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)
