keras.LSTM实现自动生成文章

from tensorflow import keras
import numpy as np
from tensorflow.keras import layers
import random
import sys

# path = keras.utils.get_file(
#     'nietzsche.txt',
#     origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')
# text = open(path).read().lower()
# print('Corpus length:', len(text))

path = keras.utils.get_file('real_test.txt', origin='.keras.datasets')
text = open(path, encoding='utf-8').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(sentences[0])
#print(next_chars[0])
print('Number of sequences:', len(sentences))
chars = sorted(list(set(text)))
#print(chars)
#['\n', ' ', '-', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
print('Unique characers:', len(chars))
#声明一个字典，将唯一的字符映射为它在列表chars中的索引
char_indices = dict((char, chars.index(char)) for char in chars)
#print(char_indices)
print('Vectorization...')
#print(next_chars)
print(len(sentences), maxlen, len(chars))

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):
        #print(i, sentence, t, char, char_indices[char])
        x[i, t, char_indices[char]] = 1
    #print(i,next_chars[i], char_indices[next_chars[i]])
    #y是第i行这60个字母的下一个字母，也就是我们希望得到的输出
    #将第i行中这个字母在字典里的排列序号置为1，如下一个字母是i，则i在字典里对应是11号，将[0，11]置位一
    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'))
#目标经过one-hot编码，因此用categorical_crossentropy
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  [0.5, 0.6]:
        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)

复现代码，笔记已经记在注释中了。
其中：

学校让写的那个学习强国总结，用keras.lstm自动生成文章实现了以下，效果如下：


今天把深度学习四大名著之一的python深度学习看完了，终于又迈进去一根脚趾头。