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TensorFlow is an open source library for machine learning and machine intelligence. It is developed by Google and became open source in November 2015.

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I have a collection of TEC data. My data sample for example: day1, day2, day3, day4. Case1: I have the following task to do: Training by the consecutive 3 days to predict the each 4th day. Each day ...
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I am working on a sign language recognition system that processes pre-recorded video datasets to extract hand gesture features for AI training. I am using MediaPipe, OpenCV, and TensorFlow to extract ...
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I've created this machine learning model that predicts the type of variable star based on the light curve. A variable star is a type of star whose luminosity changes over time. My model predicts ...
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I am performing a sports prediction multi-class classification problem, and wanted to compare the differences in model performance between normalised and non-normalised data. You can see the 2 ...
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Custom Keras Tuner with Time Series Cross-Validation I have written my own subclass of the default Keras tuner Tune class. Objective: I needed a way to incorporate ...
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Please help me optimize this script. I'm running it in Google Colab GPU runtime. I've implemented many optimizations but they worsened it. I need serious help. I have to submit my project in 3 days!. ...
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About the code Object detection YOLO v1 loss function implementation with Python + TensorFlow 2.x. It based on the Pytorch implementations below and re-implemented with TensorFlow based on my ...
mon's user avatar
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I have to do an experiment to predict student performance using Deep Learning. We can try applying these models and compare the accuracy, precision, recall, and F1 score. (Also, we can try only fully ...
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I am a PhD student working on a machine learning project with binary classification and RESNET architecture in TensorFlow. I believe I have done everything correctly but I am looking for some ...
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This program loads a video, detects and tracks faces in the video and detects the estimated age and gender of each person. The objective is to collect data on the number of unique people in a video ...
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Description It is valid to say, this work started and evolved from a standalone DQN implementation, which I included in an old question, to a mini-library xagents, housing 7 re-usable tensorflow-based ...
watch-this's user avatar
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I use the following code in order to assess the quality of an audio, which is based on this original-project: MOSNet. I call compute_mosnet_score() in a loop with a ...
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I'm a beginner both in Python and Tensorflow. I made a toy neural network to learn both. The objective of the NN is to identify the module c, or separation between lines defined by random points, and ...
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I am working on a Reinforcement learning project, where I have to gather a lot of data using a TensorFlow model. During the data gathering, the weights of the model do not change. So, I am using ...
kosa's user avatar
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I have code here for predicting temperature across months. This is the dataset that I was using: https://www.kaggle.com/sumanthvrao/daily-climate-time-series-data I used a SARIMA model, and looking at ...
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This code works, and I'm pretty sure it's mathematically/algorithmically correct: ...
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I trained a DQN agent using tensorflow and OpenAI gym Atari environment called PongNoFrameskip-v4, but this code should be compatible with any ...
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Context I am following this tutorial . My mission is to convert an English sentence to a German sentence using Bahdanau Attention. Summary of the Code I first took the whole English and German ...
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I have a neural network that I am training on a loss function composed of two terms (i.e. loss = loss1 + loss2). Ideally, I would like for both ...
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I am currently implementing this paper on generalised end to end loss: https://arxiv.org/pdf/1710.10467.pdf A batch of N speakers and M utterances are fed to the model which outputs an embedding ...
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I am in the process of converting this code predictor.py located here to tensorflow v2 with Python 3.7. I want to do this conversion part by part. So from the ...
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I wanted to create an image compressor using Machine Learning and started work on an "AutoEncoder". This is a type of Neural Network which takes in the image and creates a compressed vector form. It ...
neel g's user avatar
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I've created an application to predict emotions. But I think the application is being over fitted. And I cannot figure out how to solve the overfitting. I'm using a small data set of 107 images spread ...
Jack Reilly's user avatar
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I wanted a simple code review for improvement to increase the efficiency of my text generating model. This model is taken from the official TensorFlow site but is being trained on different datasets. ...
neel g's user avatar
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I am currently self-studying on Python generator object and use it to generate training data and do augmentation on-the-fly, then feed it into Convolutional Neural Networks. Could anyone please help ...
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I am working on an object detection API implemented in Tensorflow 1.14 and OpenCV 4.1, where my task is to recognize personal protection equipment (PPE) worn by workers at various construction site ...
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I am working on a multiple classification problem and after dabbling with multiple neural network architectures, I settled for a stacked LSTM structure as it yields the best accuracy for my use-case. ...
SuperKogito's user avatar
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I am trying to perform optimization using a surrogate model instead of the real function and for that, I need the gradient of my LSTM model with respect to the input. In this case, I have 3 features ...
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The code shown below takes the partial derivative of each pixel of the output fused image of a neural network with respect to input image of the neural network using tensorflow's ...
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What I want to know I am writing a ML model called U-Net with Python (and TensorFlow). My question is not about the machine learning or Tensorflow, I want to know the best structure of the code. I ...
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This is a TensorFlow regressor that tells me that if you get x score, you will have x% to win a game in x game(game is not important). Doing this as a project to learn tensorflow. ...
Daniel's user avatar
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I've implemented a self-organising map in Tensorflow's low-level API. After testing with sklearn's Iris data, the results seem correct. I did implement the algorithm also using NumPy before converting ...
Felix's user avatar
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I want to understand how resnet works also called us residual networks and I understand it better when I code one myself. I tried to find a simple implementation of resnet in the web but most I found ...
Eka's user avatar
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For my first ML project I have modeled a dice game called Ten Thousand, or Farkle, depending on who you ask, as a vastly over-engineered solution to a computer player. You can find the complete game, ...
Paul K's user avatar
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To learn the low-level API of Tensorflow I am trying to implement some traditional machine learning algorithms. The following Python script implements Principal Component Analysis using gradient ...
MB-F's user avatar
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I'm trying to solve the OpenAI BipedalWalker-v2 by using a one-step actor-critic agent. I'm implementing the solution using python and tensorflow. My question is whether the code is slow because of ...
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I am trying to develop a VGG 16 model distributed over a single server. I have one available GPU server with 1 CPU and two GPU cards. I have the code to treat them as different servers and implement ...
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I am a machine learning newbie and recently I implemented (or at least tried to implement) a linear regression model in tensorflow. I would love to know how I can improve my code: ...
whs's user avatar
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This algorithm is a convolutional deep neural network used for image recognition. I used the MNIST data set, which is a bunch of images from 0 to 9. As of right now, I have trained this image with ...
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I just made my machine learning code work a few days ago and I would like to know if there's a way to improve my code. Before I get to the implementation of the tasks at hand, I would like to ...
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Trying to recognize objects real time using TensorFlow Object Detection API OpenCV using ssd_mobilenet_v1_coco_11_06_2017 model in GPU. ...
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I am trying to learn LSTM model for sentiment analysis using Tensorflow, I have gone through the LSTM model. Following code (create_sentiment_featuresets.py) generates the lexicon from 5000 positive ...
LinuxBeginner's user avatar
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The python code below converts a tab spaced values file (.tsv) into a TFRecord in hopes that this approach expedites future time spent loading the data. Is the performance of this task correct and ...
prijatelj's user avatar
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I'm trying to implement Adagrad in Python. For learning purposes, I am using matrix factorisation as an example. I'd be using Autograd for computing the gradients. My main question is if the ...
Nipun Batra's user avatar
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The last part of my speech recognition series: finally training my network. Here's the dataset I did it with (self-generated, small I know), and the code I used. After running this code (takes ...
syb0rg's user avatar
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I've implemented the Thomson model in Tensorflow and it seems to work very well. I'm repeating some computation by computing the other half of the symmetric distance matrix. I think, for a GPU based ...
Hooked's user avatar
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16 votes
2 answers
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I have been working on this code for a while and it gave me a lot of headaches before I got it to work. It basically tries to use the MNIST dataset to classify handwritten digits. I am not using the ...
Awah's user avatar
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