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)