def visualize_model(args): model = MoleculeVAE() data, charset = load_dataset(args.data, split=False) if os.path.isfile(args.model): model.load(charset, args.model) else: raise ValueError("Model file %s doesn't exist" % args.model) plot(model.autoencoder, to_file=args.outfile)
def main(): args = get_arguments() model = MoleculeVAE() data, data_test, charset = load_dataset(args.data) if os.path.isfile(args.model): model.load(charset, args.model, latent_rep_size=args.latent_dim) else: raise ValueError("Model file %s doesn't exist" % args.model) if not args.visualize: x_latent = model.encoder.predict(data) np.savetxt(sys.stdout, x_latent, delimiter='\t') else: visualize_latent_rep(args, model, data)
def main(): args = get_arguments() model = MoleculeVAE() data, charset = load_dataset('data/all_smiles_120_one_hot.h5', split=False) if os.path.isfile(args.model): model.load(charset, args.model) else: raise ValueError("Model file %s doesn't exist" % args.model) sampled = model.autoencoder.predict(data[100].reshape( 1, 120, len(charset))).argmax(axis=2)[0] mol = decode_smiles_from_indexes(map(from_one_hot_array, data[100]), charset) sampled = decode_smiles_from_indexes(sampled, charset) print(mol) print(sampled)
def main(): args = get_arguments() model = MoleculeVAE() if args.target == 'autoencoder': autoencoder(args, model) elif args.target == 'encoder': encoder(args, model) elif args.target == 'decoder': decoder(args, model)
def main(): args = get_arguments() data_train, data_test, charset = load_dataset(args.data) model = MoleculeVAE() if os.path.isfile(args.model): model.load(charset, args.model, latent_rep_size=args.latent_dim) else: model.create(charset, latent_rep_size=args.latent_dim) checkpointer = ModelCheckpoint(filepath=args.model, verbose=1, save_best_only=True) reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.0001) model.autoencoder.fit(data_train, data_train, shuffle=True, nb_epoch=args.epochs, batch_size=args.batch_size, callbacks=[checkpointer, reduce_lr], validation_data=(data_test, data_test))
def main(): args = get_arguments() model = MoleculeVAE() data, data_test, charset = load_dataset(args.data) if os.path.isfile(args.model): model.load(charset, args.model, latent_rep_size=args.latent_dim) else: raise ValueError("Model file %s doesn't exist" % args.model) x_latent = model.encoder.predict(data) if not args.visualize: if not args.save_h5: np.savetxt(sys.stdout, x_latent, delimiter='\t') else: h5f = h5py.File(args.save_h5, 'w') h5f.create_dataset('charset', data=charset) h5f.create_dataset('latent_vectors', data=x_latent) h5f.close() else: visualize_latent_rep(args, model, x_latent)