def init (version = None, command = None, options = None):
options = to_seq (options)
condition = common.check_init_parameters ('darwin', None, ('version', version))
command = common.get_invocation_command ('darwin', 'g++', command)
common.handle_options ('darwin', condition, command, options)
gcc.init_link_flags ('darwin', 'darwin', condition)
def init(version=None, command=None, options=None):
options = to_seq(options)
condition = common.check_init_parameters('darwin', None, ('version', version))
command = common.get_invocation_command('darwin', 'g++', command)
common.handle_options('darwin', condition, command, options)
gcc.init_link_flags('darwin', 'darwin', condition)
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 main(argv):
options = handle_options('map', argv)
sfx = gen_sfx_key(('adt', 'nblocks', 'block_size'), options)
empty_map_args = {'initializer': tf.random_uniform_initializer}
map_adt, map_pdt = gen_map_adt(1,
10,
options,
dist_args=options,
add_dist_args=options,
empty_map_args=empty_map_args,
batch_size=options['batch_size'])
graph = tf.get_default_graph()
savedir = mk_dir(sfx)
load_train_save(options, map_adt, map_pdt, sfx, savedir)
push, pop = map_pdt.call_fns
def main(argv):
global adt, pdt, sess, X_train, sfx
options = handle_options('stack', argv)
mnist_data = load_dataset()
X_train = mnist_data[0].reshape(-1, 28, 28, 1)
empty_stack_args = {'initializer': tf.random_uniform_initializer}
adt, pdt = stack_adt(X_train,
options,
push_args=options,
nitems=options['nitems'],
pop_args=options,
empty_stack_args=empty_stack_args,
batch_size=options['batch_size'])
options['dirname'] = gen_sfx_key(('adt', 'nitems'), options)
sess = train(adt, pdt, options)
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)
def main(argv):
print("ARGV", argv)
options = handle_options('atari', argv)
options['dirname'] = gen_sfx_key(('adt', ), options)
adt, sess = run(options)
def main():
options = handle_options('scalar_field', argv)
run(options)