Text generation
Text generation is the task of generating new text that is similar to a given input text. This can be done using language models, which can estimate the probability of a sequence of words in a language. By sampling from the probability distribution of the language model, we can generate new text that is similar to the input text.
There are many ways to generate text using language models, but one popular method is to use recurrent neural networks (RNNs), such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Unit). These networks are designed to process sequences of inputs and can capture the sequential dependencies in the data.
Here is an example code for generating new song lyrics using an LSTM language model:
import tensorflow as tf
from tensorflow import keras
import numpy as np
# load the input text
with open("lyrics.txt", "r") as f:
text = f.read()
# build the vocabulary
vocab = sorted(set(text))
char2idx = {u:i for i, u in enumerate(vocab)}
idx2char = np.array(vocab)
# convert the input text to integers
text_as_int = np.array([char2idx[c] for c in text])
# create training examples and targets
seq_length = 100
examples_per_epoch = len(text)//(seq_length+1)
char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)
sequences = char_dataset.batch(seq_length+1, drop_remainder=True)
def split_input_target(chunk):
input_text = chunk[:-1]
target_text = chunk[1:]
return input_text, target_text
dataset = sequences.map(split_input_target)
# build the LSTM model
model = keras.Sequential([
keras.layers.Embedding(len(vocab), 256, batch_input_shape=[1, None]),
keras.layers.LSTM(1024, return_sequences=True, stateful=True, recurrent_initializer='glorot_uniform'),
keras.layers.Dropout(0.2),
keras.layers.LSTM(512, return_sequences=True, stateful=True, recurrent_initializer='glorot_uniform'),
keras.layers.Dropout(0.2),
keras.layers.LSTM(256, stateful=True, recurrent_initializer='glorot_uniform'),
keras.layers.Dense(len(vocab), activation='softmax')
])
# compile the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
# train the model
epochs = 30
for epoch in range(epochs):
print('Epoch {}/{}'.format(epoch+1, epochs))
for i, (input_text, target_text) in enumerate(dataset):
loss = model.train_on_batch(input_text, target_text)
if i % 100 == 0:
print('Batch {} Loss {:.4f}'.format(i, loss))
# generate new lyrics
start_text = 'I love you'
num_generate = 1000
temperature = 0.5
input_eval = [char2idx[s] for s in start_text]
input_eval = tf.expand_dims(input_eval, 0)
text_generated = []
model.reset_states()
for i in range(num_generate):
predictions = model(input_eval)
predictions = tf.squeeze(predictions, 0)
predictions = predictions / temperature
predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy()
input_eval = tf.expand_dims([predicted_id], 0)
text_generated.append(idx2char[predicted_id])
print(start_text + ''.join(text_generated))
In this example, we first load the input text from a file and build a vocabulary of characters. We then convert the input text to integers and create training examples and targets using a sliding window of size seq_length. We use an LSTM model with three layers to train on the input text. After training the model, we generate new lyrics by providing a starting text and sampling from the probability distribution of the model to predict the next character in the sequence. We continue this process to generate a sequence of num_generate characters.
The temperature parameter controls the randomness of the generated text. A higher temperature will result in more diverse but potentially less coherent text, while a lower temperature will result in more predictable but potentially repetitive text.
This approach can be applied to other types of text generation tasks, such as generating poetry or prose. By using a larger dataset and more complex models, we can generate high-quality text that is difficult to distinguish from human-written text. However, it's important to note that generating coherent and meaningful text is still an active area of research in NLP, and there are limitations to the current state of the art models.
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