tensorflow accuracy not changing

rev2022.11.3.43005. notebook with my single layer model code sample, Making location easier for developers with new data primitives, Stop requiring only one assertion per unit test: Multiple assertions are fine, Mobile app infrastructure being decommissioned. Thanks! Tensorflow val_sparse_categorical_accuracy not changing with trainingTensorflow val_sparse_categorical_accuracy . When the migration is complete, you will access your Teams at stackoverflowteams.com, and they will no longer appear in the left sidebar on stackoverflow.com. Why does the sentence uses a question form, but it is put a period in the end? To learn more, see our tips on writing great answers. Is it possible to leave a research position in the middle of a project gracefully and without burning bridges? Converting Dirac Notation to Coordinate Space. Find centralized, trusted content and collaborate around the technologies you use most. Same some of the inputs that are supposed to be marked as 1, were marked as 0. The code is: I tried playing a lot with the optimizers and activation functions, but the only thing that worked was Batchnormalization1. One-hot encoding the target variable using nputils in Keras, solved the issue of accuracy and validation loss being stuck. What exactly makes a black hole STAY a black hole? Thanks for contributing an answer to Stack Overflow! The benchmarks will take some time to run, so be patient. I have tried one hot encoding of binary class, using keras.utils.to_categorical(y_train,num_classes=2) but this issue does not resolve. Get More Data. For example, removing ops, adding attributes, and removing attributes. If accuracy does not change, it means that all your model is learning is to be more "sure" of results. What's a good single chain ring size for a 7s 12-28 cassette for better hill climbing? In a tutorial I found this mnist classification code: This code runs, and I get the result as expected: Up to this point everything runs perfectly, however when I apply the above algorithm to my dataset, accuracy gets stuck. Reason for use of accusative in this phrase? To learn more, see our tips on writing great answers. For increasng your accuracy the simplest thing to do in tensorflow is using Dropout technique. See the Keras example on RNN and LSTM. Why does Q1 turn on and Q2 turn off when I apply 5 V? Find centralized, trusted content and collaborate around the technologies you use most. as recommended in Ordering of batch normalization and dropout?. 2022 Moderator Election Q&A Question Collection. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Then, freeze the base model. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. The main difference I see between your approach and mine is that I: See this notebook with my single layer model code sample. rev2022.11.3.43005. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. I'm new to keras and tensorflow, I have a model that I am trying to train where the loss does not change after epoch #1. my data is the sequence of numbers which I want NN to learn and predict the next number: For example I want [30, 36, 28, 25, 30] to be my input and 35 to be my output. softmax is a squashing function whose range is 0 to 1. What's a good single chain ring size for a 7s 12-28 cassette for better hill climbing? It looks like it is massively overfitting and yet only reporting the accuracy values for the training set or something along those lines. For questions on how to work with TensorFlow, or support for problems that are not verified bugs in TensorFlow, please go to StackOverflow.. The rest was the same 0.57. code: def model_and_print (x, y, Epochs, Batch_Size, loss, opt, class_weight, callback): # fix random seed for reproducibility . I have 8500 training images and 500 validation images. How to draw a grid of grids-with-polygons? I'm not sure if that means my model is good because it has high accuracy or should I be concerned about the fact that the accuracy doesn't change. Playing around with the learning_rate might yield better results, but it could be that your network is just too complex (computing a super non-convex function) for simple Gradient Descent to work well here. Not the answer you're looking for? After clearing up the data now my accuracy goes up to %69. Validation Accuracy Not Changing. A stddev=1.0 is a huge value, and it alone can make your NN go astray. How many characters/pages could WordStar hold on a typical CP/M machine? Examples include tf.keras.callbacks.TensorBoard to visualize training progress and results with TensorBoard, or tf.keras.callbacks.ModelCheckpoint to periodically save your model during training. Also I tried the other optimizers in your link, however the result was the same. By mistake I had added a softmax at the end instead of sigmoid. Why is SQL Server setup recommending MAXDOP 8 here? Change it to stddev=0.01 for all your initial weights: Other than that, as already suggested in the comments, a learning rate of 0.0001 seems way too small here (given how slowly the loss is decreasing); experiment with higher values (0.01 - 0.001). Specifically, since you added sigmoid as your last activation function, I believe you are suffering from a vanishing gradient problem. @MuratAykanat Try increasing your # of epochs much more, like 1000 or 5000. So, I just converted it to values around 0 and 1. This may be an undesirable minimum. How to draw a grid of grids-with-polygons? I'd think if I were overfitting, the accuracy would peg close or . SQL PostgreSQL add attribute from polygon to all points inside polygon but keep all points not just those that fall inside polygon, Employer made me redundant, then retracted the notice after realising that I'm about to start on a new project. Keras mixed model gives same result in every epoch, How to solve constant model accuracy after each epoch, Tensorflow: loss and accuracy stay flat training CNN on image classification, LSTM Training Loss and Val Loss not changing, Machine Learning Stock Prediction model not improving accuracy. rev2022.11.3.43005. Are there small citation mistakes in published papers and how serious are they? Is it OK to check indirectly in a Bash if statement for exit codes if they are multiple? Kennet Belenky Asks: Tensorflow val_sparse_categorical_accuracy not changing with training I'm having trouble understanding the behavior of the validation metrics when calling Model.fit. I had the exactly same problem: validation loss and accuracy remaining the same through the epochs. between your hidden layers. That line would OneHot Encode the labels as mentioned by. How can we build a space probe's computer to survive centuries of interstellar travel? Do that a few times if necessary. @runDOSrun Are you sure that is related to hyperparameters? Other than that I don't spot any immediate issues, but debugging a neural network implementation can be pretty tricky sometimes. To use the suite, you will need to install TensorFlow and the suite itself. ESM-2/ESMFold ESM-2 and ESMFold are new state-of-the-art Transformer protein language and folding models from Meta AI's Fundamental AI Research Team (FAIR). I've built an NVIDIA model using tensorflow.keras in python. Using TensorFlow backend. 2022 Moderator Election Q&A Question Collection, Keras convolutional neural network validation accuracy not changing. I agree with @cyniikal, your network seems too complex for this dataset. I implemented the unet in TensorFlow for the segmentation of MRI images of the thigh. # TensorFlow and tf.keras import tensorflow as tf # Helper libraries import numpy as np import tensorflow.keras.applications as applications INPUT_SHAPE = (32, 32, 3) fashion_mnist = tf.keras.datasets . Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. This column had huge value. i have a vocabulary of 256 and a sequence of about 166000 words. [x ] If running on Theano, check that you are up-to-date with the master branch of Theano. About the changes in the loss and training accuracy, after 100 epochs, the training accuracy reaches to 99.9% and the loss comes to 0.28! I have absolutely no idea what's causing the issue. I would really appreciate it if someone can help me. The basic model is here: class BasicModel(Model): def __init__( self, rating_weight: float, retrieval_weight: float, product. tmp = tf.argmax(input=mvalue, axis=1) an_array = tmp.eval(session=tf.compat.v1.Session()) It gives me predicated labels however, I want to have an accuracy value. Try doing the latter. Not the answer you're looking for? How often are they spotted? I don't understand why I am getting the same result. There may be many possible causes here (and we don't have your data), but, according to my experience, a frequent mistake in such cases is initializing the weights with the default argument of stddev=1.0 in tf.random_normal() (see the docs), as you do here.. A stddev=1.0 is a huge value, and it alone can make your NN go astray. Scores are changing, but none is crossing your threshold so your prediction does not change. Should we burninate the [variations] tag? Please take a look at the help center. This is especially useful if you don't have many training instances. As its currently written, your answer is unclear. That would give some improvement, although it would be very small. Hi I wanted to implement a neural network for student admission dataset and output of the model and also loss doesn't change much n_features = I had similar problem. rev2022.11.3.43005. But when i train, the accuracy stays the same at around 0.1327 no matter what i do, i tried changing learning rates and batch_size. I've tried heavy dropout on the fully-connected layers, on all layers, on random layers. VGG19 model weights have been successfully loaded. What is the function of in ? A minimal dataset with 30 examples in 30 categories, one example in each category. Why does it matter that a group of January 6 rioters went to Olive Garden for dinner after the riot? Stack Overflow for Teams is moving to its own domain! Arguably the network's structure isn't ideal for this problem, but that is beside the point. Is cycling an aerobic or anaerobic exercise? Also accuracy is not a valid metric for regression. try batch_size=50 and steps per epoch = 170 that way 170 X 50 =8500 so you go through your training set once per epoch. If you are reporting a vulnerability, please use the dedicated reporting process.. For high-level discussions about TensorFlow, please post to discuss@tensorflow.org, for . How to interpret the output of a Generalized Linear Model with R lmer, Horror story: only people who smoke could see some monsters. Using weights for balancing the target classes further improved performance. If it still doesn't work, divide the learning rate by 10. Keras Maxpooling2d layer gives ValueError. What is the best way to show results of a multiple-choice quiz where multiple options may be right? Making location easier for developers with new data primitives, Stop requiring only one assertion per unit test: Multiple assertions are fine, Mobile app infrastructure being decommissioned. 1. I fixed the ImageTools.py below as: Now I actually get grayscale pixel values from 0 to 255, so now my dividing it by 255 makes sense. How can we create psychedelic experiences for healthy people without drugs? I faced same problem for multi-class, Try to changing optimizer by default it is Adam change it to sgd. [I normalized all my data using StandardScaler() but it didn't change.]. How to generate a horizontal histogram with words? from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D from keras.layers import Activation, Dropout, Flatten, Dense from keras . My last try, inspired by monolingual's and Ranjab's answers, worked. and this is my very simple code and NN: I have built a tensorflow model and am getting no change in my validation accuracy in different epochs, which makes me believe there is something wrong in my setup. If running on TensorFlow, check that you are up-to-date with the latest version. This can be easily fixed by changing the structure of the model so that this step is unnecessary. floating-point numbers. Fixing that solved it for me. I've built an NVIDIA model using tensorflow.keras in python. Does squeezing out liquid from shredded potatoes significantly reduce cook time? Anyway, combined with changing the trashold, that that is done after you have altready trained the classifier, if you have unbalanced (but that's usually high accuracy and low recall of the minority) consider oversampling the smaller class or undersampling the other. Checkpoints exist in various sizes, from 8 million parameters up to a huge 15 billion . I've the same problem as you TensorFlow is an end-to-end open source platform for machine learning. TensorFlow 2 quickstart for beginners. I tried changing the network, adding more epochs, but I always get the same result no matter what. 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