LSTM简单的例子

LSTM生成评论的例子

使用前10个字推出后面的1个字

import numpy
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils

# 读取txt文件
filename = 'comments.txt'
with open(filename, 'r', encoding='utf-8') as f:
    raw_text = f.read().lower()

# 创建文字和对应数字字典
chars = sorted(list(set(raw_text)))
char_to_int = dict((c, i) for i, c in enumerate(chars))
int_to_char = dict((i, c) for i, c in enumerate(chars))

# 对加载数据做总结
n_chars = len(raw_text)
n_vocab = len(chars)
print("总的文字数:", n_chars)
print("总的文字类别:", n_vocab)

# 生成数据集,转化为输入向量和输出向量
seq_length = 10
dataX = []
dataY = []
for i in range(0, n_chars - seq_length, 1):
    seq_in = raw_text[i: i + seq_length]	# 输入前10个字
    seq_out = raw_text[i + seq_length]		# 输出后 1个字
    dataX.append([char_to_int[char] for char in seq_in]) 	# 将字转化成对应的序号
    dataY.append(char_to_int[seq_out])
n_patterns = len(dataX) 					# 数据集的大小
print("Total Patterns: ", n_patterns)

# 将X重新转化为[samples, time_steps, features]形状
X = numpy.reshape(dataX, (n_patterns, seq_length, 1))
X = X / n_vocab
y = np_utils.to_categorical(dataY)

# 定义LSTM
model = Sequential()
model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2])))   # 输入维度(10, 1)
model.add(Dropout(0.2))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')

filepath = "./LSTM/weights-improvement.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
callbacks_list = [checkpoint]

# 模型训练
epochs = 1000
model.fit(X, y, epochs=epochs, batch_size=128, callbacks=callbacks_list)

## 模型预测 ====
input = '杭州西湖天下闻名,西'
pattern = [char_to_int[value] for value in input]
print("输入:")
print(''.join([int_to_char[value] for value in pattern]))
print("输出:")
for i in range(1000):
    x = numpy.reshape(pattern, (1, len(pattern), 1))
    x = x / float(n_vocab)
    prediction = model.predict(x, verbose=0)
    index = numpy.argmax(prediction)
    result = int_to_char[index]
    print(result, end='')
    seq_in = [int_to_char[value] for value in pattern]
    pattern.append(index)
    pattern = pattern[1: len(pattern)] 			   # 这里的pattern永远都是10个字
print("\n生成完毕。")

转载自

### Python 中 LSTM 的示例代码教程 #### 导入必要的库 为了构建并训练LSTM模型,首先需要导入所需的函数和类。这假设已经安装了一个完整的SciPy环境以及Keras深度学习库。 ```python import numpy as np import matplotlib.pyplot as plt import pandas as pd from keras.models import Sequential from keras.layers import Dense, LSTM from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error ``` #### 加载数据集 接下来加载用于时间序列预测的数据集,并对其进行预处理以便于后续建模操作。 ```python dataframe = pd.read_csv('international-airline-passengers.csv', usecols=[1], engine='python', skipfooter=3) dataset = dataframe.values dataset = dataset.astype('float32') ``` #### 数据缩放和平滑化 由于神经网络对于输入数值范围较为敏感,因此通常会对原始数据执行标准化或归一化转换来改善收敛性能。 ```python scaler = MinMaxScaler(feature_range=(0, 1)) dataset = scaler.fit_transform(dataset) train_size = int(len(dataset) * 0.67) test_size = len(dataset) - train_size train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:] print(f"Train:{len(train)}, Test:{len(test)}") ``` #### 创建适合LSTM的结构化数据 将时间序列重新构造成监督学习问题的形式,即给定过去的时间步长作为特征X,未来的一个时间点为目标y。 ```python def create_dataset(dataset, look_back=1): dataX, dataY = [], [] for i in range(len(dataset)-look_back-1): a = dataset[i:(i+look_back), 0] dataX.append(a) dataY.append(dataset[i + look_back, 0]) return np.array(dataX), np.array(dataY) look_back = 1 trainX, trainY = create_dataset(train, look_back) testX, testY = create_dataset(test, look_back) # reshape input to be [samples, time steps, features] trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) ``` #### 构建LSTM模型 定义一个简单的顺序模型架构,其中包含一层LSTM单元和全连接层来进行最终输出。 ```python model = Sequential() model.add(LSTM(4, input_shape=(1, look_back))) model.add(Dense(1)) model.compile(loss='mean_squared_error', optimizer='adam') history = model.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2) ``` #### 预测与评估 利用训练好的模型对未来值做出预测,并计算均方误差以衡量模型表现。 ```python trainPredict = model.predict(trainX) testPredict = model.predict(testX) # invert predictions trainPredict = scaler.inverse_transform(trainPredict) trainY = scaler.inverse_transform([trainY]) testPredict = scaler.inverse_transform(testPredict) testY = scaler.inverse_transform([testY]) # calculate root mean squared error trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0])) print('Train Score: %.2f RMSE' % (trainScore)) testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0])) print('Test Score: %.2f RMSE' % (testScore)) # shift train predictions for plotting trainPredictPlot = np.empty_like(dataset) trainPredictPlot[:] = np.nan trainPredictPlot[look_back:len(trainPredict)+look_back] = trainPredict # shift test predictions for plotting testPredictPlot = np.empty_like(dataset) testPredictPlot[:] = np.nan testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1] = testPredict # plot baseline and predictions plt.plot(scaler.inverse_transform(dataset)) plt.plot(trainPredictPlot) plt.plot(testPredictPlot) plt.show() ``` 上述过程展示了如何使用Python中的Keras框架实现基于LSTM的时间序列预测任务[^1]。
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