本文整理匯總了Python中mxnet.nd.arange方法的典型用法代碼示例。如果您正苦於以下問題：Python nd.arange方法的具體用法？Python nd.arange怎麽用？Python nd.arange使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類mxnet.nd的用法示例。
在下文中一共展示了nd.arange方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: extract_pairwise_multi_position_embedding_nd
# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import arange [as 別名]
def extract_pairwise_multi_position_embedding_nd(position_mat, feat_dim, wave_length=1000):
""" Extract multi-class position embedding
Args:
position_mat: [num_fg_classes, num_rois, num_rois, 4]
feat_dim: dimension of embedding feature
wave_length:
Returns:
embedding: [num_fg_classes, num_rois, num_rois, feat_dim]
"""
feat_range = nd.arange(0, feat_dim / 8)
dim_mat = nd.broadcast_power(lhs=nd.full((1,), wave_length),
rhs=(8. / feat_dim) * feat_range)
dim_mat = nd.Reshape(dim_mat, shape=(1, 1, 1, 1, -1))
position_mat = nd.expand_dims(100.0 * position_mat, axis=4)
div_mat = nd.broadcast_div(lhs=position_mat, rhs=dim_mat)
sin_mat = nd.sin(data=div_mat)
cos_mat = nd.cos(data=div_mat)
# embedding, [num_fg_classes, num_rois, num_rois, 4, feat_dim/4]
embedding = nd.concat(sin_mat, cos_mat, dim=4)
embedding = nd.Reshape(embedding, shape=(0, 0, 0, feat_dim))
return embedding 示例2: extract_rank_embedding_nd
# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import arange [as 別名]
def extract_rank_embedding_nd(rank_dim, feat_dim, wave_length=1000):
rank_range = nd.arange(0, rank_dim)
feat_range = nd.arange(0, feat_dim / 2)
dim_mat = nd.broadcast_power(lhs=nd.full((1,), wave_length),
rhs=(2. / feat_dim) * feat_range)
dim_mat = nd.Reshape(dim_mat, shape=(1, -1))
rank_mat = nd.expand_dims(rank_range, axis=1)
div_mat = nd.broadcast_div(lhs=rank_mat, rhs=dim_mat)
sin_mat = nd.sin(data=div_mat)
cos_mat = nd.cos(data=div_mat)
embedding = nd.concat(sin_mat, cos_mat, dim=1)
return embedding 示例3: label_transform
# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import arange [as 別名]
def label_transform(label, classes):
ind = label.astype('int')
res = nd.zeros((ind.shape[0], classes), ctx=label.context)
res[nd.arange(ind.shape[0], ctx=label.context), ind] = 1
return res 示例4: label_transform
# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import arange [as 別名]
def label_transform(label, classes):
ind = label.astype('int')
res = nd.zeros((ind.shape[0], classes), ctx = label.context)
res[nd.arange(ind.shape[0], ctx = label.context), ind] = 1
return res 示例5: test_hierarchical_cnn_encoders
# 需要導入模塊: from mxnet import nd [as 別名]
# 或者: from mxnet.nd import arange [as 別名]
def test_hierarchical_cnn_encoders(use_residual, hybridize) -> None:
num_ts = 2
ts_len = 10
num_static_feat = 2
num_dynamic_feat = 5
test_data = nd.arange(num_ts * ts_len).reshape(shape=(num_ts, ts_len, 1))
test_static_feat = nd.random.randn(num_ts, num_static_feat)
test_dynamic_feat = nd.random.randn(num_ts, ts_len, num_dynamic_feat)
chl_dim = [30, 30, 30]
ks_seq = [3] * len(chl_dim)
dial_seq = [1, 3, 9]
cnn = HierarchicalCausalConv1DEncoder(
dial_seq,
ks_seq,
chl_dim,
use_residual,
use_dynamic_feat=True,
use_static_feat=True,
)
cnn.collect_params().initialize()
if hybridize:
cnn.hybridize()
true_shape = (num_ts, ts_len, 31) if use_residual else (num_ts, ts_len, 30)
assert (
cnn(test_data, test_static_feat, test_dynamic_feat)[1].shape
== true_shape
)