# Why training and validation similar loss curves lead to poor performance

I am training a binary classification neural network model using matlab the graph that I got using 20 neurons in hidden layer is given below. the confusion matrix and graph between cross entropy vs epochs.

to prevent overfitting in a model the training curve in a loss graph should be similar to the validation curve.

but in the current situation the third graph shows curve where validation curve is similar to training although the overall accuracy is low as compared to the curve where the two curve diverges in the above plot.

WHy this is happening and what I am doing wrong in understanding these curves?

to prevent overfitting in a model the training curve in a loss graph should be similar to the validation curve