title characterlevel text generation with lstm fchollethttps twitter comfchollet date created 20150615 last modified 20200430 description generate text from nietzsche s writings with a characterlevel lstm accelerator gpu introduction this example demonstrates how to use a lstm model to generate text characterbycharacter at least 20 epochs are required before the generated text starts sounding locally coherent it is recommended to run this script on gpu as recurrent networks are quite computationally intensive if you try this script on new data make sure your corpus has at least 100k characters 1m is better setup prepare the data cut the text in semiredundant sequences of maxlen characters build the model a single lstm layer prepare the text sampling function helper function to sample an index from a probability array train the model introduction this example demonstrates how to use a lstm model to generate text character by character at least 20 epochs are required before the generated text starts sounding locally coherent it is recommended to run this script on gpu as recurrent networks are quite computationally intensive if you try this script on new data make sure your corpus has at least 100k characters 1m is better setup prepare the data we remove newlines chars for nicer display cut the text in semi redundant sequences of maxlen characters build the model a single lstm layer prepare the text sampling function helper function to sample an index from a probability array train the model
import keras
from keras import layers

import numpy as np
import random
import io



path = keras.utils.get_file(
    "nietzsche.txt",
    origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt",
)
with io.open(path, encoding="utf-8") as f:
    text = f.read().lower()
text = text.replace("\n", " ")  
print("Corpus length:", len(text))

chars = sorted(list(set(text)))
print("Total chars:", len(chars))
char_indices = dict((c, i) for i, c in enumerate(chars))
indices_char = dict((i, c) for i, c in enumerate(chars))


maxlen = 40
step = 3
sentences = []
next_chars = []
for i in range(0, len(text) - maxlen, step):
    sentences.append(text[i : i + maxlen])
    next_chars.append(text[i + maxlen])
print("Number of sequences:", len(sentences))

x = np.zeros((len(sentences), maxlen, len(chars)), dtype="bool")
y = np.zeros((len(sentences), len(chars)), dtype="bool")
for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        x[i, t, char_indices[char]] = 1
    y[i, char_indices[next_chars[i]]] = 1




model = keras.Sequential(
    [
        keras.Input(shape=(maxlen, len(chars))),
        layers.LSTM(128),
        layers.Dense(len(chars), activation="softmax"),
    ]
)
optimizer = keras.optimizers.RMSprop(learning_rate=0.01)
model.compile(loss="categorical_crossentropy", optimizer=optimizer)




def sample(preds, temperature=1.0):
    
    preds = np.asarray(preds).astype("float64")
    preds = np.log(preds) / temperature
    exp_preds = np.exp(preds)
    preds = exp_preds / np.sum(exp_preds)
    probas = np.random.multinomial(1, preds, 1)
    return np.argmax(probas)




epochs = 40
batch_size = 128

for epoch in range(epochs):
    model.fit(x, y, batch_size=batch_size, epochs=1)
    print()
    print("Generating text after epoch: %d" % epoch)

    start_index = random.randint(0, len(text) - maxlen - 1)
    for diversity in [0.2, 0.5, 1.0, 1.2]:
        print("...Diversity:", diversity)

        generated = ""
        sentence = text[start_index : start_index + maxlen]
        print('...Generating with seed: "' + sentence + '"')

        for i in range(400):
            x_pred = np.zeros((1, maxlen, len(chars)))
            for t, char in enumerate(sentence):
                x_pred[0, t, char_indices[char]] = 1.0
            preds = model.predict(x_pred, verbose=0)[0]
            next_index = sample(preds, diversity)
            next_char = indices_char[next_index]
            sentence = sentence[1:] + next_char
            generated += next_char

        print("...Generated: ", generated)
        print("-")