py_h = f_py_h(train.X[:100])
train.save_images(py_h, path.join(out_path, 'py_h.png'))
# ========================================================================
print_section('Getting gradients and building optimizer.')
f_grad_shared, f_grad_updates, learning_args = set_optimizer(
inps, cost, tparams, constants, updates, extra_outs, **learning_args)
# ========================================================================
print_section('Actually running (main loop)')
monitor = SimpleMonitor()
main_loop(
train, valid, tparams,
f_grad_shared, f_grad_updates, f_test, f_test_keys,
test_every=test_every,
save=save,
save_images=save_images,
monitor=monitor,
out_path=out_path,
name=name,
extra_outs_keys=extra_outs_keys,
**learning_args)
if __name__ == '__main__':
parser = make_argument_parser()
parser.add_argument('-i', '--save_images', action='store_true')
args = parser.parse_args()
exp_dict = set_experiment(args)
train(**exp_dict)
[X], cost, tparams, constants, updates, extra_outs, **learning_args)
# ========================================================================
print_section('Actually running (main loop)')
monitor = SimpleMonitor()
main_loop(train,
valid,
tparams,
f_grad_shared,
f_grad_updates,
f_test,
f_test_keys,
f_extra=f_update_partition,
test_every=test_every,
save=save,
save_images=save_images,
monitor=monitor,
out_path=out_path,
name=name,
extra_outs_keys=extra_outs_keys,
**learning_args)
if __name__ == '__main__':
parser = make_argument_parser()
parser.add_argument('-i', '--save_images', action='store_true')
args = parser.parse_args()
exp_dict = set_experiment(args)
train(**exp_dict)