# Understanding LSTM Training and Validation Graph and their metrics (LSTM Keras)

I have trained a RNN/LSTM model. I would like to interpret my model results, after plotting the graph for Loss and accuracy (b/w training and Validation data set).

My objective is to classify the labels (either 0 or 1) if i provide only a partial input to the model. In such a way I have performed training.

Train_Validate_Test_Split

Train 80% ; Validate 10 % ; Test 10%

X_train_shape : (243, 100, 5)
Y_train_shape : (243,)

X_validate_shape : (31, 100, 5)
Y_validate_shape : (31,)

X_test_shape : (28, 100, 5)
Y_test_shape : (28,)


Model Summary

Model Graph

Model Metrics

Question or Interpretation from the model results

Q 1 : What can I understand/interpret from loss and Accuracy graph ? How can i confirm whether the model trained properly for my data set or not ?

Q 2 : Whether oscillations in both loss and accuracy, have some effect in >model training ? (Or it is a normal behavior) If not, how can I regularize my model without oscillations ?

Q 3 : What can I interpret or understand from my metrics tabular column ? My > Y_test accuracy is more when compared with Train & Validation accuracy, What can i interpret from this behavior ?