#!/usr/bin/env python3 # -*- coding: utf-8 -*- import context from src.models import autoencoder from src.networks.autoencoder import AutoEncoder if __name__ == '__main__': # creation and training of an auto-encoder, to pre-process the data ae1 = AutoEncoder(model=autoencoder.Model1((400, 200, 1)), batch_size=4, dataset_path='.') # Adapt to your path ae1.compile() hae1 = ae1.fit(epochs=10, repeat=1, fname='autoencoder-Model1', fname_enc='encoder-Model1') ae1.save_losses(hae1, 'encoder-Model1') # saving the losses # Create the encoded dataset with the encoder ae1.encode_dataset('.', '.')
#!/usr/bin/env python3 # -*- coding: utf-8 -*- ###################################################################### # Train a new Auto-Encoder and save the encoder # # # ###################################################################### import context import os from src.models import autoencoder from src.networks.autoencoder import AutoEncoder if __name__ == '__main__': dataset_path = '../GREGOIRE/dataset_new' # '.' name = 'Model1_test17' # creation and training of an auto-encoder, to pre-process the data ae1 = AutoEncoder(model=autoencoder.Model1((520, 480, 1)), batch_size=64, dataset_path=dataset_path) # Adapt to your path ae1.compile() hae1 = ae1.fit(epochs=2, repeat=1, fname=('autoencoder-'+name), fname_enc=('encoder-'+name)) ae1.save_losses(hae1, 'encoder-'+name) # saving the losses # Create the encoded dataset with the encoder #ae1 = AutoEncoder(load_models='encoder-Model1_test5', version=0) if not os.path.exists(dataset_path + '/train_encoded_TS/'): os.mkdir(dataset_path + '/train_encoded_TS/') ae1.encode_dataset(dataset_path, dataset_path)