I am training my Neural Network and the loss and accuracy graph look like this:
The training accuracy drops to 0 after epoch 55.
I am training Bi-LSTM for text classification. I use word embedding as the features and I use Adam as optimizer.
Anyone knows why? Thanks!
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I am training a binary classification model using SIIM-ISIC Melanoma Classification datasets.
I am using efficientnet V2M as base model
I used cosine decay schedule with 2 warm up epochs and Adam as optimizer
However, my training loss is fluctuating while my validation loss is stable.
Is there a particular reason why this would happen?
Thank in advance
Im using CNN to classify wireless signal.
Meamwhile I meet some strange problem - when trainset accuray is 80%, I got 79% testset accuracy, but when trianset accuracy is 93%, the testset accuray fall to 71%. Anyone have same problem before?
My net is based on keras + tensorflow.
the detail of net is :
CNN(512,(2,2),tanh)
Batch_normaliztion
flatten()
DNN(512,elu)
DNN(256,elu)
DNN(128,softmax)
opt=adam
loss = mse
THANKS
This would appear to be a case of over fitting.How did you get the training accuracy to go from 80% to 93%? Was it just by running more epochs?.
If over fitting is what is happening add dropout layers to the model. This should improve the validation accuracy but it may take more epochs to achieve the desired training accuracy. Another alternative is to use regularizers in the dense layers.
The more complex your model is the more it is prone to over fitting so you might try running the model with just two dense layers or alternatively reduce the number of nodes in the hidden layers.
I've trained a multi-label multi-class image classifier by using sigmoid as output activation function and binary_crossentropy as loss function.
The accuracy curve for validation is showing up-down fluctuation while loss curve at few epochs is showing weird(very high) values.
Following is the Accuracy and loss-curve for fine-tuned(last block) VGG19 model with Dropout and BatchNormalization.
Accuracy curve
loss curve
Accuracy and loss-curve for fine-tuned(last block) VGG19 model with Dropout, BatchNormalization and Data Augmentation.
accuracy curve with data augmentation
loss curve with data augmentation
I've trained the classifier with 1800 training images(5-labels) with 100 validation images. The optimizer I'd used is SGD((lr=0.001, momentum=0.99).
Can anyone explain why loss-curve is getting so much weird or high values at some eochs?
Should I use different loss-function? If yes, which one?
Don't worry - all is well. Your loss curve doesn't say much, especially 'spikes in the loss curve'. They're totally allowed, your model is still training. You should look at your accuracy curve, and that one goes up pretty normal I think.
As a learning exercise, I'm training the Inception (v2) model from scratch using the ImageNet dataset from the Kaggle competition. I've heard people say it took them a week or so of training on a GPU to converge this model in this same dataset. I'm currently training it on my MacBook Pro (single CPU), so I'm expecting it to converge in no less than a month or so.
Here's my implementation of the Inception model. Input is 224x224x3 images, with values in range [0, 1].
The learning rate was set to a static 0.01 and I'm using the stochastic gradient descent optimizer.
My question
After 48 hours of training, the training loss seems to indicate that it's learning from the training data, but the validation loss is beginning to get worse. Ordinarily, this would feel like the model is overfitting. Does it look like something might be wrong with my model or dataset, or is this perfectly expected, since I've only trained 5.8 epochs?
My training and validation loss and accuracy after 1.5 epochs.
Training and validation loss and accuracy after 5.8 epochs.
Some input images as seen by the model, as well as the output of one of the early convolution layers.
I have a deep network of only fully connected/dense layers with the shape 128-256-512-1024-1024 all layers use LeakyReLU activation, with no dropout and the final layer has a softmax activation.
During training after the 20th epoch the validation/test loss starts to reverse and go up but the test accuracy continues to increase also. How does this make sense? And is the test accuracy actually accurate if it were shown new data or is there some kind of false positive going on here?
I compiled the model like so:
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['categorical_accuracy']
)
Graphs of my train/test accuracy and loss curves:
Accuracy
Loss
Edit:
This may help. It's the true labels plotted against the predicted labels for the last epoch:
This is easily possible with a loss function that is sensitive to the distance between an incorrect prediction and the ground truth. You can get 90% of the predictions correct, but if the misses are ridiculously far off the mark, your loss value can increase. This results in some models from not accurately identifying one or two critical factors in the ground truth.