def test_inception(devs, kv_type):
# guarantee the same weight init for each run
mx.random.seed(0)
logging.basicConfig(level=logging.DEBUG)
(train, val) = common.cifar10(batch_size=128, input_shape=(3, 28, 28))
model = mx.model.FeedForward.create(ctx=devs,
symbol=common.inception(),
X=train,
eval_data=val,
kvstore=kv_type,
num_round=10,
learning_rate=0.1,
momentum=0.9,
wd=0.00001,
initializer=mx.init.Uniform(0.07))
return common.accuracy(model, val)
def test_inception(devs, kv_type):
# guarantee the same weight init for each run
mx.random.seed(0)
logging.basicConfig(level=logging.DEBUG)
(train, val) = common.cifar10(batch_size = 128, input_shape=(3,28,28))
model = mx.model.FeedForward.create(
ctx = devs,
symbol = common.inception(),
X = train,
eval_data = val,
kvstore = kv_type,
num_epoch = 10,
learning_rate = 0.1,
momentum = 0.9,
wd = 0.00001,
initializer = mx.init.Uniform(0.07))
return common.accuracy(model, val)
#!/usr/bin/env python
# pylint: skip-file
import common
import mxnet as mx
import logging
mx.random.seed(0)
logging.basicConfig(level=logging.DEBUG)
kv = mx.kvstore.create('dist_async')
(train, val) = common.cifar10(num_parts = kv.num_workers,
part_index = kv.rank,
batch_size = 128,
input_shape=(3,28,28))
devs = [mx.gpu(i) for i in range(2)]
model = mx.model.FeedForward.create(
ctx = devs,
kvstore = kv,
symbol = common.inception(),
X = train,
eval_data = val,
num_epoch = 20,
learning_rate = 0.05,
momentum = 0.9,
wd = 0.00001,
initializer = mx.init.Uniform(0.07))
common.accuracy(model, val)
