tokenizer.texts_to_sequences Keras Tokenizer gives almost all zeros
I am working to create a text classification code but I having problems in encoding documents using the tokenizer.
1) I started by fitting a tokenizer on my document as in here:
vocabulary_size = 20000 tokenizer = Tokenizer(num_words= vocabulary_size, filters='') tokenizer.fit_on_texts(df['data'])
2) Then I wanted to check if my data is fitted correctly so I converted into sequence as in here:
sequences = tokenizer.texts_to_sequences(df['data']) data = pad_sequences(sequences, maxlen= num_words) print(data)
which gave me fine output. i.e. encoded words into numbers
[[ 9628 1743 29 ... 161 52 250] [14948 1 70 ... 31 108 78] [ 2207 1071 155 ... 37607 37608 215] ... [ 145 74 947 ... 1 76 21] [ 95 11045 1244 ... 693 693 144] [ 11 133 61 ... 87 57 24]]
Now, I wanted to convert a text into a sequence using the same method. Like this:
sequences = tokenizer.texts_to_sequences("physics is nice ") text = pad_sequences(sequences, maxlen=num_words) print(text)
it gave me weird output:
[[ 0 0 0 0 0 0 0 0 0 394] [ 0 0 0 0 0 0 0 0 0 3136] [ 0 0 0 0 0 0 0 0 0 1383] [ 0 0 0 0 0 0 0 0 0 507] [ 0 0 0 0 0 0 0 0 0 1] [ 0 0 0 0 0 0 0 0 0 1261] [ 0 0 0 0 0 0 0 0 0 0] [ 0 0 0 0 0 0 0 0 0 1114] [ 0 0 0 0 0 0 0 0 0 1] [ 0 0 0 0 0 0 0 0 0 1261] [ 0 0 0 0 0 0 0 0 0 753]]
According to Keras documentation (Keras):
Arguments: texts: list of texts to turn to sequences.
Return: list of sequences (one per text input).
is it not supposed to encode each word to its corresponding number? then pad the text if it shorter than 50 to 50? Where is the mistake ?
I guess you should call like this:
sequences = tokenizer.texts_to_sequences(["physics is nice "])
How to Prepare Text Data for Deep Learning with Keras, must be constructed and then fit on either raw text documents or integer encoded text documents. tokenizer.texts_to_sequences Keras Tokenizer gives almost all zeros You should call the method like this: new_sample = ['A new sample to be classified'] seq = tokenizer.texts_to_sequences(new_sample ) padded = pad_sequences(seq, maxlen=MAX_SEQUENCE_LENGTH) pred = model.predict(padded)
The error is where you pad the sequences. The value to maxlen should be the maximum tokens you want, e.g. 50. So, change the lines to:
maxlen = 50 data = pad_sequences(sequences, maxlen=maxlen) sequences = tokenizer.texts_to_sequences("physics is nice ") text = pad_sequences(sequences, maxlen=maxlen)
This will cut the sequences to 50 tokens and fill the shorter with zeros. Watch out for the
padding option. The default is
pre that means if a sentence is shorter than
maxlen then the padded sequence will start with zeros to fill it. If you want the zeros to the end of the sequence add to the
pad_sequences the option
You should try calling like this:
sequences = tokenizer.texts_to_sequences(["physics is nice"])
texts_to_sequences: Transform each text in texts in a sequence of , utility class. This class allows to vectorize a text corpus, by turning each text into either a sequence of integers (each integer being the index of a token in a dictionary) or into a vector where the coefficient for each token could be binary, based on word count, based on tf-idf sequence <- tokenizer %>% texts_to_sequences(renderText(auction())) This is the Keras model (which works fine on its own:
when you use, Pads sequences to the same length i.e in your case to the num_words=vocabulary_size, that is why you are getting the output, Just try with : tokenizer.texts_to_sequences , this will give you a sequence of the words. read more about padding, it is just used to match every row of your data, that islets take an extreme of 2 sentences. sentence 1 and sentence 2, sentanec1 has length of 5, while sentence 2 has a length of 8. now when we do send our data for training if we don't pad the sentence1 with 3 then we cannot perform batch Wiese training. Hope it helps
Text Preprocessing, Using Tokenizer from keras.preprocessing.text. In : tk.num_words = 2 In : tk.texts_to_sequences(texts) Out: [, , ] In : Otherwise the tokenizer drops the OOV tokens even if we give the oov_token parameter. (Although, there's almost definitely a more elegant way to do this in Python.) keras lstm attention glove840b,lb 0.043 keras. preprocessing. text import Tokenizer from keras. preprocessing. sequence the sum may be almost zero a
Using Tokenizer with num_words · Issue #8092 · keras-team/keras , Text tokenization utility class. This class allows to vectorize a text corpus, by turning each text into either a sequence of integers (each integer being the index of Text summarization is a problem in natural language processing of creating a short, accurate, and fluent summary of a source document. The Encoder-Decoder recurrent neural network architecture developed for machine translation has proven effective when applied to the problem of text summarization.
tf.keras.preprocessing.text.Tokenizer, from keras.preprocessing.text import Tokenizer t = Tokenizer() # fit the tokenizer Once fit, the Tokenizer provides 4 attributes # that you can use to query what has tokenized_train = tokenizer.texts_to_sequences(train_sentences) #Turns the lists of is not None: # # words not found in embedding index will be all-zeros. Thanks for your code. I'd like to add annotation/explaination for the code. I think it is useful for beginners (like me) to learn. import numpy as np import pandas as pd from tqdm import tqdm tqdm.pandas() from keras.models import Model from keras.layers import Input, Dense, Embedding, SpatialDropout1D, Dropout, add, concatenate from keras.layers import CuDNNLSTM, Bidirectional
Sentiment AnalysisV2, Learn about Python text classification with Keras. If you want, you can use a custom tokenizer from the NLTK library with the You can use again scikit-learn library which provides the LogisticRegression classifier: You are almost there. a vector with zeros everywhere except for the corresponding spot for the word A language model can predict the probability of the next word in the sequence, based on the words already observed in the sequence. Neural network models are a preferred method for developing statistical language models because they can use a distributed representation where different words with similar meanings have similar representation and because they can use a large context of recently