一對(duì)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)

多對(duì)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)

多對(duì)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)

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