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02_train_vae.py
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02_train_vae.py
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#python 02_train_vae.py --new_model
from vae.arch import VAE
import argparse
import numpy as np
import config
def main(args):
start_batch = args.start_batch
max_batch = args.max_batch
new_model = args.new_model
vae = VAE()
if not new_model:
try:
vae.set_weights('./vae/weights.h5')
except:
print("Either set --new_model or ensure ./vae/weights.h5 exists")
raise
for batch_num in range(start_batch, max_batch + 1):
print('Building batch {}...'.format(batch_num))
first_item = True
for env_name in config.train_envs:
try:
new_data = np.load('./data/obs_data_' + env_name + '_' + str(batch_num) + '.npy')
if first_item:
data = new_data
first_item = False
else:
data = np.concatenate([data, new_data])
print('Found {}...current data size = {} episodes'.format(env_name, len(data)))
except:
pass
if first_item == False: # i.e. data has been found for this batch number
data = np.array([item for obs in data for item in obs])
vae.train(data)
else:
print('no data found for batch number {}'.format(batch_num))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description=('Train VAE'))
parser.add_argument('--start_batch', type=int, default = 0, help='The start batch number')
parser.add_argument('--max_batch', type=int, default = 0, help='The max batch number')
parser.add_argument('--new_model', action='store_true', help='start a new model from scratch?')
args = parser.parse_args()
main(args)