def exp_ae_visual_features(): exp_name = 'ae_visual_features' out_base_dir = os.path.join( os.getcwd(), 'symlinks/exp/google_images/' + \ 'normalized_resnet_features_recon_loss_trained_on_google') exp_const = ExpConstants(exp_name, out_base_dir) exp_const.log_dir = os.path.join(exp_const.exp_dir, 'log') exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models') exp_const.batch_size = 10000 exp_const.lr = 1e-2 exp_const.num_epochs = 1000 feature_dir = os.path.join( os.getcwd(), 'symlinks/exp/google_images/' + \ 'normalized_resnet_features_recon_loss_trained_on_google') data_const = VisualFeaturesDatasetConstants(feature_dir) model_const = Constants() model_const.encoder = EncoderConstants() model_const.encoder.output_dims = 300 model_const.decoder = DecoderConstants() model_const.decoder.input_dims = 300 train_ae_visual.main(exp_const, data_const, model_const)
def exp_combine_glove_and_visual_features_with_ae(): exp_name = 'ae_glove_and_visual' out_base_dir = os.path.join( os.getcwd(), 'symlinks/exp/google_images/' + \ 'normalized_resnet_embeddings_recon_loss_trained_on_google') exp_const = ExpConstants(exp_name, out_base_dir) exp_const.log_dir = os.path.join(exp_const.exp_dir, 'log') exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models') exp_const.batch_size = 10000 exp_const.lr = 1e-2 exp_const.num_epochs = 1000 concat_embeddings_dir = os.path.join( os.getcwd(), 'symlinks/exp/google_images/' + \ 'normalized_resnet_embeddings_recon_loss_trained_on_google/' + \ 'concat_glove_and_visual') data_const = ConcatEmbedDatasetConstants(concat_embeddings_dir) data_const.embeddings_h5py = os.path.join(data_const.concat_dir, 'subset_visual_word_vecs.h5py') data_const.word_to_idx_json = os.path.join( data_const.concat_dir, 'subset_visual_word_vecs_idx.json') model_const = Constants() model_const.encoder = EncoderConstants() model_const.decoder = DecoderConstants() train_ae.main(exp_const, data_const, model_const)
def exp_save_ae_combined_glove_and_visual_features(): exp_name = 'ae_glove_and_visual' out_base_dir = os.path.join( os.getcwd(), 'symlinks/exp/google_images/' + \ 'normalized_resnet_embeddings_recon_loss_trained_on_google') exp_const = ExpConstants(exp_name, out_base_dir) exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models') exp_const.batch_size = 1000 concat_embeddings_dir = os.path.join( os.getcwd(), 'symlinks/exp/google_images/' + \ 'normalized_resnet_embeddings_recon_loss_trained_on_google/' + \ 'concat_glove_and_visual') data_const = ConcatEmbedDatasetConstants(concat_embeddings_dir) model_const = Constants() model_const.model_num = 400 model_const.encoder = EncoderConstants() model_const.decoder = DecoderConstants() save_ae_embeddings.main(exp_const, data_const, model_const)