from keras.models import Model
from keras.layers import Input
from keras.layers import LSTM
import numpy as np


batch_size = 1
timesteps = 1
input_features_count = 1
output_features_count = 1
inputs1 = Input(batch_shape=(batch_size, timesteps, input_features_count))
lstm1 = LSTM(units = output_features_count, return_sequences=True, return_state=True, stateful=True)(inputs1)
model = Model(inputs=inputs1, outputs=lstm1)
data = array([0.1]).reshape((batch_size, timesteps, input_features_count))
pred_seq, state_h, state_c = model.predict(data)
print(pred_seq.shape) # (1, 1, 1)
print(state_h.shape) # (1, 1)
print(state_c.shape) # (1, 1)


batch_size = 1
timesteps = 2
input_features_count = 3
output_features_count = 5
inputs1 = Input(batch_shape=(batch_size, timesteps, input_features_count))
lstm1 = LSTM(units = output_features_count, return_sequences=True, return_state=True, stateful=True)(inputs1)
model = Model(inputs=inputs1, outputs=lstm1)
data = array([1,2,3,4,5,6]).reshape((batch_size, timesteps, input_features_count))
pred_seq, state_h, state_c = model.predict(data)
print(pred_seq.shape) # (1, 2, 5)
print(state_h.shape) # (1, 5)
print(state_c.shape) # (1, 5)