So I have this cnn in python. My data has 1000 training images and 100 validation images for each class. However my validation images are the same with my training data just less. I'm facing some accuracy problems so could this be one of the reasons?
Yes, the validation data should be different (not a subset of) than the training data.
That's because the validation data is to validate that the model isn't overtrained to the training data... if it were a subset of the training data, that obviously won't work.
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Hello everyone so I made this cnn model.
My data:
Train folder->30 classes->800 images each->24000 all together
Validation folder->30 classes->100 images each->3000 all together
Test folder->30 classes -> 100 images each -> 3000 all together
-I've applied data augmentation. ( on the train data)
-I got 5 conv layers with filters 32->64->128->128->128
each with maxpooling and batch normalization
-Added dropout 0.5 after flattening layers
Train part looks good. Validation part has a lot of 'jumps' though. Does it overfit?
Is there any way to fix this and make validation part more stable?
Note: I plann to increase epochs on my final model I'm just experimenting to see what works best since the model takes a lot of time in order to train. So for now I train with 20 epochs.
I've applied data augmentation (on the train data).
What does this mean? What kind of data did you add and how much? You might think I'm nitpicking, but if the distribution of the augmented data is different enough from the original data, then this will indeed cause your model to generalize poorly to the validation set.
Increasing your epochs isn't going to help here, your training loss is already decreasing reasonably. Training your model for longer is a good step if the validation loss is also decreasing nicely, but that's obviously not the case.
Some things I would personally try:
Try decreasing the learning rate.
Try training the model without the augmented data and see how the validation loss behaves.
Try splitting the augmented data so that it's also contained in the validation set and see how the model behaves.
Train part looks good. Validation part has a lot of 'jumps' though. Does it overfit?
the answer is yes. The so-called 'jumps' in the validation part may indicate that the model is not generalizing well to the validation data and therefore your model might be overfitting.
Is there any way to fix this and make validation part more stable?
To fix this you can use the following:
Increasing the size of your training set
Regularization techniques
Early stopping
Reduce the complexity of your model
Use different hyperparameters like learning rate
I am doing an image classification task using Keras.
I used the vgg16 architecture, I thought it is easier to do, the task is to classify the image having tumor or not in MRI images.
As usual, I read and make all the images in same shape (224×224×3) and normalised by dividing all the images by 255. Then train test split, test dataset is 25% and training dataset is 75%.
train, test = train_test_split(X, y, test_size=0.25)
Then, I trained and got val_loss as 0.64 and val_accuracy as 0.7261.
I save the trained model in my google drive.
Next day, I used the same procedure, to improve the model performance by loading the saved model.
I didn't change the model architecture, I simply loaded the saved model which scores 0.7261 accuracy.
This time, I got better performance, the val_loss is 0.58 and val_accurqcy is 0.7976.
I wonder how this gets high accuracy. Then, I found that when splitting the dataset, the images will splits in random, and thus some of the test data in the 1st training process will become training data in the 2nd training process. So, the model learns the images and predicted well in 2nd training process.
I have to clarify, is this model is truly learns the tumor patterns or it is like that we train and test the model with same dataset or same image samples.
Thanks
When using train_test_split and validating in different sessions, always set your random seed. Otherwise, you will be using different splits, and leaking data like you stated. The model is not "learning" more, rather is being validated on data that it has already trained on. You will likely get worse real-world performance.
I built an image classification model (CNN) using tensorflow-keras. I have some new images which I need to feed into the same model in order to increase the accuracy of the existing model.
I tried using the following code. But it decreases the accuracy.
re_calibrated_model = loaded_model.fit_generator(new_training_set,
steps_per_epoch=int(stp),
epochs=int(epc),
validation_data=new_test_set,
verbose=1,
validation_steps = 50)
Is there any method that I can use to re-calibrate my CNN model?
Your new training session does not start from previous training accuracy if you use completely different dataset to do second training.
You need to feed (old_images+new_images) for your intention.
What I normally do is to train the CNN model on the first batch of images and save that model. If I need to "retrain" the model with additional images, I load the previous saved model from disk and apply the inputs (test and train) and call the fit method. As mentioned before this only works if your outputs are exactly the same i.e. if you are using the same input and output classes.
In my experience training models using different image batches does not necessarily make your model more or less accurate, but rather increase the training time with each batch. Since I am using a CPU to train, my training time is about 30% faster if I train two batches of 1000 images each as oppose to training one batch of 2000 images for example.
I am training a UNET for semantic segmentation but I only have 200 labeled images. Given the small size of the dataset, it definitely needs some data augmentation techniques.
I have question about the test and the validation set.
I have custom data generator which keep feeding data from folder for training model.
So what I plan to do is:
do data augmentation for the training set and keep all of it in the same folder
"randomly" pick some of training data into test and validation set (of course, before training).
I am not sure if this is fine, since we just do some simple processing (flipping, transposing, adjusting brightness)
Would it be better to separate the data first and do the augmentation for the rest of data in the training folder?
Can someone explain what happens when you split up the data set for testing and training?
Put simply, the accuracy of your data mining model is evaluated by making predictions based on your training set of which the result is already known in test set.
More information on the testing and validation of data mining models (MSDN)
To be able to test the predictive analysis model you built, you need to split your dataset into two sets: training and test datasets. These datasets should be selected at random and should be a good representation of the actual population.
Similar data should be used for both the training and test datasets.
Normally the training dataset is significantly larger than the test dataset.
Using the test dataset helps you avoid errors such as overfitting.
The trained model is run against test data to see how well the model will perform.
More Information