Why is the accuracy for my keras model always 0?

Why is the accuracy for my keras model always 0?

My data is a time series. I know that for time series people do not usually use Dense neurons, but it is just a test. What really tricks me is that accuracy is always 0. And, with other tests, I did even lose: gets to a “NAN” value.

How does the sequential model in keras work?

I’m implementing a neural network with Keras, but the Sequential model returns nan as loss value. I have sigmoid activation function in the output layer to squeeze output between 0 and 1, but maybe doesn’t work properly.

Why is loss being outputed as Nan in keras RNN?

A similar problem was reported here: Loss being outputed as nan in keras RNN. In that case, there were exploding gradients due to incorrect normalisation of values. Thanks for contributing an answer to Data Science Stack Exchange! Please be sure to answer the question. Provide details and share your research!

How to reduce the dropout rate in keras?

Add regularization to add l1 or l2 penalties to the weights. Otherwise, try a smaller l2 reg. i.e l2 (0.001), or remove it if already exists. Try a smaller Dropout rate. Clip the gradients to prevent their explosion. For instance in Keras you could use clipnorm=1. or clipvalue=1. as parameters for your optimizer.

How is the accuracy of a machine learning model determined?

For any machine learning problem, training and test dataset should be separated. Accuracy of the model can be determined only when we examine how it is predicting for unknown values.

Do you need 100% accuracy to get overfitting?

You don’t need 100% accuracy to get overfitting. With enough buckets, you can get irreproducible results (something that would look terrible out-of-sample). See this excerpted article from the Lancet, describing the method of chopping a sample into buckets which are far too fine.

Why is the accuracy always 0 in TensorFlow?

You are using linear (the default one) as an activation function in the output layer (and relu in the layer before). Your loss is loss=’mean_squared_error’. However, the metric that you use- metrics= [‘accuracy’] corresponds to a classification problem. If you want to do regression, remove metrics= [‘accuracy’].