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Why deep learning models still use RELU instead of SELU, as their activation function?

I am a trying to understand the SELU activation function and I was wondering why deep learning practitioners keep using RELU, with all its issues, instead of SELU, which enables a neural network to ...
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21 views

How do dilated convolutions used for upsampling the inputs in FCNs?

I am reading the paper (Long et al., 2015) on fully convolutional networks (FCNs), and I came across the section where the authors describe dilated convolution as the trick to compensate for the cost ...
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1answer
15 views

predict a binary vector of size 40

I have a dataset of shape (2600, 95) with first 55 columns are features and 40 columns are label. Label is a binary matrix of size 10x4 that flattened, and features are real valued numbers ranging (0....
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1answer
40 views

Splitting the dataset manually for k-Fold Cross-Validation

I manually divided the dataset into three sets: train, test, and validation. Each set includes several folders, one for each patient. Each patient has many images from a different point of view. As a ...
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8 views

How to improve accuracy? BERT

Dataframe: ...
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Confused about Generalized Zero shot learning Validation dataset creation

Sorry for the naive question but I am really struggling to understand how to create the Validation dataset in the case of GZSL. For Zero Shot learning, I can imagine we pick certain classes from the ...
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Why does my Yolov1 output tensor contains negative value?

I have been working on my own imlplementation of the Yolov1 model. The first thing I want to mention is that It seems to be learning. Here is the train/val curves : (<0.1 train and 0.26 val loss) ...
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62 views

Input Features of a Hierarchical Structure

I have input features of a hierarchical structure. Each feature consists of a header element and 0 to n subfeatures of the same structure. Also, there is no upper limit for n and n can be different ...
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15 views

Differentiation of Learning Capabilities of Different Networks

I have a conceptual problem regarding the overall learning capability of a neural network differentiated by the different types of input that we can give to the network. Suppose that we have a ...
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16 views

Why are the results from the keras validation split different from sklearn metrics?

I am training a Keras model, and running: model.fit(X, y, epochs=10, validation_data=(X_test, y_val)) I'm using AUC, precision, and recall as the metrics (also ...
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1answer
24 views

Difference between ReLU, ELU and Leaky ReLU. Their pros and cons majorly

I am unable to understand when to use ReLU, ELU and Leaky ReLU. How do they compare to other activation functions(like the sigmoid and the tanh) and their pros and cons.
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1answer
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How to reduce RMS error value in regression analysis & predictions - feature engineering, model selection

There's this dataset containing the metadata of Twitch's top 1,000 streamers of 2020. You can have the details here. I am currently participating in a challenge to predict the values for Followers ...
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Is there any mathematical basis for distributed training of a neural network on multiple machines?

Let us say that I have 50 people download a program that engages in supervised training of a neural network I designed. The data samples being provided are pretty random and the correct outputs are ...
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15 views

when depthwise separable convolution should be preferred over normal convolution?

As a novice in the realm of deep learning, I recently learned about Depthwise Separable Convolution. I have seen some tutorials and articles about it on internet, and in all of them the author ...
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1answer
29 views

Trained model performs worse on the whole dataset

I used pytorch as the training framework and the official pytorch imagenet example to train the image classification model with my custom dataset. My custom dataset has 2 different label (good and bad)...
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29 views

Can Adagrad or Adam be used in loss function with l1-norm regularization?

there is one question for me. I want to know that how Adam or Adagrad treat l1-norm regularization in loss-function? (e.g. Lasso) I know that l1-norm is not differentiable function at zero but we can ...
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8 views

How does 1 x 1 convolution operation help in finding cross channel patterns?

1 x 1 convolutions are very popular and I see that they are mostly used as bottlenecks. They help in dimensionality reduction which is why architectures like GoogLeNet use it. Xception net uses it for ...
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Precision and Accuracy of a custom Object Detection Models usind networks from TensorFlow Model Zoo

I am trying to develop a model with three classes. To do so, I tried to develop a model with different combinations of the data samples in each class. For example: the $1^{st}$ model has 500 images ...
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21 views

Multi-arm bandits

I would like to model a problem as a multi-armed bandit problem. In the data we have contextual information (user demographics, preferences, etc.) but this contextual information of each user is not ...
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1answer
29 views

after overcoming the overfitting, how to increase training accuracy?

I am building a CNN using keras for a classification task. I started with a simple model as a starting point and as almost all ML problems go, especially if the dataset is not very big, I faced an ...
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135 views

Convolutional Neural Network for Signal Modulation Classification

I recently posted another question and this question is the evolution of that one. By the way I will resume all the problem below, like if the previous question didn't ever exist. Problem description ...
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8 views

Loss & accuracy curves from learning rate range test interpretation

I am working on a project doing experiments with the Learning Rate Range Test (See "A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and ...
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Deep autoencoder: validation loss doesn't change

I'm trying to understand autoencoders and reproduced some code from Keras documentation: ...
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2answers
88 views

Best approach for text classification of phrases with little syntactic difference

So I have the task of classifying sentences based on their level of 'change talk' shown. Change talk is a psychology term used in counseling sessions to express how much the client wants to change ...
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13 views

ValueError: Input 0 of layer dense_123 is incompatible with the layer: expected axis -1 of input shape to have value 20 but received input with shape

I was trying to use keras to build a fully connected neural network to predict the winner of men 100m race. For simplicity sake, my data $X$ consists of 6 races (so number of training data = 6), each ...
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Identify areas within a shape/polygon with Vision / ML

Given a shape, in the format of a binary image, I would like to detect and subdivide it to new areas. Below is an attached example of such a shape and the expected outcome where each new area is ...

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