def main(argv):
options = handle_options('set', argv)
mnist_data = load_dataset()
X_train = mnist_data[0].reshape(-1, 28, 28, 1)
sfx = gen_sfx_key(('adt', 'nblocks', 'block_size'), options)
empty_set_args = {'initializer': tf.random_uniform_initializer}
set_adt, set_pdt = gen_set_adt(X_train,
options,
store_args=options,
is_in_args=options,
size_args=options,
is_empty_args=options,
empty_set_args=empty_set_args,
nitems=options['nitems'],
batch_size=options['batch_size'])
graph = tf.get_default_graph()
savedir = mk_dir(sfx)
path_name = os.path.join(
os.environ['DATADIR'],
'graphs',
sfx,
)
tf.train.SummaryWriter(path_name, graph)
load_train_save(options, set_adt, set_pdt, sfx, savedir)
push, pop = pdt.call_fns
def prep_save(sess: Session, save: bool, dirname: str, params_file: str,
load: bool):
save_params = {}
if save or load:
saver = tf.train.Saver()
if save is True:
savedir = mk_dir(dirname=dirname)
save_params['savedir'] = savedir
save_params['saver'] = saver = tf.train.Saver()
if load is True:
saver.restore(sess, params_file)
return save_params
def main(argv):
global adt, pdt, sess
options = handle_options('number', argv)
sfx = gen_sfx_key(('adt', 'template', 'nblocks', 'block_size'), options)
zero_args = {'initializer': tf.random_uniform_initializer}
adt, pdt = gen_number_adt(options,
number_shape=(10, ),
succ_args=options,
add_args=options,
mul_args=options,
encode_args=options,
decode_args=options,
zero_args=zero_args,
batch_size=options['batch_size'])
savedir = mk_dir(sfx)
sess = load_train_save(options, adt, pdt, sfx, savedir)
def main(argv):
global queue_adt, queue_pdt, sess, X_train, sfx
options = handle_options('queue', argv)
mnist_data = load_dataset()
X_train = mnist_data[0].reshape(-1, 28, 28, 1)
sfx = gen_sfx_key(('adt', 'nblocks', 'block_size'), options)
empty_queue_args = {'initializer': tf.random_uniform_initializer}
queue_adt, queue_pdt = gen_queue_adt(X_train,
options,
enqueue_args=options,
nitems=options['nitems'],
dequeue_args=options,
empty_queue_args=empty_queue_args,
batch_size=options['batch_size'])
savedir = mk_dir(sfx)
sess = load_train_save(options, queue_adt, queue_pdt, sfx, savedir)
def main(argv):
global adt, pdt, sess, X_train, sfx
options = handle_options('eqstack', argv)
mnist_data = load_dataset()
X_train = mnist_data[0].reshape(-1, 28, 28, 1)
#sfx = gen_sfx_key(('adt', 'nblocks', 'block_size'), options)
sfx = gen_sfx_key(('adt', 'nitems'), options)
empty_eqstack_args = {'initializer': tf.random_uniform_initializer}
adt, pdt = eqstack_adt(X_train,
options,
push_args=options,
nitems=options['nitems'],
pop_args=options,
empty_eqstack_args=empty_eqstack_args,
batch_size=options['batch_size'])
savedir = mk_dir(sfx)
sess = load_train_save(options, adt, pdt, sfx, savedir)
def main(argv):
global adt, pdt, sess, X_train, sfx
options = handle_options('queue', argv)
mnist_data = load_dataset()
X_train = mnist_data[0].reshape(-1, 28, 28, 1)
#sfx = gen_sfx_key(('adt', 'nblocks', 'block_size'), options)
sfx = gen_sfx_key(('adt', 'nitems'), options)
empty_queue_args = {'initializer': tf.random_uniform_initializer}
adt, pdt = queue_adt(X_train,
options,
push_args=options,
nitems=options['nitems'],
pop_args=options,
empty_queue_args=empty_queue_args,
batch_size=options['batch_size'])
datadir = os.path.join(os.environ['DATADIR'], "pdt")
savedir = mk_dir(sfx, datadir=datadir)
options['sfx'] = sfx
sess = train(adt, pdt, options, savedir, sfx)