def get_iterator(buckets):
data_shape = (784, )
impl = train_mnist.get_iterator(data_shape)
def get_iterator_impl(args, kv):
train, val = impl(args, kv)
return (BucketIter(train, buckets), BucketIter(val, buckets))
return get_iterator_impl
def get_iterator(buckets):
data_shape = (784, )
impl = train_mnist.get_iterator(data_shape)
def get_iterator_impl(args, kv):
train, val = impl(args, kv)
return (BucketIter(train, buckets), BucketIter(val, buckets))
return get_iterator_impl
import os, sys
curr_path = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(curr_path, "../../example/image-classification"))
sys.path.append(os.path.join(curr_path, "../../python"))
import mxnet as mx
import argparse
import train_mnist
import logging
if __name__ == '__main__':
args = train_mnist.parse_args()
args.batch_size = 100
data_shape = (1, 28, 28)
loader = train_mnist.get_iterator(data_shape)
kv = mx.kvstore.create(args.kv_store)
(train, val) = loader(args, kv)
net = train_mnist.get_lenet()
head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'
logging.basicConfig(level=logging.DEBUG, format=head)
mx.model.FeedForward.create(ctx=mx.gpu(kv.rank),
kvstore=kv,
symbol=net,
X=train,
eval_data=val,
num_epoch=args.num_epochs,
learning_rate=args.lr,
momentum=0.9,
wd=0.00001)
import os, sys
curr_path = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(curr_path, "../../example/image-classification"))
sys.path.append(os.path.join(curr_path, "../../python"))
import mxnet as mx
import argparse
import train_mnist
import logging
if __name__ == '__main__':
args = train_mnist.parse_args()
args.batch_size = 100
data_shape = (1, 28, 28)
loader = train_mnist.get_iterator(data_shape)
kv = mx.kvstore.create(args.kv_store)
(train, val) = loader(args, kv)
net = train_mnist.get_lenet()
head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'
logging.basicConfig(level=logging.DEBUG, format=head)
mx.model.FeedForward.create(
ctx = mx.gpu(kv.rank),
kvstore = kv,
symbol = net,
X = train,
eval_data = val,
num_epoch = args.num_epochs,
learning_rate = args.lr,
momentum = 0.9,
wd = 0.00001)
