r - Can anyone help me interpret these graphs I obtained when using tensorflow for neural networks?

Question

Plot visualization in R

model %>%
   fit (x = x_train,
        y = y_train,
        epochs = 25,
        batch_size = 32,
        validation_split =.2
        )

The code above is the one I used to obtain the graphs when the fit argument is ran, however, every time the code is ran the graphs are different. I understand this is how you visualize how the model is training or learning. Loss is displayed in the top chart and the accuracy in the second one, for this last one I keep changing the epochs, batch size and validation and it's always 0. I do not fully understand these plots and what the numbers on the y-axis mean.

question from:https://stackoverflow.com/questions/66055251/can-anyone-help-me-interpret-these-graphs-i-obtained-when-using-tensorflow-for-n

Answer 1

There is a great explanation on what exactly the "loss" and "accuracy" refers to in a Machine Learning model right here. The term "mape" refers to "Mean Absolute Percentage Error" which is a different way to measure the performance of your model - the lower it is, the better your model performs.

Looking at the plots attached, it's easy to tell that there's a problem with your model since the accuracy of your model is increasing neither on the training nor on the validation set, and the loss is not decreasing either. This might for example be due to a problem with the model you're using (not suited for the task you expect it to perform) or the data that you feed into the model (which might e.g. be labeled in an inconsistent way).