提交 e1bfd85f 编写于 作者: T Tao Luo 提交者: GitHub

Merge pull request #1950 from Haichao-Zhang/beam_search_doc_error_fix

fixed error in beam_search example and documents
......@@ -1349,9 +1349,9 @@ def last_seq(input,
"""
Get Last Timestamp Activation of a sequence.
If stride > 0, this layer slides a window whose size is determined by stride,
and return the last value of the window as the output. Thus, a long sequence
will be shorten. Note that for sequence with sub-sequence, the default value
If stride > 0, this layer slides a window whose size is determined by stride,
and return the last value of the window as the output. Thus, a long sequence
will be shorten. Note that for sequence with sub-sequence, the default value
of stride is -1.
The simple usage is:
......@@ -1365,7 +1365,7 @@ def last_seq(input,
:type name: basestring
:param input: Input layer name.
:type input: LayerOutput
:param stride: window size.
:param stride: window size.
:type stride: Int
:param layer_attr: extra layer attributes.
:type layer_attr: ExtraLayerAttribute.
......@@ -1405,9 +1405,9 @@ def first_seq(input,
"""
Get First Timestamp Activation of a sequence.
If stride > 0, this layer slides a window whose size is determined by stride,
and return the first value of the window as the output. Thus, a long sequence
will be shorten. Note that for sequence with sub-sequence, the default value
If stride > 0, this layer slides a window whose size is determined by stride,
and return the first value of the window as the output. Thus, a long sequence
will be shorten. Note that for sequence with sub-sequence, the default value
of stride is -1.
The simple usage is:
......@@ -1421,7 +1421,7 @@ def first_seq(input,
:type name: basestring
:param input: Input layer name.
:type input: LayerOutput
:param stride: window size.
:param stride: window size.
:type stride: Int
:param layer_attr: extra layer attributes.
:type layer_attr: ExtraLayerAttribute.
......@@ -1561,7 +1561,7 @@ def seq_reshape_layer(input,
bias_attr=None):
"""
A layer for reshaping the sequence. Assume the input sequence has T instances,
the dimension of each instance is M, and the input reshape_size is N, then the
the dimension of each instance is M, and the input reshape_size is N, then the
output sequence has T*M/N instances, the dimension of each instance is N.
Note that T*M/N must be an integer.
......@@ -2118,8 +2118,8 @@ def img_conv_layer(input,
:param trans: true if it is a convTransLayer, false if it is a convLayer
:type trans: bool
:param layer_type: specify the layer_type, default is None. If trans=True,
layer_type has to be "exconvt" or "cudnn_convt",
otherwise layer_type has to be either "exconv" or
layer_type has to be "exconvt" or "cudnn_convt",
otherwise layer_type has to be either "exconv" or
"cudnn_conv"
:type layer_type: String
:return: LayerOutput object.
......@@ -2337,9 +2337,9 @@ def spp_layer(input,
.. code-block:: python
spp = spp_layer(input=data,
pyramid_height=2,
num_channels=16,
spp = spp_layer(input=data,
pyramid_height=2,
num_channels=16,
pool_type=MaxPooling())
:param name: layer name.
......@@ -2433,7 +2433,7 @@ def img_cmrnorm_layer(input,
The example usage is:
.. code-block:: python
norm = img_cmrnorm_layer(input=net, size=5)
:param name: layer name.
......@@ -2494,7 +2494,7 @@ def batch_norm_layer(input,
The example usage is:
.. code-block:: python
norm = batch_norm_layer(input=net, act=ReluActivation())
:param name: layer name.
......@@ -2795,11 +2795,11 @@ def seq_concat_layer(a, b, act=None, name=None, layer_attr=None,
"""
Concat sequence a with sequence b.
Inputs:
Inputs:
- a = [a1, a2, ..., an]
- b = [b1, b2, ..., bn]
- Note that the length of a and b should be the same.
Output: [a1, b1, a2, b2, ..., an, bn]
The example usage is:
......@@ -3563,9 +3563,15 @@ def beam_search(step,
simple_rnn += last_time_step_output
return simple_rnn
generated_word_embedding = GeneratedInput(
size=target_dictionary_dim,
embedding_name="target_language_embedding",
embedding_size=word_vector_dim)
beam_gen = beam_search(name="decoder",
step=rnn_step,
input=[StaticInput(encoder_last)],
input=[StaticInput(encoder_last),
generated_word_embedding],
bos_id=0,
eos_id=1,
beam_size=5)
......@@ -3584,7 +3590,8 @@ def beam_search(step,
You can refer to the first parameter of recurrent_group, or
demo/seqToseq/seqToseq_net.py for more details.
:type step: callable
:param input: Input data for the recurrent unit
:param input: Input data for the recurrent unit, which should include the
previously generated words as a GeneratedInput object.
:type input: list
:param bos_id: Index of the start symbol in the dictionary. The start symbol
is a special token for NLP task, which indicates the
......
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