The main motivation is that a bidirectional RNN, useful in DeepSpeech like speech models, learns representation for a sequence by performing a
forward and a backward pass through the entire sequence. However, unlike
unidirectional RNNs, bidirectional RNNs are challenging to deploy in an online
and low-latency setting. The lookahead convolution incorporates information
from future subsequences in a computationally efficient manner to improve
unidirectional recurrent neural networks. The row convolution operator is
different from the 1D sequence convolution, and is computed as follows:
Given an input sequence X of length t and input dimension D, and a filter (W) of size context * D.
More details about row_conv please refer to the design document https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645 .
Args:
name_scope (str): See base class.
future_context_size (int): Future context size. Please note, the shape
of convolution kernel is [future_context_size + 1, D].
param_attr (ParamAttr): Attributes of parameters, including
name, initializer etc.
act (str): Non-linear activation to be applied to output variable.
Returns:
the output(Out) is a LodTensor, which supports variable time-length input sequences. The underlying tensor in this LodTensor is a matrix with shape T x N, i.e., the same shape as X.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
with fluid.dygraph.guard():
x = numpy.random.random((16)).astype('float32')
rowConv = fluid.dygraph.nn.RowConv(
'RowConv', future_context_size=2)
ret = rowConv(fluid.dygraph.base.to_variable(x))
"""
def__init__(self,
def__init__(self,
name_scope,
name_scope,
future_context_size,
future_context_size,
...
@@ -2252,6 +2334,16 @@ class GroupNorm(layers.Layer):
...
@@ -2252,6 +2334,16 @@ class GroupNorm(layers.Layer):
Returns:
Returns:
Variable: A tensor variable which is the result after applying group normalization on the input.
Variable: A tensor variable which is the result after applying group normalization on the input.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
with fluid.dygraph.guard():
x = numpy.random.random((8, 32, 32)).astype('float32')
Refer to `Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .
Args:
name_scope (str): See base class.
dim(int): The index of dimension which should be permuted to the first before reshaping Input(Weight) to matrix, it should be set as 0 if Input(Weight) is the weight of fc layer, and should be set as 1 if Input(Weight) is the weight of conv layer, default 0
power_iters(int): number of power iterations to calculate spectral norm, default 1
eps(float): epsilon for numerical stability in calculating norms
name (str): The name of this layer. It is optional.
Returns:
Variable: A tensor variable of weight parameters after spectral normalization.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import numpy
with fluid.dygraph.guard():
x = numpy.random.random((2, 8, 32, 32)).astype('float32')
@@ -2362,6 +2511,44 @@ class SpectralNorm(layers.Layer):
...
@@ -2362,6 +2511,44 @@ class SpectralNorm(layers.Layer):
classTreeConv(layers.Layer):
classTreeConv(layers.Layer):
"""
***Tree-Based Convolution Operator***
Tree-Based Convolution is a kind of convolution based on tree structure.
Tree-Based Convolution is a part of Tree-Based Convolution Neural Network(TBCNN),
which is used to classify tree structures, such as Abstract Syntax Tree.
Tree-Based Convolution proposed a kind of data structure called continuous binary tree,
which regards multiway tree as binary tree.
The paper of Tree-Based Convolution Operator is here: https://arxiv.org/abs/1409.5718v1
Args:
name_scope (str): See base class.
output_size(int): output feature width
num_filters(int): number of filters, Default 1
max_depth(int): max depth of filters, Default 2
act(str): activation function, Default tanh
param_attr(ParamAttr): the parameter attribute for the filters, Default None
bias_attr(ParamAttr): the parameter attribute for the bias of this layer, Default None
name(str): a name of this layer(optional). If set None, the layer will be named automatically, Default None
Returns:
out(Variable): (Tensor) The feature vector of subtrees. The shape of the output tensor is [max_tree_node_size, output_size, num_filters]. The output tensor could be a new feature vector for next tree convolution layers
