I have a basic question that I can't seem to find an answer to.
I built and trained with good results (above 90% accuracy) a NLP Log classifier that takes in a UTF-8 payload and classifies it into 32 distinct categories but I am having a hard time writing a simple script that loads all the necessary info from my training and testing session (model.h5 and ?).
This is the structure of my code.
# load data logs and split it 80-20 for training and testing vocab_size = 500 tokenizer = text.Tokenizer(num_words=vocab_size) tokenize.fit_to_text(trainRawLogs) x_train = tokenize.text_to_matrix(trainRawLogs) x_test = tokenize.text_to_matrix(testRawLogs) encoder = labelBinarizer() encoder.fit(trainRawLogs) #Model build is simple ReLu - Softmax model.compile.. model.fit.. model.evaluate..
Now here is my question.
Out of all of this process what do I need to save to build a lightweight classifier? The model? The model and the labels? Anything else? I tried loading the model