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I am doing sentence classification task with variable sentence lengths using LSTMs. It only takes a minute to sign up. I am building a keras CNN model using ResNet50 utilizing transfer learning. Why is SQL Server setup recommending MAXDOP 8 here? epochs=30,validation_split=0.2,shuffle=True)`. width_shift_range=0.2, Why isn't the val_acc changing over iterations? print(predictions). Transfer Learning. What is a good way to make an abstract board game truly alien? After 3 days I tuned the optimizer trying to change learning rate and learning rate decay, and finally everything improved and everything makes sense, trying to increase learning rate decay slightly till the model start to improve without stuck at 50%. Why is proving something is NP-complete useful, and where can I use it? I am trying to understand a relationship between some x-cols and a y-col. model.add(Conv2D(256, (3, 3), activation='relu',padding='same',name='block4_conv2')) y=Conv2D(8,(4,4),padding='valid',data_format='channels_first',activation='relu',use_bias=True)(y) We can also access the values of w and b using the model.weights command.The model predicted w as 2.003785 (actual value is 2.0) and b as 0.97882223 (actual value is 1.0). Some of the samples did not have enough entries so they are zero-padded to the correct size. Keras loss: 0.0000e+00 and accuracy stays constant. TF Keras ValueError: Shapes (None, 3, 3) and (None, 3) are incompatible. valid_data=[] 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 I see a lot of problems but rarely any solution in the discussions above. x=Dense(1024,activation='relu')(x) Were you able to resolve ? Making statements based on opinion; back them up with references or personal experience. The simplest model in Keras is the sequential, which is built by stacking layers sequentially. I discovered it after debugging my preprocessing step in which I tried to write some of the images in a disk. Alhasan Alkhaddour This problem may be due to the "batch normalization". Our website specializes in programming languages. 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. 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I am dealing with the Street View House Number recognition problem. Epoch 9/15 We proposed modified VGG network [7] and ResNet [1] network for this experiment.. "/> ap calculus unit 1 practice test. Short story about skydiving while on a time dilation drug. I have 101 folders from 0-100 containing synthetic training images. layer.trainable=True, adam = Adam(lr=0.0001) If I keep the number of neurons in the output layer and use sigmoid, for each epochs, there is no change in the accuracy. I found that using smaller neural network architecture. Strangely, I am seeing the same behavior with similar code but with VGG19. valid_array= cv2.imread(file) I used Keras for CNN model on the Kaggle platform with GPU. What is the issue? Transformer 220/380/440 V 24 V explanation. The input data is a 3d array with the form (Nsamples, Entries/Sample, EntryDim). To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 18272/18272 [==============================] - 114s - loss: 0.0322 - acc: 0.4297 - val_loss: 0.0282 - val_acc: 0.4286 By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. I am new to Neural Networks and currently doing a project for university. With Dropout the optimizer is forced to focus on many different places. Hyperparameters are the variables that govern the training process and the topology . print(y_train), y_valid=np.ones((num_validsamples,),dtype = int) Handling Overfitting and Underfitting problem. Now that is a problem for me, as I am trying to compare the effect of the data sample size on my network. But with val_loss (keras validation loss) and val_acc (keras validation accuracy), many cases can be possible . print(file) It's a way to take large feature vectors and map to a class. Python . Does anyone know how to solve this issues? This seems to be the case really no matter what I do. #model.add(LSTM(output_dim=16,return_sequences=True,stateful=False )) 2021 Copyrights. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Such problem is more serious when you are doing ConvNet, and it's the reason why we got residual network. model.compile(optimizer='adam', , I think the problem is with the scope of the variable loss.You also don't add losses each iteration of the loop. How true is this slide on deep learning claiming that all improvements from 1980s are only due to much more data and much faster computers? I tried different setups from LR, optimizer, number of filters and even playing with the model size. I went into my image directories to check if my two different classes are mixed, and they are not. model.add(MaxPooling2D(pool_size=(2, 2),name='block3_pool')), model.add(Conv2D(256, (3, 3), activation='relu',padding='same',name='block4_conv1')) ; name: Optional name for the returned operation.Default to the name passed to the Optimizer constructor. Asking for help, clarification, or responding to other answers. Viewed 4k times 6 New! print(i,layer.name), for layer in model.layers[:75]: P.S. 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. ValueError: Error when checking target: expected dense_4 to have shape (1,) but got array with shape (2,), batch_size = 32 What percentage of page does/should a text occupy inkwise. 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 Also it's unlikely it's overfitting as I'm really using heavy dropouts (between 0.5~0.7 for each layer). In my case when I attempt LSTM time series classification often val_acc starts with a high value and stays the same, even though loss, val_loss and acc change. I don't know if it is a bug with the framework; my best guess is that it is not, because other students are finding success. The code below is for my CNN model and I want to plot the accuracy and loss for it, any help would be much appreciated. Keras model always predicts same output class. print score, Anyone meets the same problem? x=MaxPooling2D(pool_size=(2,2),strides=(2,2))(x), x=Conv2D(32,(5,5),padding='same',data_format='channels_first',activation='relu',use_bias=True)(x) This is time-series data so perhaps I need to adjust the model somehow? When I increased the number to 500+, it started to converge better, but still there are periods when loss, acc and val_loss changes, but val_acc sticks to the same value. Also, when I try to evaluate , The reason you get any accuracy at all is likely because Keras does y_true == round (y_pred), rounding the model prediction. Keras loss: 0.0000e+00 and accuracy stays constant. Epoch 10/15 Is it OK to check indirectly in a Bash if statement for exit codes if they are multiple? I built a sequential deep learning model using Keras Tuner optimal hyperparameters and plotted the accuracy and loss for X_train and X_test. this happened when I used My Keras CNN doesn't learn. 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. 18272/18272 [==============================] - 114s - loss: 0.0312 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286, @talentlei But the loos function has not reached a minima because after each epoch the val_loss keeps decreasing, but the val_accuracy remains constant 0.97 since epoch 1.. model.add(Conv2D(64, (3, 3), activation='relu',padding='same',name='block2_conv3')) Accuracy still stayed around 0.5 but loss started pretty low (0.01). Thanks! If I were you, I would use 3x3 layer. It helps to avoid over fitting and is almost standard at this point. TonyBP13. Not the answer you're looking for? I'm currently using a batch size of 50, and even running past 50 epochs showed no increase in accuracy or loss. Loss value going down while accuracy remains constant? 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 )) Reduce network complexity. 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 . The reason is pretty straightforward in your final Dense layers where you are specifying the output basically the softmax layer , here number of cells should be equal to number of classes. This leads me to believe that the issue is not with the actual model code and somewhere in the pre-processing. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Validation accuracy is same throughout the training. training_data.append(train_array), #Creating array of validation samples Thank you in Advance. You can have a decreasing loss with the same accuracy if the algorithm gets more and more sure of the points it identified before. keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False) I am training on ~2000 images and have two classes. couldn't find a fix yet. Ultimately, my validation accuracy stays stuck at a single value. Using TensorFlow backend. tf.keras.metrics.Accuracy(name="accuracy", dtype=None) Calculates how often predictions equal labels. 472/472 [==============================] - 0s - loss: 0.5100 - acc: 0.7585 - val_loss: 1.2699 - val_acc: 0.4151. 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 . Connect and share knowledge within a single location that is structured and easy to search. 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. Sorted by: 0. @hadisaadat reduce ur learning rate and try for a few smaller learning rates. Tags: machine-learning keras neural-network time-series lstm LO Writer: Easiest way to put line of words into table as rows (list). . please help me. y_train[225:363]=1 #Class2=1 print ("Test Accuracy = " + str(eval[1])), predictions= model.predict(x_valid) . I've never experienced the same phenomenon using raw tensorflow so I think it's a keras thing. y=Conv2D(32,(2,2),padding='same',data_format='channels_first',activation='relu',use_bias=True)(y) In your log, the loss seems to start from a very low value, and converge very soon after a few epochs. In this tutorial, you will discover how to use Keras to develop and evaluate neural network models for multi-class classification problems. Would it be illegal for me to act as a Civillian Traffic Enforcer? Hope this help. There may be many shortcomings, please advise. My problem is that training loss and training accuracy decrease over epochs but validation accuracy fluctuates in a small interval. Types of Loss Functions for Classification Tasks. If you want to print your output shape of your model, try. how to correctly interpenetrate accuracy with keras model, giving perfectly linear relation input vs output? I am also facing the exact same issue. So I tried with balanced dataset and I do get zero validation accuracy on first epoch. 1 Answer. I have event tried to over fit my data by just using a small part of my data. Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.01, amsgrad=False) It got resolved by changing the optimizer from 'rmsprop' to 'adam'. My solution to this is changing the learning rate of the optimizer.sometimes it helps, haha. @hadisaadat setting shuffle=true did not improve my results. So turns out your loss might be the problem after all. I have a similar problem. Had the same issue. Is it normal for acc and val_acc to stay constant like this? 18272/18272 [==============================] - 119s - loss: 0.0314 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286 My training accuracy isnt improving (almost constant) and my loss is decreasing very slowly. However when predicting I am only able to get 2 values from the output. @vishnu-zsf I'm having the same problem it seems, what optimizer/ learning rate did you use? @andrew-ayers Did you manage to solve this issue? 472/472 [==============================] - 0s - loss: 0.5179 - acc: 0.7479 - val_loss: 1.2844 - val_acc: 0.4151 @prabaHridayami what architecture are you using? 18272/18272 [==============================] - 113s - loss: 0.0319 - acc: 0.4297 - val_loss: 0.0281 - val_acc: 0.4286 How could that be? model.add(Dense(256, activation='relu')) y_train[0:224]=0 #Class1=0 Mobile app infrastructure being decommissioned. 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. y=Conv2D(32,(2,2),padding='same',data_format='channels_first',activation='relu',use_bias=True)(y) Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. rev2022.11.4.43008. This was when i used 100,000 data samples and had 10 epochs. I've tried heavy dropout on the fully-connected layers, on all layers, on random layers. The accuracy and loss for the test set did not show up in the plots. jQuery . I'm currently doing the Udacity Self-Driving Car Engineer Nanodegree course; my cohort is currently doing the behavioral cloning lab. 18272/18272 [==============================] - 115s - loss: 0.0314 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286 model.add(LSTM(256, input_shape=(6, 10))) code to run with decaying lr in Keras 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, SHould solve ur problem, @AkhilAshref , even i had the similar issue as @hadisaadat , mine worked after reducing the lr. Do reply if you the issue still persists. 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? In the end I don't know if there is still a bug in the framework, or it all results from an overly complicated model and the insufficient size of the training set, but all things considered, I am satisfied with the performance of the model and the results that I have achieved and believe that Keras LSTM is usable for time series classification. 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. Neural networks - what does learning rate exactly mean and is it applied over batches or epochs? 50/472 [==>] - ETA: 0s - loss: 0.5385 - acc: 0.7400Epoch 02817: val_acc did not improve Have a question about this project? I have a similar problem with NVIDIA (adam, mse, 120k samples including flipped data) model for Self_Driving Car Engineer course - validation loss changes but validation accuracy stays the same. Thanks for contributing an answer to Stack Overflow! y_valid[0:101]=0 Then: create the model, compile, load weights, call fit_generator: everything works beautifully. Stack Overflow for Teams is moving to its own domain! 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. I used to face the same result before. and the scores do not change. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. All . Before I was knowing that this is wrong, I did add Batch Normalisation layer after every learnable layer, and that helps. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Your 3x3, 4x4, 5x5 convolution layer seems weird, but I can't give you a reason (maybe someone else?). MathJax reference. Retraining with the same data returns different accuracies. How to constrain regression coefficients to be proportional. I'd think if I were overfitting, the accuracy would peg close or at 100%? score = model.evaluate(self.X_test,self.Y_test,batch_size=16) If you now score it 0.95, you still predict it to be a 1. I tried to share my experience in case anyone else is facing the same issue. model.add(Dense(n_class,activation='softmax')) #where n_class is number of classes But, if I make a change in the number of layers as mentioned above, same error as you are getting. So Dense is just a fully connected layer, it is what does a lot of the "decision making" based on the resulting feature vector. 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. Do any Trinitarian denominations teach from John 1 with, 'In the beginning was Jesus'? Sometimes the problem is caused by a unsuitable Dense layers. validation_data=(x_valid,y_valid),validation_steps=num_validsamples // batch_size,callbacks=[tensorboard]), eval = model.evaluate(x_valid, y_valid) Save questions or answers and organize your favorite content. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. 5th Nov, 2020. 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. It offers five different accuracy metrics for evaluating classifiers. training_data=[] To learn more, see our tips on writing great answers. The reason is pretty straightforward in your final Dense layers where you are specifying the output basically the softmax layer , here number of cells should be equal to number of classes. Does the Fog Cloud spell work in conjunction with the Blind Fighting fighting style the way I think it does? Sign in it was not. 1. model.add(MaxPooling2D(pool_size=(2, 2),strides =(2,2),name='block4pool')), model.add(Flatten()) Why l2 norm squared but l1 norm not squared? Laravel .Net . How many characters/pages could WordStar hold on a typical CP/M machine? increase to 3 layers with say 512 units without providing more training data), it would behave the same as before - flat or irregular training accuracy. 18272/18272 [==============================] - 116s - loss: 0.0314 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286 @prabaHridayami That is very low amount of data, it can be hard to obtain good results. To learn more, see our tips on writing great answers. I guess my test set was too small to feed large batches into the CNN. model.compile(loss='binary_crossentropy', More evidence that something is wonky is that I make one of the input columns have the same values as the output column. 10 epoches may be too few. I have a similar issue when i tried to build an autoencoder using LSTM for sequences or CNN for images, the model reaches around 50% accuracy, 2.5 loss then stuck, nothing improving at all. I am trying to train each row of 220 features to predict whether the y will be a 1 or a 0, so this is a binary classification problem (I think). Similar problem here. model = Sequential() My convnet is the same one from the NVidia end-to-end paper (relu on all layers). I divide my pixels by 255 (as is customary) but can still see what the image looks like when plotting it. For example, if the NNet predicted a vector ( 0.6, 0.6, 0.4), by optimising the weights, the prediction can change to ( 0.99, 0.99, 0.01) - now the algorithm predicts exactly the same labels as before . I have tried reducing the learning rate, increasing the learning rate, tried both sdg and adam optimizers. can you please help me . x=Conv2D(64,(3,3),padding='same',data_format='channels_first',activation='relu',use_bias=True)(x) If you use SparseCategoricalCrossentropy instead as loss it should work. So I increased the learning rate and loss started around 5.1 and then dropped of to 0.02 after the 6th Epoch. Getting low accuracy on keras pretrained word embeddings example. Asking for help, clarification, or responding to other answers. If you use SparseCategoricalCrossentropy instead as loss it should work. Have you solved the problem? Are you doing any type of data augmentation? Generalize the Gdel sentence requires a fixed point theorem. 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. Does squeezing out liquid from shredded potatoes significantly reduce cook time? z=Dense(4,activation='softmax')(z), model=Model(inputs=[inputs_x,inputs_y],outputs=outp) I had same issue: epoch accuracy was growing while validation was the same value (0.41). model.add(Dense(1)) Still if I make the model overly complex (e.g. But just to be sure I changed to number of nodes to two, and I got the same results as before. While training a model with this parameter settings, training and validation accuracy does not change over a all the epochs. Already on GitHub? Why I'm getting constant training and validation accuracy in my model? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Indian Institute of Technology Kharagpur. opt = optimizers.adam(lr=0.0008) What did Lem find in his game-theoretical analysis of the writings of Marquis de Sade? There are about 25 features, some of which are categorical type. Arguments. How do I simplify/combine these two methods for finding the smallest and largest int in an array? BinaryCrossentropy: Computes the cross . Accuracy; Binary Accuracy data_augmentation = True, img_rows, img_cols = 224,224 Scores are changing, but none is crossing your threshold so your prediction does not change. Bidyut Saha. Connect and share knowledge within a single location that is structured and easy to search. Is God worried about Adam eating once or in an on-going pattern from the Tree of Life at Genesis 3:22? Connect and share knowledge within a single location that is structured and easy to search. 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. learn better, but that means more parameters. I get the output that I posted below. print(file) Programming Tutorials. A great example of this is working with text in deep learning problems such as word2vec. I am building a keras CNN model using ResNet50 utilizing transfer learning. The loss decreases (because it is calculated using the score), but . I made learning rate ("lr" parameter in optimizer) smaller and it solved the problem. i'm currently trying to train 10 class with val_acc is 0.6870 and val_loss is 1.4573. what do you think? Jasper Fadden Asks: Binary Image Classifer in Keras Shows Properly Decreasing Loss but Constant accuracy I have a basic keras image classifier used on grayscale 64x64 images pulled out from local folders. Let's get right into it. It works ! Use "model.eval ()" when you want to evaluate . This frequency is ultimately returned as binary accuracy: an idempotent operation that simply divides total by . Should we burninate the [variations] tag? 18272/18272 [==============================] - 116s - loss: 0.0312 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286 In some situation, your input might not carry as much information as the neural network expects, and therefore, the weights are gonna vanish to zeros even after several layers. 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 been trying to train 100 class with 10 images for each class. The Keras Tuner is a library that helps you pick the optimal set of hyperparameters for your TensorFlow program. Usually with every epoch increasing, loss should be going lower and accuracy should be going higher. After a one-hot transformation on the categorical x-cols, the 25 features become about 220 features, so the input into the neural-network is a matrix of about 40,000 rows and about 220 columns. That would be my suggestion to increase the variety of data your model sees. can you send me your code of optimization of autoencoder. Stack Overflow for Teams is moving to its own domain! What does puncturing in cryptography mean. train_path = "D:/data/train*. height_shift_range=0.2, If anyone has a decent solution except sample size, kindly let me know. 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. thank you very much, i'll check that out Keras CNN with low/constant accuracies. and then define num_classes at the start of your code for better flexibility and readability. rev2022.11.4.43008. I have (2364, 256, 256, 3) shaped data of rgb images and (2364, 8, 8) shaped labels. @vishnu-zsf @amcneil1998 in my case, the lr had no impact actually and the solution for me was shuffling data for each epoch. How can I check if I'm properly grounded? Now, I want to add the accuracy and loss scores from model.test_on_batch(X_test, y_test) and plot it. self.model.compile(loss='binary_crossentropy', optimizer=opt,metrics = ['accuracy']) By clicking Sign up for GitHub, you agree to our terms of service and Why are only 2 out of the 3 boosters on Falcon Heavy reused? When I ensured that in such configuration the training progresses in a reasonable way, I have slowly added more features, more units, etc, and in the end got a satisfactory result. To me it seems like I missed a step, but when calling load_weights on the model it was corrected. Rather, it seems like it is getting stuck in a local minima. In my practice, 100+ epoches will be applied. Thanks. Employer made me redundant, then retracted the notice after realising that I'm about to start on a new project. 18272/18272 [==============================] - 113s - loss: 0.0312 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286 Find centralized, trusted content and collaborate around the technologies you use most. Loss and accuracy on the training set change from epoch to epoch, but the validation accuracy / loss doesn't, which is a bit odd. You can check documentation about Dense layer here : https://faroit.github.io/keras-docs/2.0.0/layers/core/. Reason behind should be due to vanishing gradient. for layer in model.layers[75:]: The best answers are voted up and rise to the top, Not the answer you're looking for? @vishnu-zsf All of my input/output data is regularized from -1-1 with a mean of 0. This leads me to believe that the issue is not with the actual model code and . Is there a bug when it's not updating (even though loss, acc and val_loss update during the same epoch)? It become true that I was doing regression with ReLU last activation layer, which is obviously wrong. y=MaxPooling2D(pool_size=(2,2),strides=(2,2))(y), y=Conv2D(32,(4,4),padding='same',data_format='channels_first',activation='relu',use_bias=True)(y) img_channels = 3, #Creating array of training samples Epoch 2818/10000 Loss was constant 4.000 and accuracy 0.142 on 7 target values dataset. How to draw a grid of grids-with-polygons? The log loss is decreasing a tiny bit, but then gets stuck. Is it considered harrassment in the US to call a black man the N-word? Increase the initial learning rate and/or choose a different optimizer. It had worked. x=Dense(512,activation='relu')(x) Modified 3 years, 4 months ago. I suspect that this combination does not lead to nonzero weight adjustments (however, I do not have any literature background on this).
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