constant accuracy keras

Keras loss: 0.0000e+00 and accuracy stays constant. model.add(Activation('softmax')) print score, Anyone meets the same problem? django-models 110 Questions flask 163 Questions for-loop 112 Questions function 114 Questions html 132 Questions json 181 Questions keras 154 Questions list 444 Questions loops 106 Questions machine-learning 133 Questions . When I changed optimization methods from Adam to RMSprop, it was run but I refreshed all kernel and restart I took the same issue. Thanks to :https://stackoverflow.com/questions/51581521/accuracy-stuck-at-50-keras, @sayedathar11 This is time-series data so perhaps I need to adjust the model somehow? @talentlei Have solved the problem I stuck in the same situation when I use RNN but I don't know how to solve it. from keras.preprocessing.image import ImageDataGenerator. 2. Have you solved the problem? You might find it useful to change to 'sigmoid'. I went into my image directories to check if my two different classes are mixed, and they are not. What is the issue? The way I think about it is that if there are certain sections that are contributing a lot to a correct result, the optimizer could ignore everything else. and then define num_classes at the start of your code for better flexibility and readability. The more you have the more "flexible" it can be, i.e. for layer in model.layers[75:]: x = GlobalMaxPooling2D()(x) If you use SparseCategoricalCrossentropy instead as loss it should work. model.compile(loss='binary_crossentropy', Your validation accuracy on a binary classification problem (I assume) is "fluctuating" around 50%, that means your model is giving completely random predictions (sometimes it guesses correctly few samples more, sometimes a few samples less). Hyperparameters are the variables that govern the training process and the topology . It only takes a minute to sign up. To learn more, see our tips on writing great answers. This leads me to believe that the issue is not with the actual model code and somewhere in the pre-processing. model.compile (optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy (from_logits=True), metrics= ['accuracy']) After this you . horizontal_flip=True, Generally, your model is not better than flipping a coin. hey, I'm new at deep learning especially CNN. To me it seems like I missed a step, but when calling load_weights on the model it was corrected. Our website specializes in programming languages. While training a model with this parameter settings, training and validation accuracy does not change over a all the epochs. I changed again RMSprop to SGD. valid_path = "D:/data/valid*. Epoch 13/15 The reason is that my validation set has 2500+ observations for a dataset of size like this, as long as there's change in the weights (and theres is since the training error is decreasing), there should be change in the val_loss, either positive or negative. I noticed later on while trying to predict results that my predictions were heading towards 0, with them coming closer the longer I trained. Mobile app infrastructure being decommissioned. Handling Overfitting and Underfitting problem. Tags: machine-learning keras neural-network time-series lstm i want to optimize my autoencoder network but i have no idea how to do that. I am new to Neural Networks and currently doing a project for university. brightness_range=(0.2,2.5), When I train the network, the training accuracy increases slowly until it reaches 100%, while the validation accuracy remains around 65% (It is . x_valid = np.array(x_valid, dtype="float")/255.0, #Creating array for Labels Wordpress . . This metric creates two local variables, total and count that are used to compute the frequency with which y_pred matches y_true. I have (2364, 256, 256, 3) shaped data of rgb images and (2364, 8, 8) shaped labels. img_channels = 3, #Creating array of training samples Turns out, I just needed to let it train for a long time before it started to find where the loss was decreasing. training_data=[] *" 50/472 [==>] - ETA: 0s - loss: 0.5385 - acc: 0.7400Epoch 02817: val_acc did not improve Why is proving something is NP-complete useful, and where can I use it? Just a note, always try to keep a variable to handle number of classes, something like. @hadisaadat reduce ur learning rate and try for a few smaller learning rates. Some of the samples did not have enough entries so they are zero-padded to the correct size. I've narrowed down the issue to not enough training sequences (around 300). I get the output that I posted below. I discovered it after debugging my preprocessing step in which I tried to write some of the images in a disk. I have tried reducing the learning rate, increasing the learning rate, tried both sdg and adam optimizers. x=Dense(1, activation= 'sigmoid')(x) For some reason my accuracy and loss are exactly the same for every epoch. If the letter V occurs in a few native words, why isn't it included in the Irish Alphabet? Were you able to resolve ? What does puncturing in cryptography mean. train_array=cv2.resize(train_array,(img_rows,img_cols),3) To learn more, see our tips on writing great answers. Before I was knowing that this is wrong, I did add Batch Normalisation layer after every learnable layer, and that helps. How can we build a space probe's computer to survive centuries of interstellar travel? 2. Accuracy started at 0.5 and averaged around that on both training and validation data for the 120 epochs that I trained. In the next example, we are stacking three dense layers, and keras builds an implicit input layer with your data, using the input_shape parameter. I used to face the same result before. I tried to increase number of nodes, number of layers but with no progress. model.add(Dropout(0.4)), model.add(Dense(256, activation='relu')) Then, go through the accuracy code with the ability to manually inspect the values of the matrices. Keras: acc and val_acc are constant over 300 epochs, is this normal? . model = Sequential() Do any Trinitarian denominations teach from John 1 with, 'In the beginning was Jesus'? By clicking Sign up for GitHub, you agree to our terms of service and It seems that your model is not able to make sensible adjustments to your weights. x=Dense(1024,activation='relu')(x) Stack Exchange network consists of 182 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Should we burninate the [variations] tag? 18272/18272 [==============================] - 115s - loss: 0.0314 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286 Go with the suggestion given by @kodon0 . 1 Answer. 18272/18272 [==============================] - 114s - loss: 0.0312 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286, @talentlei Creating a sequential model in Keras. If I keep the number of neurons in the output layer and use sigmoid, for each epochs, there is no change in the accuracy. Reason behind should be due to vanishing gradient. Keras is a Python library for deep learning that wraps the efficient numerical libraries Theano and TensorFlow. But, I saved the weights after an epoch and then when I loaded the weights and continued training, everything worked. data_augmentation = True, img_rows, img_cols = 224,224 I'm currently doing the Udacity Self-Driving Car Engineer Nanodegree course; my cohort is currently doing the behavioral cloning lab. Does the Fog Cloud spell work in conjunction with the Blind Fighting fighting style the way I think it does? I have always been under the impression that resnet is more technologically advanced than vgg and so you will always get better performance on resnet . nb_classes = 2 do you know what is the function of these two? print ("Loss = " + str(eval[0])) To get started, open a new file, name it cifar10_checkpoint_improvements.py, and insert the following code: # import the necessary packages from sklearn.preprocessing import LabelBinarizer from pyimagesearch.nn.conv import MiniVGGNet from tensorflow.keras.callbacks import ModelCheckpoint from tensorflow.keras.optimizers import SGD from . Retraining with the same data returns different accuracies. I have 101 folders from 0-100 containing synthetic training images. My personal go-to is VGG19. Thanks! I prefer women who cook good food, who speak three languages, and who go mountain hiking - what if it is a woman who only has one of the attributes? I used Keras for CNN model on the Kaggle platform with GPU. High, constant training loss with CNN. Ask Question Asked 5 years, 7 months ago. Is it normal for acc and val_acc to stay constant like this? Loss value going down while accuracy remains constant? model.compile (optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy (from_logits=True), metrics= ['accuracy']) After this you should adjust the last layer to: So turns out your loss might be the problem after all. I've been using many kinds of architecture but the val_loss really high and val_acc really low. I've done this in MATLAB with and without any data preprocessing, and both have very good prediction results, so I'm at a loss for what to do. The simplest model in Keras is the sequential, which is built by stacking layers sequentially. This frequency is ultimately returned as binary accuracy: an idempotent operation that simply divides total by . Why l2 norm squared but l1 norm not squared? 2. We were given a dataset of approximately 20k+ features and labels; I take it and augment it with flipping - so I have about 40k of data. I just want to say thank you ahead of time. @andrew-ayers Did you manage to solve this issue? learn better, but that means more parameters. @prabaHridayami I would recommend using a pre trained and well studied architecture for feature extraction and then fine tuning the layers on the top. model.add(Dense(n_class,activation='softmax')) #where n_class is number of classes Thanks to :https://stackoverflow.com/questions/51581521/accuracy-stuck-at-50-keras. BinaryCrossentropy: Computes the cross . With Dropout the optimizer is forced to focus on many different places. I divide my pixels by 255 (as is customary) but can still see what the image looks like when plotting it. So I increased the learning rate and loss started around 5.1 and then dropped of to 0.02 after the 6th Epoch. Indian Institute of Technology Kharagpur. I am doing sentence classification task with variable sentence lengths using LSTMs. I currently have 900 data points, of which I am using 100 for both test and validation, and 700 for training. In this tutorial, you will discover how to use Keras to develop and evaluate neural network models for multi-class classification problems. I think I'm going to need to do some visualization of the data, to verify that it is balanced, plus I have some other ideas to try, but so far it is very frustrating. i'm currently trying to train 10 class with val_acc is 0.6870 and val_loss is 1.4573. what do you think? labels categories are 1 to 7. model = Sequential() mode. More evidence that something is wonky is that I make one of the input columns have the same values as the output column. I have a similar problem. I am trying to train a CNN using frames that portray me shooting a ball through a basket. All . Using TensorFlow backend. I am building a keras CNN model using ResNet50 utilizing transfer learning. y_valid[102:155]=1 The process of selecting the right set of hyperparameters for your machine learning (ML) application is called hyperparameter tuning or hypertuning. Data Augmentation. After completing this step-by-step tutorial, you will know: How to load data from CSV and make it available to Keras How to prepare multi-class What is a good way to make an abstract board game truly alien? @amcneil1998 you may have to regularize and can even use the Earlystopping in callbacks, but before that could you share your code and ur data ( 5 sample points would do) , coz like i said the methods we use pretty much depend on the type of data we use. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. We proposed modified VGG network [7] and ResNet [1] network for this experiment.. "/> ap calculus unit 1 practice test. 1. Why does it matter that a group of January 6 rioters went to Olive Garden for dinner after the riot? to your account. model.add(LSTM(output_dim=64,input_length=self.seq_len,batch_input_shape=(16,1,200),input_dim=self.embed_length,return_sequences=True,stateful=False )) If you are solving Binary Classification all you need to do this use 1 cell with sigmoid activation. I use LSTM to do a sequence labeling task, but I got the same acc and cal_acc for each epoch. What percentage of page does/should a text occupy inkwise. This article attempts to explain these metrics at a fundamental level by exploring their components and calculations with experimentation. It offers five different accuracy metrics for evaluating classifiers. Use drop out . Keras model always predicts same output class. model.add(Dense(256, activation='relu')) So turns out your loss might be the problem after all. Hope this help. How many characters/pages could WordStar hold on a typical CP/M machine? 50/472 [==>] - ETA: 0s - loss: 0.6281 - acc: 0.6800Epoch 02815: val_acc did not improve for file in glob.glob(train_path): So Dense is just a fully connected layer, it is what does a lot of the "decision making" based on the resulting feature vector. Otherwise accuracy would almost always be zero since the . If you use SparseCategoricalCrossentropy instead as loss it should work. Next, I build the keras model, I basically follow this guide: I put my epoch outputs into a pandas dataframe and this is it looks like. 18272/18272 [==============================] - 116s - loss: 0.0312 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286 this happened when I used Epoch 3/15 Loss in LSTM network is decreasing and predicting time series data closer to existing data but Accuracy is increased to some value like acc - 0.784 and constantly repeating for all the Epochs or else There is another possibility will be like accuracy will be 0 for all the epochs neither it's increasing nor it's decreasing. model.add(Conv2D(32, (3, 3), activation='relu',padding='same',name='block1_conv3')) @vishnu-zsf I'm having the same problem it seems, what optimizer/ learning rate did you use? x=Conv2D(16,(5,5),padding='valid',data_format='channels_first',activation='relu',use_bias=True)(x) model.add(Conv2D(128, (3, 3), activation='relu',padding='same',name='block3_conv2')) its the training log after epochs: @hujiao1314 I do not know if I really understand what you are trying to do, so forgive me if it does not make sense. Epoch 2818/10000 tf.keras.metrics.Accuracy(name="accuracy", dtype=None) Calculates how often predictions equal labels. PHP . What did Lem find in his game-theoretical analysis of the writings of Marquis de Sade? hist = model.fit(X_train_mat, Y_train_mat, nb_epoch=10000, batch_size=30, validation_split=0.1), Epoch 2816/10000 But just to be sure I changed to number of nodes to two, and I got the same results as before. 2022 Moderator Election Q&A Question Collection, Test score vs test accuracy when evaluating model using Keras, loss, val_loss, acc and val_acc do not update at all over epochs, How to understand loss acc val_loss val_acc in Keras model fitting, training vgg on flowers dataset with keras, validation loss not changing, Keras fit_generator and fit results are different. Here the accuracy obtained is around 85%, but the validation loss and accuracy remain constant after epoch 15 and do not improve for the rest of the 100 epochs. I've never experienced the same phenomenon using raw tensorflow so I think it's a keras thing. model.add(MaxPooling2D(pool_size=(2, 2),strides =(2,2),name='block4pool')), model.add(Flatten()) Basically, I was doing some preprocessing to my data before training which ends up squeezing the pixel intensity to near zero (in short all images were just black images). I've tried heavy dropout on the fully-connected layers, on all layers, on random layers. If anyone has a decent solution except sample size, kindly let me know. You can check documentation about Dense layer here : https://faroit.github.io/keras-docs/2.0.0/layers/core/. Already on GitHub? 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By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. it was not. Programming Tutorials. Accuracy; Binary Accuracy But later I discovered it was an issue with my preprocessing of data. Keras is a deep learning application programming interface for Python. When I call model.fit (X_train, y_train, validation_data= [X_val, y_val]), it shows 0 validation loss and accuracy for all epochs, but it trains just fine. In theory, the network should figure out that there is 100% relationship here and accuracy should increase, but it doesn't. My solution was to increase the size of the training set, reduce the number of features, start with just one layer and not too many units (say 128). Reducing Initial Learning Rate helps. Usually with every epoch increasing, loss should be going lower and accuracy should be going higher. Increase the initial learning rate and/or choose a different optimizer. Also it's unlikely it's overfitting as I'm really using heavy dropouts (between 0.5~0.7 for each layer). Employer made me redundant, then retracted the notice after realising that I'm about to start on a new project. The VGG-16 model showed the best accuracy at 0.81, with a recall rate of 0.90. significant accuracy to identify the small objects from the input ima ge. Why I'm getting constant training and validation accuracy in my model? 1 Answer. with this architecture, I get 0.73 constantly. model.add(MaxPooling2D(pool_size=(2, 2),name='block3_pool')), model.add(Conv2D(256, (3, 3), activation='relu',padding='same',name='block4_conv1')) Stack Overflow Public questions & answers; Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Talent Build your employer brand ; Advertising Reach developers & technologists worldwide; About the company I am using adam and mse for optimizer/loss. Asking for help, clarification, or responding to other answers. Modified 3 years, 4 months ago. How could that be? Here is the code for the model after the test data has been split off: I have faced the same issue multiple times while using Keras. 18272/18272 [==============================] - 120s - loss: 0.0316 - acc: 0.4297 - val_loss: 0.0281 - val_acc: 0.4286 zoom_range=0.5, By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Then: create the model, compile, load weights, call fit_generator: everything works beautifully. Getting low accuracy on keras pretrained word embeddings example. 18272/18272 [==============================] - 119s - loss: 0.0314 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286 The AUC was stagnant for 35 epochs then it started increasing. from keras.models import Sequential from keras.layers import Convolution2D, MaxPooling2D Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company, 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, Keras del stuck with constant loss and accuracy, Getting low accuracy on keras pretrained word embeddings example, Keras + Tensorflow CNN with multiple image inputs. Is it considered harrassment in the US to call a black man the N-word? x=MaxPooling2D(pool_size=(2,2),strides=(2,2))(x), inputs_y=Input(shape=(1,32,21)) model = Sequential() The final layer should have a 'sigmoid' activation instead of softmax since it is binary classification. . I took the same problems all epoch step had same val_loss and val_acc. ValueError: Error when checking target: expected dense_4 to have shape (1,) but got array with shape (2,), batch_size = 32 Nothing seems to help out, except increasing the data size. Spanish - How to write lm instead of lim? Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.01, amsgrad=False) Epoch 14/15 The fact that loss keeps dropping but accuracy stays constant says (to me) that this is as good as it can be. model.add(Dense(1)) @vishnu-zsf All of my input/output data is regularized from -1-1 with a mean of 0. I am trying to understand a relationship between some x-cols and a y-col. I tried changing optimizers, learning rates, momentum, network depth, and all other parameters. 18272/18272 [==============================] - 113s - loss: 0.0312 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286 My network is shown below: . When we are training the model in keras, accuracy and loss in keras model for validation data could be variating with different cases. CSS . Epoch 15/15 I found that using smaller neural network architecture. Codeigniter . How to Improve Low Accuracy Keras Model Design? vertical_flip=True), history= model.fit_generator(train_datagen.flow(x_train, y_train, batch_size = 10,shuffle=True),steps_per_epoch=len(x_train),epochs = 500,shuffle=True, And here is how I created and trained the model: The problem is that I get very low accuracies that remain with the same value at each epoch: As I don't have your data, I can only give you some suggestions. following is my code ,very simple. Thanks for contributing an answer to Stack Overflow! rev2022.11.4.43008. Epoch 7/15 @prabaHridayami what architecture are you using? 18272/18272 [==============================] - 117s - loss: 0.0314 - acc: 0.4297 - val_loss: 0.0281 - val_acc: 0.4286 Viewed 4k times 6 New! I'm currently using a batch size of 50, and even running past 50 epochs showed no increase in accuracy or loss. y=MaxPooling2D(pool_size=(2,2),strides=(2,2))(y), merged_input=keras.layers.concatenate([x,y],axis=-1), z=Dense(16,activation='softmax')(merged_input) next step on music theory as a guitar player. model.add(Conv2D(32, (3, 3), input_shape=(100, 400, 3), activation='relu', padding='same',name='block1_conv1')) depends on your data nature [time series or not] you should select a convenient cross-validation and shuffling strategy. for weight initialization in the Dense layers 50/472 [==>] - ETA: 0s - loss: 0.4406 - acc: 0.8600Epoch 02816: val_acc did not improve 18272/18272 [==============================] - 116s - loss: 0.0314 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286 code to run with decaying lr in Keras Tuning the parameters will change from problem to another of course. I am trying to understand a relationship between some x-cols and a y-col. It had worked. Python . I trained and tested my model on a sample data and got 93.7% accuracy on vgg19 and 93.3% accuracy on. I had the same problem while training a convolutional auto encoder. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Why is SQL Server setup recommending MAXDOP 8 here? Is a planet-sized magnet a good interstellar weapon? I made learning rate ("lr" parameter in optimizer) smaller and it solved the problem. Browse other questions tagged, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company. It got resolved by changing the optimizer from 'rmsprop' to 'adam'. From what I know, it's fairly normal for the accuracy of a model to plateau if the loss function reaches a minima. @vishnu-zsf @amcneil1998 in my case, the lr had no impact actually and the solution for me was shuffling data for each epoch. The inputs vary in order of magnitude, and even when I scale everything to between 0 and 1, the same issue occurs. opt = optimizers.adam(lr=0.0008) 2021 Copyrights. . can you please help me . What could be the reason? layer.trainable=False In this case it is (900, 225, 6). Accuracy still stayed around 0.5 but loss started pretty low (0.01). model.add(Dropout(0.4)), model.add(Dense(20, activation='softmax')), this is my architecture model using sequential. I am building a keras CNN model using ResNet50 utilizing transfer learning. model.add(Conv2D(64, (3, 3), activation='relu',padding='same',name='block2_conv2')) Rather, it seems like it is getting stuck in a local minima. My Keras CNN doesn't learn. x=Dense(512,activation='relu')(x) It works ! Loss and accuracy on the training set change from epoch to epoch, but the validation accuracy / loss doesn't, which is a bit odd.

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