seq_len = 1000
x_train = np.zeros((1, seq_len, 1))  # [batch_size, seq_len, num_feat]
target = np.linspace(100, 0, num=seq_len).reshape(1, -1, 1)

from keras.models import Model
from keras.layers import LSTM, Dense, Input, TimeDistributed

x_in = Input((seq_len, 1))
seq1 = LSTM(8, return_sequences=True)(x_in)
dense1 = TimeDistributed(Dense(8))(seq1)
seq2 = LSTM(8, return_sequences=True)(dense1)
dense2 = TimeDistributed(Dense(8))(seq2)
out = TimeDistributed(Dense(1))(dense2)

model = Model(inputs=x_in, outputs=out)
model.compile(optimizer='adam', loss='mean_squared_error')

history = model.fit(x_train, target, batch_size=1, epochs=1000,
                    validation_split=0.)