Questions tagged [tensorflow]

A Python library for deep learning developed by Google. Use this tag for any on-topic question that (a) involves tensorflow either as a critical part of the question or expected answer, & (b) is not just about how to use tensorflow.

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Best way to approximate head point having only face keypoints

I'm using the BlazeFace model from TensorFlow which only has this few keypoints: I need those keypoints plus a head keypoint, like this one: My question is, which would be the best way to ...
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How to properly mask MultiHeadAttention for sliding window time series data

I have data in the shape (batch, seq_len, features) that is a time series sliding window. In essence, I'm using the most recent ...
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How to predict a mathematical progression with keras

I try the following model for a many-to-many recurrent network: ...
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Tensorflow - calling a model inside a GradientTape scope VS calling it inside a loss function

Is there a difference in the gradient computation between the two code snippets ... Code 1: ...
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Updating temporal embeddings depending on the input

I'm building a forecasting model and I'm using a temporal embedding along with a positional embedding following the same architecture as Informer. ( https://arxiv.org/abs/2012.07436 ) My problem is ...
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Predict gaps in time-series using LSTM

I have the number of timeseries with missing values (gaps). I want to train the LSTM NN for the prediction gaps task. Each time-series have the different numbers of gaps. Now, I use the mean value ...
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what is average unit activation and how to measure it?

recently I read a research paper about exponential linear units named "FAST AND ACCURATE DEEP NETWORK LEARNING BY EXPONENTIAL LINEAR UNITS (ELUS)". in section 4.1.1 they train 8 layers (128 ...
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Train a Final Machine Learning Model with Tensorflow

Based on a previous question and on this article, it is suggested that you split the data between train and test (or train/validate/test). But once you have control of your model, you should retrain ...
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Understanding tf.feature_column and dimensionality input

I hope this is the correct forum. I am going over the feature_column package of the TensorFlow [1] and have checked the code that generates a DNN using the feature_column. Assume that there is a ...
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Neural Network Regression Making Bad predictions

I have a dataset that has 83 dimentions and 300K observations for the training set. These 300K observations are standardized and fitted to a 3-layer DNN with the following parameters: ...
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Image-recognition model makes good predictions only with training examples

Im trying to use a kaggle dataset to train a model that recognizes american fingerspelling language from an image. The problem is that, built the model, if i record the screen with the examples ...
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Neural network gives very different accuracies if repeated on same data, why?

I'm running a neural network to classify audio files into 4 classes. This uses 3300 1min files split roughly evenly across classes. I split this into 80:10:10 train:validation:test sets. This trains ...
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Choosing the 'best' epoch to stop the training of neural network. Top accuracy not improving, but average is

I'm familiar with concepts like early stopping, and detection of plateau and so on. Tensorflow CNN training has a possibility of saving only best model too, according to model's accuracy metric (for ...
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Loss Jumps while Train a Fully Connected MLP

i am currently trying to train a Fully Connected MLP with vibration data from a machine aggregate for classification. During training, the loss jumps up abruptly in each epoch. Here is an excerpt of ...
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Optimizing population split predictions

I am trying to find the best way of building a NN to predict population statistics; The problem is defined by a set of $(x_i, y_i)$, such that $x_i \in R^d$ and $y_i \in R^k$. Furthermore, for each $i$...
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Using `relu` as activation function for regression with only positive values

I'm building a deep learning model to predict times of arrival. By definition, the time of arrival is always positive. I'm wondering if I can use a relu as the ...
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Why an agent can't learn on cheat data?

I want to train a FinRL model which will trade on an exchange using Ray Tune. I tried two different tune runs: with future data(you can find this code by "#Future data") and without. I ...
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What's the difference between stacked LSTM and encoder-decoder LSTM

I wanted to learn about encoder-decoder LSTM and after some digging around I get that the first LSTM layer in an encoder-decoder-LSTM outputs its hidden state and then the next LSTM layer uses that ...
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Classifying images with categorical and numerical data in a GAN

I want to create a GAN model that accepts tabular data as well as a corresponding image. The data should be trained all together. For the final processing I want to be able to pass the tabular data ...
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Convolutional neural network architecture calculation question

I'm attempting to understand the neural network architecture used in this paper: Visualizing and Understanding Convolutional Networks. Here's an image of the network achitecture from the paper that is ...
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Choosing a model for machine learning problem

Update: See bottom of question for insight into the real application which this small example is attempting to simplify. Questin: In the different machine learning libraries, what kind of model suits ...
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How to get the probability a prediction is correct from a binary classifier

I have an image binary classifier that where class a = 0 and class b = 1 When I receive a prediction of a single image, is working out the probability that the prediction is correct as simple as: a: ...
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Training MLP by early-stopping without dropout layers

I am training a multi-layer perceptron (MLP) with 4 hidden layers. I got the best hyper-parameters by the following steps using HParams: Training model by each ...
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Multiple time series forecasting: How to split the data for training of a neural network

Use case: I have sales of 90 products during the first 180 days since the product launch. I want to train an LSTM network to predict sales 4 weeks ahead given the last 7 days of sales. The model ...
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TensorFlow/keras conversation score prediction

First the question: I'm new to machine learning and have made a first attempt at predicting scores (see problem below). I'm now looking for the right way to set up the model (layers, activations, ...
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How does Walk-Forward work with LSTM

I have been looking at how to split my data for training/validation/test for a timeseries using LSTM and have some conflicting thoughts I would like to get a bit more clarity on. I came across: QA1 ...
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the value of accuracy and loss change by the order of the training

https://colab.research.google.com/drive/13wMNCXxKs_uqFVzuJqE2zxhsxWK8Ya4k?usp=sharing Hello guys I’m having a hard time trying to figure out what I am doing wrong here. I used 4 pretrained models from ...
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Getting same prediction for all the classes in mobilenetV2 - Tensorflow

I am using mobile net v2 for multiclass image classification problem, here is how I am loading the data ...
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How to shape dataset for multivariate LSTM model for 'multiple timelines'

I'm trying to make a model to predict the gross income of our company's stores based off of a 14-feature data monthly data set that I have for each store. We have 950 stores. I have no problem ...
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Why my training Precision and Recall is higher than Validation Precision and Recall?

I am training a deep learning model for binary image classification using Keras and TensorFlow. My model gave the highest ...
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Why my training Precision and Recall is higher than Validation Precision and Recall? [duplicate]

I am training a deep learning model for binary image classification using Keras and TensorFlow. My model gave the highest ...
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2 votes
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Fine-tune: ways to determine how many layers to unfreeze

How to determine amount of layers I should unfreeze while fine-tuning deep learning model? Is there any sets of rules or I should just experiment?
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3 answers
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Impose a condition on neural network

I am building a neural network model with TensorFlow and Keras in python. My model is performing well on unseen data in the way I desire and everything is fine. but the problem that I don't have any ...
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How to apply Keras Conv1D over 3D dimensional input?

Context: I'm predicting whether a machine will break down within 1 hour, and I have sensors located at 4 different parts of the machine, which give me historical readings of different metrics. ...
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Can Grad-CAM be used for CNNs with a flatten layer?

I would like to visualize CNNs with a flatten layer. I looked into Grad-CAM, which is one of the most popular visualization methods for CNNs, but I thought it could only be used for CNNs with a global ...
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Neural Network for identifying handwritten letters is super inaccurate

I am trying to make a neural network that classifies letters. I have it training and it gets to 96% accuracy but when I get it to classify things, it either guesses correctly or guesses R. When I try ...
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Why applying Grad-CAM to the input layer is not common?

Grad-CAM is a popular tool that could be applied to the last convolutional layer to understand the inner structure of the deep neural network. It is general to apply Grad-CAM onto the last ...
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How to determine the number of hidden layers a model needs? [duplicate]

I built a multi-layer perception model, from online tutorials. They are very good tutorials. However, when I come down to build my own model, with 32 variables(input layer), one output(regression ...
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Tuning Neural Network in R [duplicate]

I working in R and I try to fit neural network with TensorFlow and Keras. Generally I am not satisficed with result from model. I tried to fit this model sequential model. ...
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Many-to-many time-series prediction problem

I would like some advice on how to implement an RNN or LSTM for my problem. I am working in Keras Tensorflow. My data describes the moisture % histogram of a sample of material. There are 42 features ...
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Interpretation of plots from neural network

I am working on a Keras and TensorFlow in R and I trying to make good predictions for a regression problem. Below you can see how is look like plots two plots one for loss function and second for mean ...
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How to deal with zeros in neural network? [closed]

I am working on a Keras and TensorFlow in R and I try to make good predictions for a regression problem (not classification). In my dataset there are several features and my target variable has a lot ...
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Techniques for estimation of GEV distribution parameters [duplicate]

I am fitting distributions on observation data to make generalizations about the frequencies of different types of natural events. Right now I am focused on the generalized extreme value (GEV) ...
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What if weights of model is output of neurons?

If instead of, giving axon's weights some number value, why not give it output value from other neuron. I think, taking output from neuron in previous layer and setting it as weight in current layer ...
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Loss Function of a Variational Autoencoder when using Implicit Reparameterization Gradients (Dirichlet distributed latent space)

I would like to implement a VAE with a Dirichlet distributed latent space in Python. Since the reparametrization trick does not work for the Dirichlet Distribution I would use Implicit ...
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1 answer
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How to interpret the model weights extracted from tensorflow2 keras LSTM model?

I fitted a tensorflow.keras.layers.LSTM model and extracted the model weights via get_weights(). However, I find it hard to ...
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1 answer
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Is it possible in deep learning to train on a subset of training set in order to find the best hyper-parameters?

In classic machine learning, it is not uncommon to do a search for hyper-parameters by training different configurations on a small subset of training set. Usually, for each set of hyper-parameters, a ...
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Confidence intervals for next frame video prediction

I'm using a ConvLSTM network for next frame video prediction. The output is a deterministic prediction of an image in the future. My question is: can a ConvLSTM model give me an interval of prediction ...
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15 votes
5 answers
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Can I enforce monotonically increasing neural net outputs (min, mean, max)?

Hi I'm using a DL model (TensorFlow) to predict daily minimum, mean, and maximum values of a target dataset. I was thinking that the model would have 3 outputs for each day, (min, mean, max). Is there ...
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CNN model is not improving and has zero validation accuracy [duplicate]

I'm trying to create a model to classify images. there are 21 different classes they each have around 800 images. images are in different sizes and aspect ratios and they are resized to (64,64) pixels....
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