Using Keras how and what do I need to export to use my classifier independently?
$begingroup$
I have a basic question that I can't seem to find an answer to.
I built and trained with good results (above 90% acc.) 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
Any ideas would be of great help.
Thanks in advance.
python classification multilabel-classification natural-language-process
asked 1 min ago
MinetzMinetz
1
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Minetz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
I have a basic question that I can't seem to find an answer to.
I built and trained with good results (above 90% acc.) 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
Any ideas would be of great help.
Thanks in advance.
python classification multilabel-classification natural-language-process
asked 1 min ago
MinetzMinetz
1
New contributor
Minetz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I have a basic question that I can't seem to find an answer to.
I built and trained with good results (above 90% acc.) 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
Any ideas would be of great help.
Thanks in advance.
python classification multilabel-classification natural-language-process
asked 1 min ago
MinetzMinetz
1
New contributor
Minetz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a basic question that I can't seem to find an answer to.
I built and trained with good results (above 90% acc.) 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
Any ideas would be of great help.
Thanks in advance.
python classification multilabel-classification natural-language-process
python classification multilabel-classification natural-language-process
asked 1 min ago
MinetzMinetz
1
New contributor
Minetz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
MinetzMinetz
1
New contributor
Minetz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
MinetzMinetz
1
New contributor
Minetz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
MinetzMinetz
1
asked 1 min ago
MinetzMinetz
1
1
New contributor
Minetz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Minetz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Minetz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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