使用LSTM生成文本（python深度学习）

# -*- coding = utf-8 -*-
# @Time : 2021/7/22
# @Author : pistachio
# @File : p23.py
# @Software : PyCharm
import keras
from keras import layers
import numpy as np
import random
import sys

path = r'D:\PYCHARMprojects\Dailypractise\nietzsche.txt'
text = open(path).read().lower()
print('Corpus length:', len(text))

maxlen = 60
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))

chars = sorted(list(set(text)))
print('Unique character:', len(chars))
char_indices = dict((char, chars.index(char)) for char in chars)

print('Vectorization...')
x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)
y = np.zeros((len(sentences), len(chars)), dtype=np.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.models.Sequential()
model.add(layers.LSTM(128, input_shape=(maxlen, len(chars))))
model.add(layers.Dense(len(chars), activation='softmax'))

optimizer = keras.optimizers.RMSprop(lr=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)

for epoch in range(1, 60):
    print('epoch', epoch)
    model.fit(x, y, batch_size=128, epochs=1)
    start_index = random.randint(0, len(text) - maxlen - 1)
    generated_text = text[start_index: start_index + maxlen]
    print('---Generating with seed:"'+ generated_text + '"')
    for temperature in [0.2, 0.5, 1.0, 1.2]:
        print('------temperature:', temperature)
        sys.stdout.write(generated_text)
        
for i in range(400):
    sampled = np.zeros((1, maxlen, len(chars)))
    for t, char in enumerate(generated_text):
        sampled[0, t, char_indices[char]] = 1
                
        preds = model.predict(sampled, verbose=0)[0]
        next_index = sample(preds, temperature)
        next_char = chars[next_index]
            
        generated_text += next_char
        generated_text = generated_text[1:]
            
        sys.stdout.write(next_char)