def test_mlp(devs, kv_type):
# guarantee the same weight init for each run
mx.random.seed(0)
logging.basicConfig(level=logging.DEBUG)
(train, val) = common.mnist(batch_size=100, input_shape=(784,))
# train
model = mx.model.FeedForward.create(
symbol=common.mlp(), ctx=devs, X=train, num_epoch=4, learning_rate=0.1, wd=0.0004, momentum=0.9, kvstore=kv_type
)
return common.accuracy(model, val)
def test_mlp(devs, kv_type):
# guarantee the same weight init for each run
mx.random.seed(0)
logging.basicConfig(level=logging.DEBUG)
(train, val) = mnist(batch_size=102, input_shape=(784, ))
# train
model = mx.model.FeedForward.create(symbol=softmax,
ctx=devs,
X=train,
num_round=2,
learning_rate=0.1,
wd=0.0004,
momentum=0.9,
kvstore=kv_type)
return 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) = cifar10(batch_size = 128, input_shape=(3,28,28))
(train, val) = mnist(batch_size=100, input_shape=(1, 28, 28))
model = mx.model.FeedForward.create(ctx=devs,
kvstore=kv_type,
symbol=lenet,
X=train,
num_round=3,
learning_rate=0.1,
momentum=0.9,
wd=0.00001)
return 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 mxnet as mx
import logging
import common
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 = (784,))
# train
model = mx.model.FeedForward.create(
symbol = common.mlp(),
ctx = mx.cpu(),
X = train,
num_round = 4,
learning_rate = 0.05,
wd = 0.0004,
momentum = 0.9,
kvstore = kv)
common.accuracy(model, val)
#!/usr/bin/env python
import mxnet as mx
import logging
import common
mx.random.seed(0)
logging.basicConfig(level=logging.DEBUG)
kv = mx.kvstore.create('dist_sync')
# feed each machine the whole data
(train, val) = common.mnist(batch_size = 100,
input_shape = (784,))
# train
model = mx.model.FeedForward.create(
symbol = common.mlp(),
ctx = mx.cpu(),
X = train,
num_epoch = 4,
learning_rate = 0.1,
wd = 0.0004,
momentum = 0.9,
kvstore = kv)
common.accuracy(model, val)
data_iter.reset()
Y = np.concatenate([y[0].asnumpy() for _, y, _, _ in data_iter])
data_iter.reset()
X = np.concatenate([x[0].asnumpy() for x, _, _, _ in data_iter])
assert X.shape[0] == Y.shape[0]
return (X, Y)
def test_iter(data_iter):
X, Y = get_XY(data_iter)
print X.shape, Y.shape
for i in range(4):
A, B = get_XY(data_iter)
assert (A.shape == X.shape)
assert (B.shape == Y.shape)
assert (np.sum(A != X) == 0)
assert (np.sum(B != Y) == 0)
(train, val) = mnist(batch_size=100, input_shape=(784, ))
test_iter(train)
test_iter(val)
(train, val) = mnist(batch_size=100, input_shape=(1, 28, 28))
test_iter(train)
test_iter(val)
(train, val) = cifar10(batch_size=128, input_shape=(3, 28, 28))
test_iter(train)
test_iter(val)
#!/usr/bin/env python
import mxnet as mx
import logging
import common
mx.random.seed(0)
logging.basicConfig(level=logging.DEBUG)
kv = mx.kvstore.create('dist_sync')
# feed each machine the whole data
(train, val) = common.mnist(batch_size=100, input_shape=(1, 28, 28))
# train, worker i uses gpu i
model = mx.model.FeedForward.create(ctx=mx.gpu(kv.rank),
kvstore=kv,
symbol=common.lenet(),
X=train,
num_round=3,
learning_rate=0.1,
momentum=0.9,
wd=0.00001)
common.accuracy(model, val)
def get_XY(data_iter):
data_iter.reset()
Y = np.concatenate([y[0].asnumpy() for _, y, _, _ in data_iter])
data_iter.reset()
X = np.concatenate([x[0].asnumpy() for x, _, _, _ in data_iter])
assert X.shape[0] == Y.shape[0]
return (X,Y)
def test_iter(data_iter):
X, Y = get_XY(data_iter)
print X.shape, Y.shape
for i in range(4):
A, B = get_XY(data_iter)
assert(A.shape == X.shape)
assert(B.shape == Y.shape)
assert(np.sum(A != X) == 0)
assert(np.sum(B != Y) == 0)
(train, val) = mnist(batch_size = 100, input_shape = (784,))
test_iter(train)
test_iter(val)
(train, val) = mnist(batch_size = 100, input_shape=(1,28,28))
test_iter(train)
test_iter(val)
(train, val) = cifar10(batch_size = 128, input_shape=(3,28,28))
test_iter(train)
test_iter(val)
#!/usr/bin/env python
import mxnet as mx
import logging
import common
mx.random.seed(0)
logging.basicConfig(level=logging.DEBUG)
kv = mx.kvstore.create('dist_sync')
# feed each machine the whole data
(train, val) = common.mnist(batch_size=100, input_shape=(784, ))
# train
model = mx.model.FeedForward.create(symbol=common.mlp(),
ctx=mx.cpu(),
X=train,
num_epoch=4,
learning_rate=0.1,
wd=0.0004,
momentum=0.9,
kvstore=kv)
common.accuracy(model, val)
#!/usr/bin/env python
import mxnet as mx
import logging
import common
mx.random.seed(0)
logging.basicConfig(level=logging.DEBUG)
kv = mx.kvstore.create("dist_sync")
# feed each machine the whole data
(train, val) = common.mnist(batch_size=100, input_shape=(1, 28, 28))
# train, worker i uses gpu i
model = mx.model.FeedForward.create(
ctx=mx.gpu(kv.rank),
kvstore=kv,
symbol=common.lenet(),
X=train,
num_round=3,
learning_rate=0.1,
momentum=0.9,
wd=0.00001,
)
common.accuracy(model, val)
