  Hyperparameters tuning is a time-consuming approach, particularly when the
architecture of the neural network is decided as part of this process. For
instance, in convolutional neural networks (CNNs), the selection of the number
and the characteristics of the hidden (convolutional) layers may be decided.
This implies that the search process involves the training of all these
candidate network architectures.
  This paper describes a proposal to reuse the weights of hidden
(convolutional) layers among different trainings to shorten this process. The
rationale is that if a set of convolutional layers have been trained to solve a
given problem, the weights calculated in this training may be useful when a new
convolutional layer is added to the network architecture.
  This idea has been tested using the CIFAR-10 dataset, testing different CNNs
architectures with up to 3 convolutional layers and up to 3 fully connected
layers. The experiments compare the training time and the validation loss when
reusing and not reusing convolutional layers. They confirm that this strategy
reduces the training time while it even increases the accuracy of the resulting
neural network. This finding opens up the future possibility of integrating
this strategy in existing AutoML methods with the purpose of reducing the total
search time.
