LSTM文本生成

data = open("datas/any").read()
#移除换行符
data = data.replace("\n", "").replace("\r", "")
data

image.png

#字符去重
letters = list(set(data))
num_letters = len(letters)

#建立字典
int_to_char = {a:b for a,b in enumerate(letters)}
char_to_int = {b:a for a,b in enumerate(letters)}

#17个预测第18个字符
time_step = 17

import numpy as np
from keras.utils import to_categorical
#滑动窗口提取数据
def extract_data(data, slide):
    x = []
    y = []
    for i in range(len(data) -slide):
        x.append([a for a in data[i:i+slide]])
        y.append(data[i+slide])
    return x, y
#字符到数字的批量转化
def char_to_int_Data(x, y, char_to_int):
    x_to_int = []
    y_to_int = []
    for i in range(len(x)):
        x_to_int.append([char_to_int[char] for char in x[i]])
        y_to_int.append([char_to_int[char] for char in y[i]])
    return x_to_int, y_to_int
#实现输入字符文章的批量处理，输入整个字符，滑动窗口大小，转化字典
def data_preprocessing(data, slide, num_letters, char_to_int):
    char_Data = extract_data(data, slide)
    int_Data = char_to_int_Data(char_Data[0], char_Data[1], char_to_int)
    Input = int_Data[0]
    Output = list(np.array(int_Data[1]).flatten())
    Input_RESHAPE = np.array(Input).reshape(len(Input), slide)
    new = np.random.randint(0, 10, size=[Input_RESHAPE.shape[0], Input_RESHAPE.shape[1],
                                        num_letters])
    for i in range(Input_RESHAPE.shape[0]):
        for j in range(Input_RESHAPE.shape[1]):
            new[i, j, :] = to_categorical(Input_RESHAPE[i, j], num_classes=num_letters)
    return new, Output

#提取x和y
X, y = data_preprocessing(data, time_step, num_letters, char_to_int)

#数据分离
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=10)
X_train.shape   #(576, 17, 17)

#one hot
y_train_category = to_categorical(y_train, num_letters)

#建立模型
from keras.models import Sequential
from keras.layers import Dense, LSTM
model = Sequential()
model.add(LSTM(units=20, input_shape=(X_train.shape[1], X_train.shape[2]), activation="relu"))
model.add(Dense(units=num_letters, activation="softmax"))
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics="accuracy")
model.summary()

#训练
model.fit(X_train, y_train_category, batch_size=1000, epochs=5)

#预测训练集
y_train_predict = model.predict(X_train)
y_train_predict = np.argmax(y_train_predict, axis=1)
y_train_predict_char = [int_to_char[i] for i in y_train_predict]

from sklearn.metrics import accuracy_score
accuracy_score(y_train, y_train_predict)   #准确率低可以重复训练

#预测测试集
y_test_predict = model.predict(X_test)
y_test_predict = np.argmax(y_test_predict, axis=1)
accuracy_score(y_test, y_test_predict)
y_test_predict_char = [int_to_char[i] for i in y_test_predict]

#用语句预测
new_letters = "this is a good day. happy to meet you.Nice day.this is a good day."
x_new, y_new = data_preprocessing(new_letters, time_step, num_letters, char_to_int)
y_new_predict = model.predict(x_new)
y_new_predict = np.argmax(y_new_predict, axis=1)
y_new_predict_char = [int_to_char[i] for i in y_new_predict]
for i in range(0, x_new.shape[0]-time_step):
    print(new_letters[i:i+10], y_new_predict_char[i])