Is there any way to know the importance of features once I have trained a keras model for text classification?

Asked
Active3 hr before
Viewed126 times

4 Answers

classificationtrainedmodel
90%

An important difference between the two is that option 2 enables you to do asynchronous CPU processing and buffering of your data when training on GPU, So if you're training the model on GPU, you probably want to go with this option to get the best performance

Example_snippet/controller/utility/_classification.js/ import tensorflow as tf import. . .
import tensorflow as tf
import numpy as np
load more v
88%

Following snnipet shows how to use pre-trained word embeddings in the model, There are four essential steps:,Loading the pretrained word embeddings,You can download the pre-trained word embeddings from here,Text Classification is an example of supervised machine learning task since a labelled dataset containing text documents and their labels is used for train a classifier

Example_snippet/controller/utility/_classification.js/ from sklearn import model_sele. . .
from sklearn
import model_selection, preprocessing, linear_model, naive_bayes, metrics, svm
from sklearn.feature_extraction.text
import TfidfVectorizer, CountVectorizer
from sklearn
import decomposition, ensemble

import pandas, xgboost, numpy, textblob, string
from keras.preprocessing
import text, sequence
from keras
import layers, models, optimizers
load more v
72%

Alright, it’s time to understand an extremely important step you’ll have to deal with when working with text data, Once you have your text data completely clean of noise, it’s time to transform it into floating-point tensors

Example_snippet/controller/utility/_trained.js/ def depure_data(data): . . .
def depure_data(data): #Removing URLs with a regular expression url_pattern = re.compile(r 'https?://\S+|www\.\S+') data = url_pattern.sub(r '', data) # Remove Emails data = re.sub('\S*@\S*\s?', '', data) # Remove new line characters data = re.sub('\s+', ' ', data) # Remove distracting single quotes data = re.sub("\'", "", data) return data
load more v
65%

Next, you will call adapt to fit the state of the preprocessing layer to the dataset, This will cause the model to build an index of strings to integers

Example_snippet/controller/utility/_trained.js/ import matplotlib.pyplot as pl. . .
import matplotlib.pyplot as plt
import os
import re
import shutil
import string
import tensorflow as tf

from tensorflow.keras
import layers
from tensorflow.keras
import losses
from tensorflow.keras
import preprocessing
from tensorflow.keras.layers.experimental.preprocessing
import TextVectorization
load more v