...
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
...

dim = 10
time = 20
batch_size = 20

def baseline_model():
    model = Sequential()
    model.add(LSTM(12, stateful=True, batch_input_shape=(batch_size, time, dim)))
    model.add(Dense(3, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

model = KerasClassifier(build_fn=baseline_model, batch_size=batch_size, shuffle=False)

for i in range(100):
    model.fit(X_train, y_train, epochs=1)
    model.reset_states()

for i in range(100):
    model.fit(X_train, y_train, epochs=1)