# parse command line arguments
parser = NgraphArgparser()
parser.add_argument(
'--plot_interval',
type=int,
default=200,
help='save generated images with a period of this many iterations')
parser.add_argument('--seed', type=int, default=0, help='random seed')
args = parser.parse_args()
np.random.seed(args.rng_seed)
args.batch_size = 32
# Create the dataloader
train_data, valid_data = MNIST(args.data_dir).load_data()
train_set = ArrayIterator(train_data, args.batch_size)
# noise source
noise_dim = (2, 1, 3, 3)
noise_generator = Noise(train_set.ndata,
shape=noise_dim + (args.batch_size, ),
seed=args.seed)
# generator network
g_scope = 'generator'
filter_init = GaussianInit(var=0.05)
relu = Rectlin(slope=0)
deconv_layers = [
Deconvolution((1, 1, 16),
def train_mnist_mlp(transformer_name,
data_dir=None,
rng_seed=12,
batch_size=128,
train_iter=10,
eval_iter=10):
assert transformer_name in ['cpu', 'hetr']
assert isinstance(rng_seed, int)
# Apply this metadata to graph regardless of transformer,
# but it is ignored for non-HeTr case
hetr_device_ids = (0, 1)
# use consistent rng seed between runs
np.random.seed(rng_seed)
# Data
train_data, valid_data = MNIST(path=data_dir).load_data()
train_set = ArrayIterator(train_data,
batch_size,
total_iterations=train_iter)
valid_set = ArrayIterator(valid_data, batch_size)
inputs = train_set.make_placeholders()
ax.Y.length = 10
# Model
with ng.metadata(device_id=hetr_device_ids, parallel=ax.N):
seq1 = Sequential([
Preprocess(functor=lambda x: x / 255.),
Affine(nout=100, weight_init=GaussianInit(), activation=Rectlin()),
Affine(axes=ax.Y,
weight_init=GaussianInit(),
activation=Logistic())
])
train_prob = seq1(inputs['image'])
train_loss = ng.cross_entropy_binary(
train_prob, ng.one_hot(inputs['label'], axis=ax.Y))
optimizer = GradientDescentMomentum(0.1, 0.9)
batch_cost = ng.sequential(
[optimizer(train_loss),
ng.mean(train_loss, out_axes=())])
train_outputs = dict(batch_cost=batch_cost)
with Layer.inference_mode_on():
inference_prob = seq1(inputs['image'])
errors = ng.not_equal(ng.argmax(inference_prob, out_axes=[ax.N]),
inputs['label'])
eval_loss = ng.cross_entropy_binary(
inference_prob, ng.one_hot(inputs['label'], axis=ax.Y))
eval_outputs = dict(cross_ent_loss=eval_loss, misclass_pct=errors)
# Runtime
with closing(
ngt.make_transformer_factory(transformer_name)()) as transformer:
train_computation = make_bound_computation(transformer, train_outputs,
inputs)
loss_computation = make_bound_computation(transformer, eval_outputs,
inputs)
train_costs = list()
for step in range(train_iter):
out = train_computation(next(train_set))
train_costs.append(float(out['batch_cost']))
ce_loss = list()
for step in range(eval_iter):
out = loss_computation(next(valid_set))
ce_loss.append(np.mean(out['cross_ent_loss']))
return train_costs, ce_loss