def test_lenet(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))
(train, val) = common.mnist(batch_size=100, input_shape=(1, 28, 28))
model = mx.model.FeedForward.create(ctx=devs,
kvstore=kv_type,
symbol=common.lenet(),
X=train,
num_epoch=3,
learning_rate=0.1,
momentum=0.9,
wd=0.00001)
return common.accuracy(model, val)
def test_lenet(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))
(train, val) = common.mnist(batch_size = 100, input_shape=(1,28,28))
model = mx.model.FeedForward.create(
ctx = devs,
kvstore = kv_type,
symbol = common.lenet(),
X = train,
num_round = 3,
learning_rate = 0.1,
momentum = 0.9,
wd = 0.00001)
return common.accuracy(model, val)
#!/usr/bin/env python
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.mnist(num_parts=kv.num_workers,
part_index=kv.rank,
batch_size=100,
input_shape=(1, 28, 28))
model = mx.model.FeedForward.create(ctx=mx.gpu(kv.rank),
kvstore=kv,
symbol=common.lenet(),
X=train,
num_epoch=10,
learning_rate=0.05,
momentum=0.9,
wd=0.00001)
common.accuracy(model, val)
#!/usr/bin/env python
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.mnist(num_parts = kv.num_workers,
part_index = kv.rank,
batch_size = 100,
input_shape = (1,28,28))
model = mx.model.FeedForward.create(
ctx = mx.gpu(kv.rank),
kvstore = kv,
symbol = common.lenet(),
X = train,
num_epoch = 10,
learning_rate = 0.05,
momentum = 0.9,
wd = 0.00001)
common.accuracy(model, val)
