I use Tensorflow2 to train my CNN model.After training,I save the best model and want to test it on my test dataset,However,the result is much worse than valid result during training.I find when the test batch is bigger ,the result is better,and the test dataset is not shuffled,the result is worse.
My CNN Model includes batch normlization layers,I guess that is the reason,but I can't resolve the problem.Help me , please.this is my code
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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 have created text classification model(.pb) using tensorflow. Prediction is good.
Is it possible to check the sentence using for prediction is already used to train the model or not. I need to retrain the model when new sentence is given to model to predict.
I did some research and couldn't find a way to get the train data only with the pb file because that file only stores the features and not the actual train data(obviously),but if you have the dataset,then you can easily verify duh....
I don't think you can ever find the exact train data with only the trained model,cause the model only contains the features and not the actual train data
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?
So far I have trained a couple different models in TensorFlow (with Keras) and I see that getting the batch_size right seems to be important not just for speed of training but also the resultant accuracy of the model.
What confuses me is a case where a model has an actual batch channel as the first dimension on the input (and output as well). If my batch size is 32 but I'm always inputting 1 data at run-time then where does the batch channel apply? How could I utilize the vast majority of it if I'm inherently only using 1/batch_size amount of it in forward pass?
If you are curious the model I am researching, it is this one:
https://github.com/pierluigiferrari/ssd_keras/blob/master/models/keras_ssd300.py
see:
Output shape of predictions: (batch, n_boxes_total, n_classes + 4 + 8)
predictions = Concatenate(axis=2, name='predictions')([mbox_conf_softmax, mbox_loc, mbox_priorbox])
The tensors had run through numerous other layers that had constants and such pretrained with [batch_size] as well. To me it just seems like inputs at various batch index would have to yield different results. Maybe I just need something incredibly obvious pointed out to me.
It would seem that after training you must recompile the model with a batch size of 1, then transfer the weights from the training model to the new model for evaluation. The alternative is performing 'batch_size' count of predictions at once (which of course is not always feasible per application). If there are alternatives (or if I read wrong) please feel free to add an answer.