[LSTM学习笔记4]How t o Develop Vanilla LSTMs

一.Vanilla LSTM
这是在LSTM原始论文中定义的标准的也是最简单的LSTM模型，可以用于解决简单的序列问题，其结构如下图：

1.Keras实现

model = Sequential()  
model.add(LSTM(..., input_shape=(...)))  
model.add(Dense(...))

2.实例：Echo Sequence Predition Problem
给定输入序列，预测给定time step的输出，类似于：

代码实现：

from random import randint
from numpy import array
from numpy import argmax
import keras
from keras.layers import Dense,LSTM
from keras.models import Sequential


def generate_sequence(length,n_features):
    return [randint(0,n_features-1) for _ in range(length)]

# one hot encode sequence
def one_hot_encode(sequence, n_features):
    encoding = list ()
    for value in sequence:
        vector = [0 for _ in range(n_features)]
        vector[value] = 1
        encoding.append(vector)
    return array(encoding)

# decode a one hot encoded string
def one_hot_decode(encoded_seq):
    return [argmax(vector) for vector in encoded_seq]

# generate one example for an lstm
def generate_example(length, n_features, out_index):
    # generate sequence
    sequence = generate_sequence(length, n_features)
    # one hot encode
    encoded = one_hot_encode(sequence, n_features)
    # reshape sequence to be 3D
    X = encoded.reshape((1, length, n_features))
    # select output
    y = encoded[out_index].reshape(1, n_features)
    return X, y

#define model
length = 5
n_features = 10
out_index = 2
model = Sequential()
#25 memory units,fully connect layer(10 neurons)
model.add(LSTM(25,input_shape=(length,n_features)))
model.add(Dense(n_features,activation='softmax'))
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['acc'])
print(model.summary())

#fit the model
for i in range(10000):
    X,y = generate_example(length,n_features,out_index)
    model.fit(X,y,epochs=1,verbose=2)

# evaluate model
correct = 0
for i in range(100):
    X, y = generate_example(length, n_features, out_index)
    yhat = model.predict(X)
    if one_hot_decode(yhat) == one_hot_decode(y):
        correct += 1
print('Accuracy:%f' %((correct/100)*100.0))

输出为：

 

Accuracy:100.000000

# prediction on new data
X, y = generate_example(length=5, n_features=10, out_index=2)
yhat = model.predict(X)
print ('Sequence: %s' % [one_hot_decode(x) for x in X])
print ('Expected: %s'% one_hot_decode(y))
print ('Predicted : %s' % one_hot_decode(yhat))

Sequence: [[1, 6, 4, 4, 8]]
Expected: [4]
Predicted : [4]