Is there any way to know the importance of features once I have trained a keras model for text classification?
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
import tensorflow as tf import numpy as np
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
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
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
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
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
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
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