Confusion regarding the accuracy of my model
your results look fine. Your test set and validation set have probability distributions which  must be a close match to each other consequently the similarity in the validation and test data accuracies.
I'm facing this issue in my college project which is a clickbait news classifier, i.e., it classifies between clickbait headlines and non-clickbait headlines. I'm using a dataset which has 16000 headlines of each type. Now, the main issue I'm facing is on training my network on 70% data, with 30% as my test set size. My validation set is 30% of the training set. But after fitting and evaluating the model on my test set, and I get this
After fitting:

Epoch 10/10 131/131 [==============================] - 1s 6ms/step - loss: 0.2098 - accuracy: 0.9457 - val_loss: 0.3263 - val_accuracy: 0.9417

After evaluating on test set:

300/300 [==============================] - 1s 2ms/step - loss: 0.3030 - accuracy: 0.9432

Confusion Matrix:

array([[4638, 162], [ 383, 4417]])

Now I'm very new to neural networks and i'm not sure if these accuracies are supposed to be this similar to each other. Is this something that I should be concerned about or am I missing something? I appreciate all the help I can get... Thanks!