![]() ![]() Print(''.join( for i in sequence.numpy()]))Īs you may notice, I used 2*sequence_length +1 size of each sample, and you'll see why I did that very soon. Sequences = char_dataset.batch(2*sequence_length + 1, drop_remainder=True) Luckily for us, we have to use tf.data.Dataset's batch() method to gather characters together: # build sequences by batching ![]() Great, now we need to construct our sequences, as mentioned earlier, we want each input sample to be a sequence of characters of the length sequence_length and the output of a single character that is the next one. This will take the very first 8 characters and print them out along with their integer representation: 38 Since we want to scale our code for larger datasets, we need to use tf.data API for efficient dataset handling, as a result, let's create a tf.data.Dataset object on this encoded_text array: # construct tf.data.Dataset objectĬhar_dataset = tf._tensor_slices(encoded_text)Īwesome, now this char_dataset object has all the characters of this dataset let's try to print the first characters: # print first 5 characters Now let's encode our dataset, in other words, we gonna convert each character into its corresponding integer number: # convert all text into integersĮncoded_text = np.array( for c in text]) Since we have vocab as our vocabulary that contains all the unique characters of our dataset, we can make two dictionaries that map each character to an integer number and vice-versa: # dictionary that converts characters to integersĬhar2int = -int2char.pickle", "wb")) Now that we loaded and cleaned the dataset successfully, we need a way to convert these characters into integers, there are a lot of Keras and Scikit-Learn utilities out there for that, but we are going to make this manually in Python. Print("Number of unique characters:", n_unique_chars) Let's print some statistics about the dataset: # print some stats If you wish to keep commas, periods and colons, just define your own punctuation string variable. The above code reduces our vocabulary for better and faster training by removing upper case characters and punctuations as well as replacing two consecutive newlines with just one. Text = anslate(str.maketrans("", "", punctuation)) # remove caps, comment this code if you want uppercase characters as well Text = open(FILE_PATH, encoding="utf-8").read() Now let's define our parameters and try to clean this dataset: sequence_length = 100 Just make sure you have a folder called "data" exist in your current directory. ![]() Open("data/wonderland.txt", "w", encoding="utf-8").write(content) These lines of code will download it and save it in a text file: import requests But you can use any book/corpus you want. We are going to use a free downloadable book as the dataset for this tutorial: Alice’s Adventures in Wonderland by Lewis Carroll. Importing everything: import tensorflow as tfįrom import Sequentialįrom import Dense, LSTM, Dropoutįrom string import punctuation Preparing the Dataset Let's install the required dependencies for this tutorial: pip3 install tensorflow=2.0.1 numpy requests tqdm Related: How to Perform Text Classification in Python using Tensorflow 2 and Keras. We need to show the model as many examples as we can grab in order to make reasonable predictions. The second sample input would be "ython is a great languag" and the output is "e", and so on, until we loop all over the dataset. For instance, say we want to train on the sentence "python is a great language", the input of the first sample is "python is a great langua" and output would be "g". ![]() Each input is a sequence of characters and the output is the next single character. In text generation, we show the model many training examples so it can learn a pattern between the input and output. If you want a better text generator, check this tutorial that uses transformer models to generate text. Note that the ultimate goal of this tutorial is to use TensorFlow and Keras to use LSTM models for text generation. However, in this tutorial, we are doing to do something different, we will use RNNs as generative models, which means they can learn the sequences of a problem and then generate entirely a new sequence for the problem domain.Īfter reading this tutorial, you will learn how to build an LSTM model that can generate text (character by character) using TensorFlow and Keras in Python. Recurrent Neural Networks ( RNNs) are very powerful sequence models for classification problems. Disclosure: This post may contain affiliate links, meaning when you click the links and make a purchase, we receive a commission. ![]()
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