from __future__ import print_function
import argparse
import os
import h5py
import numpy as np
NUM_EPOCHS = 1
BATCH_SIZE = 600
LATENT_DIM = 292
RANDOM_SEED = 1337
def get_arguments():
parser = argparse.ArgumentParser(description='Molecular autoencoder network')
parser.add_argument('data', type=str, help='The HDF5 file containing preprocessed data.')
parser.add_argument('model', type=str,
help='Where to save the trained model. If this file exists, it will be opened and resumed.')
parser.add_argument('--epochs', type=int, metavar='N', default=NUM_EPOCHS,
help='Number of epochs to run during training.')
parser.add_argument('--latent_dim', type=int, metavar='N', default=LATENT_DIM,
help='Dimensionality of the latent representation.')
parser.add_argument('--batch_size', type=int, metavar='N', default=BATCH_SIZE,
help='Number of samples to process per minibatch during training.')
parser.add_argument('--random_seed', type=int, metavar='N', default=RANDOM_SEED,
help='Seed to use to start randomizer for shuffling.')
return parser.parse_args()
def main():
args = get_arguments()
np.random.seed(args.random_seed)
from molecules.model import MoleculeVAE
from molecules.utils import one_hot_array, one_hot_index, from_one_hot_array, \
decode_smiles_from_indexes, load_dataset
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
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)
)
if __name__ == '__main__':
main()
