def train_autoencoder(data):
""" Train an autoencoder
Args:
data: A fucntion that provides the input data for the network.
Returns:
LCMC and MSE metric for the autoencoder that has been trained.
"""
# Setup
layers = calculate_layer_sizes(784, 200, 0.5)
# Generations is needed to correct the number of training steps to match
# the number of steps used in the evolutionary algorithm
generations = 10
config = NeuralNetworkConfig()
# Start with a mutated config to have some variation between runs
config.mutate()
config.num_steps *= generations
autoencoder = Autoencoder(config, layers[:2])
# Training
autoencoder.train(data)
for index, layer_size in enumerate(layers[2:]):
autoencoder.append_layer(layer_size)
autoencoder.train(data, restore_layers=index+1)
# Evaluation
autoencoder.save_history()
print(autoencoder.config)
print(autoencoder.save_path)
# autoencoder.reconstruct_images(data)
return lcmc_fitness(autoencoder, data, True), mse_fitness(autoencoder, data, True)
