import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.utils import np_utils
# fix random seed for reproducibility
numpy.random.seed(7)
def process_data():
# define the raw dataset
alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# create mapping of characters to integers (0-25) and the reverse
char_to_int = dict((c, i) for i, c in enumerate(alphabet))
int_to_char = dict((i, c) for i, c in enumerate(alphabet))
seq_length =3
sample_length=len(alphabet)
dataX = []
dataY = []
for i in range(0, len(alphabet) - seq_length, 1):
seq_in = alphabet[i:i + seq_length]
# seq_in = alphabet[i]
seq_out = alphabet[i + seq_length]
dataX.append([char_to_int[char] for char in seq_in])
dataY.append(char_to_int[seq_out])
# print (seq_in, '->', seq_out)
# print(dataX)
# reshape X to be [samples, time steps, features]
a=len(dataX)
b=a/2
# print(a/2)
# X = numpy.reshape(dataX, (len(dataX), 1,seq_length))#timesteps这个参数，我们设置了1
X = numpy.reshape(dataX, (len(dataX),seq_length, 1))#timesteps这个参数，此处设置了3
# X = numpy.reshape(dataX, (12, 1,seq_length ))#lstm要求三维的输入，所以需要将原始数据转成3维的,这里将原始数据做成了24个矩阵，每个矩阵是1行1列的，
#当然可以做成12个矩阵，每个矩阵是1行2列的
# normalize，归一化
X = X / float(len(alphabet))
#可以把这个问题当作是一个序列的分类问题，26个不同的字母代表了不同的类别，我们用keras的内置的 to_categorical()函数把datay进行 one——hot编码，作为输出层的结果。
# one hot encode the output variable
y = np_utils.to_categorical(dataY)
# print(X.shape[1], X.shape[2])
return X,y,int_to_char,dataX,sample_length
def buile_model():
# create and fit the model
model = Sequential()
model.add(LSTM(128, input_shape=(X.shape[1], X.shape[2])))#确定输入数据是多少行，多少列的,在单层的lstm下，若神经元个数为32训练313个epoch后，准确度达到100%
#若神经元个数达到128，可以在训练到175个epoch后，准确度达到100%
# model.add(LSTM(32))
model.add(Dense(y.shape[1], activation='softmax'))#输出应该是多少类，是由输出的字母类别数目决定的
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def train_model(model,X, y):
for i in range(0,500):
model.fit(X, y, epochs=1, batch_size=1, verbose=2)#模型进行训练
if i% 10 == 0:
model.save_weights("./save/abcd.h5")#每10个迭代保存依次模型
# summarize performance of the model
scores = model.evaluate(X, y, verbose=0)
print("Model Accuracy: %.2f%%" % (scores[1]*100))
print('here',i)
def predict(dataX,model,sample_length):
# demonstrate some model predictions
model.load_weights("./save/abcd.h5")#预测时先载入训练好的权重
for pattern in dataX:
# print(pattern)
# x = numpy.reshape(pattern, (1, 1, len(pattern)))#这个是在time_step=1时用
x = numpy.reshape(pattern, (1, len(pattern),1 ))#这个是在time_step=3时用
x = x / float(sample_length)
prediction = model.predict(x, verbose=0)
index = numpy.argmax(prediction)
result = int_to_char[index]
seq_in = [int_to_char[value] for value in pattern]
print (seq_in, "->", result)
if __name__ == '__main__':
X, y,int_to_char,dataX,sample_length=process_data()
model=buile_model()
train_model(model,X, y)
# predict(dataX,model,sample_length)
'''
authour:wanghua
date:2018/3/16
探讨下输入，预测之间维度的关系
如何增加多层的lstm
'''
import numpy
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.utils import np_utils
# fix random seed for reproducibility
numpy.random.seed(7)
def process_data():
# define the raw dataset
alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# create mapping of characters to integers (0-25) and the reverse
char_to_int = dict((c, i) for i, c in enumerate(alphabet))
int_to_char = dict((i, c) for i, c in enumerate(alphabet))
seq_length =3
sample_length=len(alphabet)
dataX = []
dataY = []
for i in range(0, len(alphabet) - seq_length, 1):
seq_in = alphabet[i:i + seq_length]
# seq_in = alphabet[i]
seq_out = alphabet[i + seq_length]
dataX.append([char_to_int[char] for char in seq_in])
dataY.append(char_to_int[seq_out])
# print (seq_in, '->', seq_out)
# print(dataX)
# reshape X to be [samples, time steps, features]
a=len(dataX)
b=a/2
# print(a/2)
# X = numpy.reshape(dataX, (len(dataX), 1,seq_length))#timesteps这个参数，我们设置了1
X = numpy.reshape(dataX, (len(dataX),seq_length, 1))#timesteps这个参数，此处设置了3
# X = numpy.reshape(dataX, (12, 1,seq_length ))#lstm要求三维的输入，所以需要将原始数据转成3维的,这里将原始数据做成了24个矩阵，每个矩阵是1行1列的，
#当然可以做成12个矩阵，每个矩阵是1行2列的
# normalize，归一化
X = X / float(len(alphabet))
#可以把这个问题当作是一个序列的分类问题，26个不同的字母代表了不同的类别，我们用keras的内置的 to_categorical()函数把datay进行 one——hot编码，作为输出层的结果。
# one hot encode the output variable
y = np_utils.to_categorical(dataY)
# print(X.shape[1], X.shape[2])
return X,y,int_to_char,dataX,sample_length
def buile_model():
# create and fit the model
model = Sequential()
model.add(LSTM(128,dropout_W=0.2, dropout_U=0.2, input_shape=(X.shape[1], X.shape[2]),return_sequences=True))#确定输入数据是多少行，多少列的,在单层的lstm下，若神经元个数为32训练313个epoch后，准确度达到100%
#此处通过设置return_sequences=True，可以添加多层的lstm
model.add(LSTM(256, return_sequences=False))#最后一层的lstm，return_sequences=False
#若神经元个数达到128，可以在训练到175个epoch后，准确度达到100%
# model.add(LSTM(32))
model.add(Dense(y.shape[1], activation='softmax'))#输出应该是多少类，是由输出的字母类别数目决定的
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def train_model(model,X, y):
for i in range(0,500):
model.fit(X, y, epochs=1, batch_size=1, verbose=2)#模型进行训练
if i% 10 == 0:
model.save_weights("./save/abcd.h5")#每10个迭代保存依次模型
# summarize performance of the model
scores = model.evaluate(X, y, verbose=0)
print("Model Accuracy: %.2f%%" % (scores[1]*100))
print('here',i)
def predict(dataX,model,sample_length):
# demonstrate some model predictions
model.load_weights("./save/abcd.h5")#预测时先载入训练好的权重
for pattern in dataX:
# print(pattern)
# x = numpy.reshape(pattern, (1, 1, len(pattern)))#这个是在time_step=1时用
x = numpy.reshape(pattern, (1, len(pattern),1 ))#这个是在time_step=3时用
x = x / float(sample_length)
prediction = model.predict(x, verbose=0)
index = numpy.argmax(prediction)
result = int_to_char[index]
seq_in = [int_to_char[value] for value in pattern]
print (seq_in, "->", result)
if __name__ == '__main__':
X, y,int_to_char,dataX,sample_length=process_data()
model=buile_model()
train_model(model,X, y)
# predict(dataX,model,sample_length)