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Validation loss increase and constant training accuracy 1D cnn


Validation loss increase and constant training accuracy 1D cnn

By : sakamotodesu
Date : November 21 2020, 04:01 AM
this one helps. You are using "binary_crossentropy" for a problem of multiple classes. Change it to "categorical_crossentrop".
The accuracy computed with Keras using the binary_crossentropy with a model of more than 2 labels is just wrong.
code :


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Tensorflow CNN model not training? Constant loss and accuracy

Tensorflow CNN model not training? Constant loss and accuracy


By : Hakunamatata
Date : March 29 2020, 07:55 AM
this will help The code you started from is just a benchmark of the forward and backward pass and isn't designed to do training. You should start from an example that actually trains a model instead and ignore the benchmark code.
You might have an easier time starting from a completely working training example program instead of trying to combine two pieces.
Keras: Training loss decrases (accuracy increase) while validation loss increases (accuracy decrease)

Keras: Training loss decrases (accuracy increase) while validation loss increases (accuracy decrease)


By : arpita mohini
Date : March 29 2020, 07:55 AM
like below fixes the issue What I can think of by analyzing your metric outputs (from the link you provided):
code :
#save best True saves only if the metric improves
chk = ModelCheckpoint("myModel.h5", monitor='val_loss', save_best_only=False) 
callbacks_list = [chk]
#pass callback on fit
history = model.fit(X, Y, ... , callbacks=callbacks_list)
How to avoid training loss to increase dramatically in first 100 steps of object detection with accuracy of -1?

How to avoid training loss to increase dramatically in first 100 steps of object detection with accuracy of -1?


By : user3062867
Date : March 29 2020, 07:55 AM
I wish this helpful for you Few steps which will help you to debug
Make sure your input training images does not contain any spaces in their naming convention, for example image name can be "cat1.jpg" but it cannot be"cat 1.jpg", "cat1 .jpg" etc. Make sure your image file is not corrupted, try to open each image with 'cv2.imread()' if that dosent read your image then that image is cerainly corrupted Start annotating the images only when point 1 and 2 are seriously considered Make sure while generating tf record you donot get any errors. Make sure each image had width x height > 300x300 Recheck path in fine_tune_checkpoint: ".../pathto/model.ckpt",label_map_path: ".../pathto/pbtxtxt_input.pbtxt", input_path: ".../pathto/ttt_tensorm_train.record" make sure this all is correct
Validation accuracy fluctuating while training accuracy increase?

Validation accuracy fluctuating while training accuracy increase?


By : user3465070
Date : March 29 2020, 07:55 AM
this will help What you are describing here is overfitting. This means your model keeps learning about your training data and doesn't generalize, or other said it is learning the exact features of your training set. This is the main problem you can deal with in deep learning. There is no solution per se. You have to try out different architectures, different hyperparameters and so on.
You can try with a small model that underfits (that is the train acc and validation are at low percentage) and keep increasing your model until it overfits. Then you can play around with the optimizer and other hyperparameters.
Validation loss and accuracy not changing from training

Validation loss and accuracy not changing from training


By : Rajprasad
Date : October 05 2020, 04:00 AM
hop of those help? I'm using transfer learning with MobileNet v2 with Tensorflow 2.0 to train a network for skin quality classification. Everything is working perfectly fine, however it seems that the validation loss and validation accuracy do not change. , I would think that can be one of several things:
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