The classifier runs without errors, yet accuracy remains at a near-constant 50% across. In my case I had validation accuracy of 0.0000e+00 throughout training (using Keras and CNTK-GPU backend) when my batch size was 64 but there were only 120 samples in my validation set (divided into three classes). I tried different setups from LR, optimizer, number of filters and even playing with the model size. train_array=cv2.resize(train_array,(img_rows,img_cols),3) 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? 472/472 [==============================] - 0s - loss: 0.5151 - acc: 0.7648 - val_loss: 1.2978 - val_acc: 0.4151 To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Retraining with the same data returns different accuracies. I'm currently using a batch size of 50, and even running past 50 epochs showed no increase in accuracy or loss. Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.01, amsgrad=False) I have event tried to over fit my data by just using a small part of my data. Your 3x3, 4x4, 5x5 convolution layer seems weird, but I can't give you a reason (maybe someone else?). How can we build a space probe's computer to survive centuries of interstellar travel? How can I check if I'm properly grounded? How can I increase the full scale of an analog voltmeter and analog current meter or ammeter? Find centralized, trusted content and collaborate around the technologies you use most. Why l2 norm squared but l1 norm not squared? model.compile(optimizer= adam, loss='binary_crossentropy', metrics=['accuracy']), train_datagen = ImageDataGenerator( I 'm not sure but I solved this problem. Is it normal for acc and val_acc to stay constant like this? More evidence that something is wonky is that I make one of the input columns have the same values as the output column. 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. Before I was knowing that this is wrong, I did add Batch Normalisation layer after every learnable layer, and that helps. What are the impacts of different learning rates on this model and why does it keep overfitting? 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. Keras loss: 0.0000e+00 and accuracy stays constant. Does squeezing out liquid from shredded potatoes significantly reduce cook time? Keras loss: 0.0000e+00 and accuracy stays constant ; Ask Question. I had same issue: epoch accuracy was growing while validation was the same value (0.41). Nothing seems to help out, except increasing the data size. I'm not sure I have seen an example of Keras with less than 16 units in an LSTM. Some of the samples did not have enough entries so they are zero-padded to the correct size. I am also facing the exact same issue. 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. img_channels = 3, #Creating array of training samples Dropout literally takes random weights and drops them by setting them to 0. 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). Epoch 12/15 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. The fact that loss keeps dropping but accuracy stays constant says (to me) that this is as good as it can be. 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. I am trying to train a CNN using frames that portray me shooting a ball through a basket. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. @prabaHridayami what architecture are you using? keras.metrics.categorical_accuracy(y_true, y_pred) sparse_categorical_accuracy is similar to the categorical_accuracy but mostly used when making predictions for sparse targets. But just to be sure I changed to number of nodes to two, and I got the same results as before. You can have a decreasing loss with the same accuracy if the algorithm gets more and more sure of the points it identified before. How to draw a grid of grids-with-polygons? I have tried reducing the learning rate, increasing the learning rate, tried both sdg and adam optimizers. Transformer 220/380/440 V 24 V explanation. Drupal . brightness_range=(0.2,2.5), Sign in But could you give a bit more detailed explanation as to why the gradient becomes zero. @vishnu-zsf I'm having the same problem it seems, what optimizer/ learning rate did you use? 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 model.add(Dropout(0.4)), model.add(Dense(20, activation='softmax')), this is my architecture model using sequential. Mine is all resolved now btw. layer.trainable=True, adam = Adam(lr=0.0001) But later I discovered it was an issue with my preprocessing of data. 18272/18272 [==============================] - 117s - loss: 0.0314 - acc: 0.4297 - val_loss: 0.0281 - val_acc: 0.4286 The simplest model in Keras is the sequential, which is built by stacking layers sequentially. x=Conv2D(64,(3,3),padding='same',data_format='channels_first',activation='relu',use_bias=True)(x) Not the answer you're looking for? following is my code ,very simple. That would be my suggestion to increase the variety of data your model sees. rev2022.11.4.43008. labels categories are 1 to 7. model = Sequential() mode. x = GlobalMaxPooling2D()(x) Our website specializes in programming languages. We proposed modified VGG network [7] and ResNet [1] network for this experiment.. "/> ap calculus unit 1 practice test. It is just randomly guessing. Due to the randomness . How many characters/pages could WordStar hold on a typical CP/M machine? Generally, your model is not better than flipping a coin. Is God worried about Adam eating once or in an on-going pattern from the Tree of Life at Genesis 3:22? for file in glob.glob(valid_path): The process of selecting the right set of hyperparameters for your machine learning (ML) application is called hyperparameter tuning or hypertuning. 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, Have a question about this project? and it just worked when I removed it and used the default settings !!!! The best answers are voted up and rise to the top, Not the answer you're looking for? Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, Your last Dense layer should have one unit per class, so predictions = Dense(, https://faroit.github.io/keras-docs/2.0.0/layers/core/, 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. 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. With tf.keras, I even tried validation_data = [X_train, y_train], this also gives zero accuracy. 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? How many characters/pages could WordStar hold on a typical CP/M machine? Save questions or answers and organize your favorite content. nb_classes = 2 train_path = "D:/data/train*. 1 Answer. I've been trying to train 100 class with 10 images for each class. code to run with decaying lr in Keras 18272/18272 [==============================] - 115s - loss: 0.0313 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286 I built a sequential deep learning model using Keras Tuner optimal hyperparameters and plotted the accuracy and loss for X_train and X_test. How does taking the difference between commitments verifies that the messages are correct? It got resolved by changing the optimizer from 'rmsprop' to 'adam'. 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 . Codeigniter . I met a similar problem with my keras CNN model, my training samples were 4000, and validation samples were 1000. I'm currently doing the Udacity Self-Driving Car Engineer Nanodegree course; my cohort is currently doing the behavioral cloning lab. Just a note, always try to keep a variable to handle number of classes, something like. A good method for debugging this issue is to use an ipython/jupyter notebook, compile the model, and then have it predict for one of your batches. While training a model with this parameter settings, training and validation accuracy does not change over a all the epochs. Keras model always predicts same output class. @hadisaadat setting shuffle=true did not improve my results. TonyBP13. Training accuracy only changes from 1st to 2nd epoch and then it stays at 0.3949. Loss was constant 4.000 and accuracy 0.142 on 7 target values dataset. This seems to be the case really no matter what I do. I am trying to train a CNN with Keras. Accuracy started at 0.5 and averaged around that on both training and validation data for the 120 epochs that I trained. Thanks. model.add(Dense(n_class,activation='softmax')) #where n_class is number of classes Keras is a Python library for deep learning that wraps the efficient numerical libraries Theano and TensorFlow. 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) 2. When I changed optimization methods from Adam to RMSprop, it was run but I refreshed all kernel and restart I took the same issue. The more you have the more "flexible" it can be, i.e. Accuracy; Binary Accuracy So Dense is just a fully connected layer, it is what does a lot of the "decision making" based on the resulting feature vector. Assuming your data is fully-prepared, some suggestions of the CNN: The first and the second convolution layers should have ReLU activations, at least not linear activations. 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 . So in total we'll have an input layer and the output layer. Reducing Initial Learning Rate helps. Hyperparameters are the variables that govern the training process and the topology . Why is SQL Server setup recommending MAXDOP 8 here? The Keras Tuner is a library that helps you pick the optimal set of hyperparameters for your TensorFlow program. training_data.append(train_array), #Creating array of validation samples Handling Overfitting and Underfitting problem. So turns out your loss might be the problem after all. But with val_loss (keras validation loss) and val_acc (keras validation accuracy), many cases can be possible . Do reply if you the issue still persists. Were you able to resolve ? model.add(Conv2D(128, (3, 3), activation='relu',padding='same',name='block3_conv2')) All . To be honest, I was suspecting it was a bug from Keras but boom! y_train[225:363]=1 #Class2=1 What percentage of page does/should a text occupy inkwise. 1 Answer. LO Writer: Easiest way to put line of words into table as rows (list). Reduce network complexity. Reason behind should be due to vanishing gradient. Can't think of why, but it eventually started to learn. I think that the learning rate is the problem. You might find it useful to change to 'sigmoid'. This was when i used 100,000 data samples and had 10 epochs. My Keras CNN doesn't learn. What could be the reason? Use "model.eval ()" when you want to evaluate . with this architecture, I get 0.73 constantly. Add a comment. model.compile(loss="categorical_crossentropy" , optimizer='rmsprop' , class_mode='categorical') *" To me it seems like I missed a step, but when calling load_weights on the model it was corrected. 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. MathJax reference. I'm gunna throw my voice in here, too. Already on GitHub? Does the Fog Cloud spell work in conjunction with the Blind Fighting fighting style the way I think it does? I've found stepping through code like this in mysterious situations to be enlightening. It become true that I was doing regression with ReLU last activation layer, which is obviously wrong. . If anyone has a decent solution except sample size, kindly let me know. in my model, by LSTM I have got repeating training and validation accuracy for each epoch!! 18272/18272 [==============================] - 113s - loss: 0.0312 - acc: 0.4297 - val_loss: 0.0280 - val_acc: 0.4286 model.add(Conv2D(256, (3, 3), activation='relu',padding='same',name='block4_conv2')) I use LSTM to do a sequence labeling task, but I got the same acc and cal_acc for each epoch. Train-test-val split: I am building a keras CNN model using ResNet50 utilizing transfer learning. Why are only 2 out of the 3 boosters on Falcon Heavy reused? Indian Institute of Technology Kharagpur. @skhadem yeah, i'm doing several augmentations so 1 image is going to be having 88 image augmentation. MathJax reference. print(file) do you know what is the function of these two? When we are training the model in keras, accuracy and loss in keras model for validation data could be variating with different cases. The loss decreases (because it is calculated using the score), but . VGG19 model weights have been successfully loaded. I currently have 900 data points, of which I am using 100 for both test and validation, and 700 for training. model.add(Activation('softmax')) Strangely, I am seeing the same behavior with similar code but with VGG19. How to Improve Low Accuracy Keras Model Design? *" There are about 25 features, some of which are categorical type. For a binary output you can use either of the two. to your account. We provide programming data of 20 most popular languages, hope to help you! Keras offers the following Accuracy metrics. I tried changing optimizers, learning rates, momentum, network depth, and all other parameters. Keras CNN with low/constant accuracies. Getting low accuracy on keras pretrained word embeddings example. model.add(Dropout(0.4)), model.add(Dense(256, activation='relu')) I am facing the same issue and am starting to suspect this is the problem. I changed again RMSprop to SGD. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. 50/472 [==>] - ETA: 0s - loss: 0.5385 - acc: 0.7400Epoch 02817: val_acc did not improve 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. y=Conv2D(8,(4,4),padding='valid',data_format='channels_first',activation='relu',use_bias=True)(y) 472/472 [==============================] - 0s - loss: 0.5100 - acc: 0.7585 - val_loss: 1.2699 - val_acc: 0.4151. model.add(MaxPooling2D(pool_size=(2, 2),name='block2_pool')), model.add(Conv2D(128, (3, 3), activation='relu',padding='same',name='block3_conv1')) (and maybe remove batch_input_shape attribute in your LSTM layer, i guess.). Use MathJax to format equations. I've tried heavy dropout on the fully-connected layers, on all layers, on random layers. From what I know, it's fairly normal for the accuracy of a model to plateau if the loss function reaches a minima. How many characters/pages could WordStar hold on a typical CP/M machine? 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. I have tried increasing my amount of data to 2800, using 400 for both test and validation, and 2000 for training. It works ! Why is SQL Server setup recommending MAXDOP 8 here? This article attempts to explain these metrics at a fundamental level by exploring their components and calculations with experimentation. Arguments. If you want to print your output shape of your model, try. How do you actually pronounce the vowels that form a synalepha/sinalefe, specifically when singing? 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. Had the same issue. i want to optimize my autoencoder network but i have no idea how to do that. 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 . Also it's unlikely it's overfitting as I'm really using heavy dropouts (between 0.5~0.7 for each layer). Are you doing any type of data augmentation? My network is shown below: . When I train the network, the training accuracy increases slowly until it reaches 100%, while the validation accuracy remains around 65% (It is .

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