本文整理汇总了Python中tensorflow_fold.OneOf方法的典型用法代码示例。如果您正苦于以下问题：Python tensorflow_fold.OneOf方法的具体用法？Python tensorflow_fold.OneOf怎么用？Python tensorflow_fold.OneOf使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow_fold的用法示例。
在下文中一共展示了tensorflow_fold.OneOf方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: dynamic_pooling_blk
# 需要导入模块: import tensorflow_fold [as 别名]
# 或者: from tensorflow_fold import OneOf [as 别名]
def dynamic_pooling_blk():
"""Input: root node dic
Output: pooled, TensorType([hyper.conv_dim, ])
"""
leaf_case = feature_detector_blk()
pool_fwd = td.ForwardDeclaration(td.PyObjectType(), td.TensorType([hyper.conv_dim, ]))
pool = td.Composition()
with pool.scope():
cur_fea = feature_detector_blk().reads(pool.input)
children = td.GetItem('children').reads(pool.input)
mapped = td.Map(pool_fwd()).reads(children)
summed = td.Reduce(td.Function(tf.maximum)).reads(mapped)
summed = td.Function(tf.maximum).reads(summed, cur_fea)
pool.output.reads(summed)
pool = td.OneOf(lambda x: x['clen'] == 0,
{True: leaf_case, False: pool})
pool_fwd.resolve_to(pool)
return pool 示例2: l2loss_blk
# 需要导入模块: import tensorflow_fold [as 别名]
# 或者: from tensorflow_fold import OneOf [as 别名]
def l2loss_blk():
# rewrite using metric
leaf_case = td.Composition()
with leaf_case.scope():
leaf_case.output.reads(td.FromTensor(tf.constant(1.)))
nonleaf_case = td.Composition()
with nonleaf_case.scope():
direct = direct_embed_blk().reads(nonleaf_case.input)
com = composed_embed_blk().reads(nonleaf_case.input)
loss = td.Function(batch_nn_l2loss).reads(direct, com)
nonleaf_case.output.reads(loss)
return td.OneOf(lambda node: node['clen'] != 0,
{False: leaf_case, True: nonleaf_case})
# generalize to tree_reduce, accepts one block that takes two node, returns a value 示例3: composed_embed_blk
# 需要导入模块: import tensorflow_fold [as 别名]
# 或者: from tensorflow_fold import OneOf [as 别名]
def composed_embed_blk():
leaf_case = direct_embed_blk()
nonleaf_case = td.Composition(name='composed_embed_nonleaf')
with nonleaf_case.scope():
children = td.GetItem('children').reads(nonleaf_case.input)
clen = td.Scalar().reads(td.GetItem('clen').reads(nonleaf_case.input))
cclens = td.Map(td.GetItem('clen') >> td.Scalar()).reads(children)
fchildren = td.Map(direct_embed_blk()).reads(children)
initial_state = td.Composition()
with initial_state.scope():
initial_state.output.reads(
td.FromTensor(tf.zeros(hyper.word_dim)),
td.FromTensor(tf.zeros([])),
)
summed = td.Zip().reads(fchildren, cclens, td.Broadcast().reads(clen))
summed = td.Fold(continous_weighted_add_blk(), initial_state).reads(summed)[0]
added = td.Function(tf.add, name='add_bias').reads(summed, td.FromTensor(param.get('B')))
normed = clip_by_norm_blk().reads(added)
act_fn = tf.nn.relu if hyper.use_relu else tf.nn.tanh
relu = td.Function(act_fn).reads(normed)
nonleaf_case.output.reads(relu)
return td.OneOf(lambda node: node['clen'] == 0,
{True: leaf_case, False: nonleaf_case})