# Accuracy too high too fast?

I have a simple text classifier, with the following structure:

    input = keras.layers.Input(shape=(len(train_x[0]),))

x=keras.layers.Dense(500, activation='relu')(input)
x=keras.layers.Dropout(0.5)(x)
x=keras.layers.Dense(250, activation='relu')(x)
x=keras.layers.Dropout(0.5)(x)
preds = keras.layers.Dense(len(train_y[0]), activation="sigmoid")(x)

model = keras.Model(input, preds)


When training it with 300,000 samples, with a batch size of 500, I get an accuracy value of .95 and loss of .22 in the first iteration, and the subsequent iterations are .96 and .11.

Why does the accuracy grow so quickly, and then just stop growing?

• By any chance you are running it on a Jupyter notebook, can you share more of your code, especially about the training part ? – Semih Korkmaz Sep 2 '19 at 20:44
• I'm not. I'm new to this. I don't even know what a jupyter notebook is :P – hjf Sep 2 '19 at 21:03
• Without more information I don't think we can help you. A few things I can thinkg of: First of all, are you sure you mean iteration and not epoch? Secondly, are your data balanced (i.e. same number of samples in each class)? If not it is possible, if let's say 95% of your samples belong to a single class that your model is predicting this class only and achieving 0.95 accuracy. Finally, it is possible that it is just a very simple task and your model actually scores so high. – Djib2011 Sep 2 '19 at 21:21