model = Sequential()
model.add(LSTM(units = 50, return_sequences = True , input_shape = (X_train.shape[1], 1)))
model.add(Dense(units = 1))
model.compile(optimizer = 'adam', loss = 'mean_squared_error')
model.fit(X_train, y_train, epochs = 100, batch_size = 32)

new_model = Sequential()
new_model.add(LSTM(units = 50, return_sequences = True , input_shape = (1 , 60 , 1 )))
new_model.add(Dense(1))
old_weights = model.get_weights()
new_model.set_weights(old_weights)
new_model.compile(optimizer = 'adam', loss = 'mean_squared_error')

inputs = []
for i in range(10):
    inputs = dataset_total[len(dataset_total) - 60:].values
    inputs = np.reshape(inputs, (1 , 60, 1))
    predicted = new_model.predict(inputs)
    inputs.append(predicted)