# Accuracy converging to one in neural network (tensorflow.keras)

I was wondering if somebody would be able to shine a light on accuracy converging to 1 relatively quickly during training.

I am working on some new data and this is the first time i have seen this. I have attached the imaged of accuracy and loss (actual and validation set).

I am running a multi-label network where the data fits in to either class one, two or three. The code I am using is as follows:

model= tf.keras.models.Sequential()

model.compile(loss="categorical_crossentropy", optimizer="SGD", metrics=['accuracy'])

history=model.fit(X_trainERSC.values, y_trainERSC,
epochs=20,
batch_size=32,
verbose=1,
validation_split=0.15,
callbacks=[EarlyStopping(monitor='val_loss', patience=5)],shuffle=True)


Now the network trains fine and everything seems ot be ok and in a similar trend to data i have worked with previously. However, unlike the previous data which converges to about 97% for accuracy, the accuracy in this data converges to 1. I was wondering if this is normal? I know this seems vague, but i am not sure what to make of this and if for, accuracy and loss, extremely low loss as well as convergence to 1 is normal?

Many thanks!!