TimeDistributed in LSTM

一对一的LSTM

# one input and one output
from numpy import array
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
# prepare sequence
length = 5
seq = array([i/float(length) for i in range(length)])
X = seq.reshape(len(seq), 1, 1)
y = seq.reshape(len(seq), 1)
# define LSTM configuration
n_neurons = length
n_batch = length
n_epoch = 1000
# create LSTM
model = Sequential()
model.add(LSTM(n_neurons, input_shape=(1, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
print(model.summary())
# train LSTM
model.fit(X, y, epochs=n_epoch, batch_size=n_batch, verbose=2)
# evaluate
result = model.predict(X, batch_size=n_batch, verbose=0)
for value in result:
    print('%.1f' % value)

多对一的LSTM

#multinput to one output 
    
from numpy import array
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
# prepare sequence
length = 5
seq = array([i/float(length) for i in range(length)])
X = seq.reshape(1, length, 1)
y = seq.reshape(1, length)
# define LSTM configuration
n_neurons = length
n_batch = 1
n_epoch = 500
# create LSTM
model = Sequential()
model.add(LSTM(n_neurons, input_shape=(length, 1)))
model.add(Dense(length))
model.compile(loss='mean_squared_error', optimizer='adam')
print(model.summary())
# train LSTM
model.fit(X, y, epochs=n_epoch, batch_size=n_batch, verbose=2)
# evaluate
result = model.predict(X, batch_size=n_batch, verbose=0)
for value in result[0,:]:
    print('%.1f' % value)

多对多的LSTM

# multinput and multioutput  
from numpy import array
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import TimeDistributed
from keras.layers import LSTM
# prepare sequence
length = 5
seq = array([i/float(length) for i in range(length)])
X = seq.reshape(1, length, 1)
y = seq.reshape(1, length, 1)
# define LSTM configuration
n_neurons = length
n_batch = 1
n_epoch = 1000
# create LSTM
model = Sequential()
model.add(LSTM(n_neurons, input_shape=(length, 1), return_sequences=True))
model.add(TimeDistributed(Dense(1)))
model.compile(loss='mean_squared_error', optimizer='adam')
print(model.summary())
# train LSTM
model.fit(X, y, epochs=n_epoch, batch_size=n_batch, verbose=2)
# evaluate
result = model.predict(X, batch_size=n_batch, verbose=0)
for value in result[0,:,0]:
    print('%.1f' % value)