keras实现LSTM

from keras.models import Sequential
from keras.layers.core import Dense,Activation,Dropout
from keras.layers.recurrent import LSTM
from keras.utils.data_utils import get_file
import numpy as np
import random,sys

# helper function to sample an index from a probability array
def sample(a,temperator=1.0):
    a = np.log(a)/temperator
    a = np.exp(a)/np.sum(np.exp(a))
    return np.argmax(np.random.multinomial(1,a,1))


if __name__=="__main__":
    path = './poetry.txt'
    print('opeing txt')
    text = open(path, 'r',encoding='utf-8').read().lower()
    print(text)
    print('corpus length',len(text))

    chars = set(text)
    print('total chars',len(chars))
    char_indices = dict((c,i) for i,c in enumerate(chars))
    print(char_indices)
    indices_char = dict((i,c) for i,c in enumerate(chars))
    print(indices_char)

    # cut the text in semi-redundant sequences of maxlen characters
    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("nb sentences:",len(sentences))

    #########向量化###########
    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

    # build the model: 2 stacked LSTM
    print("Build model......")
    model = Sequential()
    # model.add(LSTM(len(chars), 512, return_sequences=True))
    #######network#########
    # 两层的LSTM，注意在首层中使用input_shape
    # 中间的各层都不需要再次计算输入层数，网络自行计算
    # LSTM后不需要像CNN那样接一个激活函数，在LSTM内部存在
    #######################

    model.add(LSTM(512,return_sequences = True,input_shape=(maxlen,len(chars))))
    model.add(Dropout(0.2))
    model.add(LSTM(512,return_sequences=False))
    model.add(Dropout(0.2))
    model.add(Dense(len(chars)))
    model.add(Activation('softmax'))
    print("Modelling finishing")
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

    for iteration in range(1,100):
        print('-'*50)
        print("Iteration",iteration)
        model.fit(X,y,batch_size=128,nb_epoch=1)

        start_index = random.randint(0,len(text)-maxlen-1)

        for diversity in [0.2, 0.5,0.8, 1.0,1.1, 1.2, 1.5]:
            print('----- diversity:', diversity)

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

            for iteration in range(120):
                x = np.zeros((1, maxlen, len(chars)))
                for t, char in enumerate(sentence):
                    x[0, t, char_indices[char]] = 1.
                preds = model.predict(x, verbose=0)[0]
                next_index = sample(preds,diversity)
                next_char = indices_char[next_index]

                generated += next_char
                sentence = sentence[1:] + next_char
            print(generated)