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Questions tagged [neural-network]

Artificial neural networks (ANN), are composed of 'neurons' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network designer having had a model of a real system.

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11 views

Softmax gives output vector whose sum is greater than 1 in Pytorch

I am a newbie to PyTorch. I was trying out the following network architecture to train a multi-class classifier. I used Softmax at the output layer and cross entropy as the loss function. However, the ...
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9 views

BERT for non-textual sequence data

I'm working on a deep learning solution for classifying sequence data that isn't raw text but rather entities (which have already been extracted from the text). I am currently using word2vec-style ...
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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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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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1answer
22 views

What is difference between feed forward neural network and lstm?

What is the difference between feed forward neural network and lstm? How do they differ in their architecture?
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2answers
29 views

How to handle weekdays in a NN?

I want to test if using additional information of weekdays would improve my NN. Therefore, I just converted the weekdays numerically such as ...
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33 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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8 views

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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6 views

What point processes can I use to model Spikes? [on hold]

I would like to know in Python. I'm new to Python and I don't know how to proceed.
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2answers
39 views

Understandable and explainable machine learning model

I want to find formula for best financial portfolio. Inputs: Historical fundamental data for last 15 years. For 3000 companies for every quatal we have things like ...
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1answer
27 views

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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2answers
43 views

Why activation functions used in neural networks generally have limited range?

Why do we generally use activation functions with only limited range in neural networks? for e.g. $sigmoid$ activation function has range $[0, 1]$ $tanh$ activation function has range $[-1, 1]$ Q1) ...
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14 views

Can I use/modify an Autoencoder to handle missing data?

I am about to implement an Autoencoder to detect anomalies. Therefore, e.g., in my test set, there is a situation where the data stream broke for some days. This results in a lack of data and should ...
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26 views

Python Neural Network choose correct text

Hey I am new into the neural network scene. I would like to create a network which takes as input several file names. To train it I would say which files are usable and which are not. For example: ...
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1answer
35 views

How can different classification algorithms expressed as neural networks?

I have heard that each of the different classification algorithms can be expressed as a neural network architecture. How can the different algorithms like Logistic Regression, SVM(Support Vector ...
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7 views

Understanding the output of embeddings and an error message when I change a parameter in the embedding layer in Keras/tensorflow 2.0 [closed]

I am trying to understand and familiarize myself with embeddings. I created an artficial dataset of 5000 observations comprised from the union of 5 datasets: Sample of 1000 values drawn from a ...
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1answer
28 views

Advice on machine learning for small inputs and outputs

I am planning on using a machine learning algorithm to learn the mapping between sets of four coordinates (x,y,z + a distance d ...
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18 views

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
49 views

What model should I use for multiple time series input

I want to predict bacteria plate count from time series(around 10000 values in a row) of water temperature in the water on a one minute granularity, and other daily climate data including min and max ...
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54 views

Results are too good.. what is wrong? How to predict correctly?

I am about to evaluate a neural network and want to check whether the predictions make sense. The variables: ...
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0answers
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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11 views

Keras Custom Loss Function

I am looking to design a custom loss function for Keras model. The model itself is neural network that accepts a set of images and is supposed to run a regression to get an output, which is a value. ...
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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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25 views

Time series forecasting produce same values with different training data

I'm developing a python program which predict daily timeseries values. Each daily timeseries contains 288 values (a record every 5 minutes). The main idea is to train a LSTM model with 7 days data ...
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1answer
34 views

How do I know how to construct the layers of my CNN

I've done a CNN project with Keras and OpenCV, and I've got roughly 65% accuracy. And now I have to present this work in my University, but I'm afraid if the teachers ask me for how do I knew how to ...
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43 views

Neural Network for text generation

I'm new into ML and NN and I would like to figure out what is the best way to solve this (initially little) problem. Suppose i have a {number} in input and i want as output the phrase "The number in ...
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23 views

Understanding reduced dimension embedding from tabular data

Background I am working on building a collaborative filtering recommender system in Keras for a school project, following an approach from this article. The approach is to take tabular user, item ...
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9 views

DL classification loss lowers but doesn't go to zero

I'm trying to train a text classifier using pytorch and the model currently uses pretrained embeddings, a bi-lstm followed by a linear layer and dropout. When I start training the loss is at 60-70, ...
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12 views

How is this function (for updating a Stochastic Gradient Descent model) called without a parameter?

I'm in the middle of a Deep Learning Course offered by DataCamp and the example below was given for optimizing a SGD model: As you can see, the function "get_new_model" requires one parameter: "...
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2answers
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What's the meaning of precomputed features?

When i learn about deep learning, I found dataset with precomputed features form. Link (http://cs.stanford.edu/people/karpathy/deepimagesent/coco.zip). What's the different with usual dataset?
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42 views

How to use additional variables that are not available in test set?

I have additional variables in my dataset that are somewhat correlated to the continuous target variable, but that are completely unavailable in the test set. So, I'm wondering how the best to use ...
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1answer
16 views

Can an R^2, or coefficient of determination be used on non-linear data?

I have used the $R^2$ metric to determine how well my neural network performs a non-linear regression. And it seems to work. The plots look almost identical, and I get an $R^2$ value of 0.93... it ...
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22 views

Recurrent Python Neural Networks - Reshape () Error [duplicate]

The script below creates the arrays for data prediction using recurring neural networks, If I set the period to 4, the script runs, but I have a 5 value input, how to fix my reshape? DATA SET ...
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47 views

For loop and assignment procedure in losses.py for customizing loss function of ML model

I want to customize a loss function for neural networks. A computation of the loss function requires the inclusion of a for loop like below. However, it is super difficult to implement because ...
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27 views

How does the distribution of “features” in the hidden layers of neural networks work?

I would love some additional help understanding the hidden layers of neural networks. I’ve read that each of the neurons of the hidden layers will relate to various features from the given data set. ...
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12 views

can i get weights per iteration of MLP?

im building an mlp with scikit learn. Is there a way I can access weights and biases of the output layer per iteration? There is an option mlp.coefs_ But it ...
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2answers
31 views

how to access weights of individual Neurons in the output layers in MLPs?

im working on a neural network using Keras. Its an mlp(multi-layer perceptron). With 8 Neurons in the output layer. Is there a way I can access weights and biases of individual neurons of the output ...
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2 views

Best DDPG use case when having sensor historical data and virtual environment of a real world system

I have a historical data (from real sensors) which have enough knowledge of the actions and states needed for the use of reinforcement learning and a modeled virtual environment of a real system (...
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13 views

What will happen if we replace the transformer of BERT to evolved transformer?

If we replace the official BERT's transformer to evolved transformer, do the change accelerate the inference speed without losing accuracy?
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1answer
26 views

Structure of LSTM gates

It is my impression that a single layer LSTM architecture consists of $t$ LSTM cells that are identical duplicates, where $t$ is the number of time steps. Then there are gates within the LSTM cell. I ...
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31 views

Why are discriminative models denoted as P(Y|X?

In general, we can speak we know deterministic and probabilistic models. Discriminative ones are deterministic, while generative one is probabilistic. But I have been reading about the difference ...
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15 views

Binary classification of graph pairs

I have a dataset made of pairs of graphs and a binary label (0 or 1 depending on if the graphs are similar). I am trying to find a model that, when given two graphs, will output if these two graphs ...
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15 views

normalization/standardization of input/output of autoencoder and Gaussian Process

I have two machine learning algorithms that deal with time series data. My data consist of 1500 time series, each of 500 time components. The first machine learning algorithm is an autoencoder, ...
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1answer
21 views

Back-propagation and stochastic gradient descent

Is backpropagation a learning method or an optimisation method? How are backpropagation and stochastic gradient descent related to each other?
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1answer
67 views

Grid search or gradient descent?

Assume we have a neural network and one if its activation functions is a function of parameter a. We want to find the weights and parameter a that leads to the minimum loss on the validation set which ...
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1answer
31 views

Deep Learning for non-continuous dataset

I am working with this dataset which is record of student academic details and I want to predict the student's performance. since the dataset is non-continuous I cannot apply CNN on this dataset. ...
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0answers
17 views

How to approach mapping families of vectors on a lattice and forecast resulting value

I describe here a model to describe how neighbours influence a node. I wish to implement it to attempt forecasting to values associate nodes; I post here asking for suggestions on mathematical model ...
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16 views

LSTM pulse signal prediction

i'm trying to capture long-term dependencies using LSTM, by creating a unit pulse signal every 62 points. The idea is to go back 62 time-steps and copy the value for the next time-step, so as to ...
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0answers
16 views

What's the meaning of having a UNK token for out of vocabulary words during decoding?

Adding a UNK token to the vocabulary is a conventional way to handle oov works in tasks of NLP. It is totally understandable to have it for encoding, but what's the ...