提交 9cb833cb 编写于 作者: C caoying03

rename sequence into seq to keep a constant style.

上级 92c293ac
......@@ -137,7 +137,7 @@ __all__ = [
'clip_layer',
'slice_projection',
'seq_slice_layer',
'kmax_sequence_score_layer',
'kmax_seq_score_layer',
'img_pool3d_layer',
'scale_shift_layer',
'img_conv3d_layer',
......@@ -5994,7 +5994,7 @@ def cross_entropy_over_beam(input, name=None):
Note that, if gold falls off the beam at search step t, then the cost is
calculated over the beam at step t.
This cost layer always works together with kmax_sequence_score_layer,
This cost layer always works together with kmax_seq_score_layer,
sub_nested_seq_layer, and sequence_slice_layer to trim the input to form a
sub-search space.
......@@ -6597,14 +6597,14 @@ def seq_slice_layer(input, starts, ends, name=None):
@wrap_name_default()
@layer_support()
def kmax_sequence_score_layer(input, name=None, beam_size=1):
def kmax_seq_score_layer(input, name=None, beam_size=1):
"""
This layer accepts one input which are scores over a sequence or a nested
sequence, and returns indices of beam_size sequences with highest scores.
.. code-block:: python
kmax_indices = kmax_sequence_score_layer(input=input_layer, beam_size)
kmax_indices = kmax_seq_score_layer(input=input_layer, beam_size)
:param name: The Layer Name.
......@@ -6617,10 +6617,10 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1):
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput), ("kmax_sequence_score_layer "
assert isinstance(input, LayerOutput), ("kmax_seq_score_layer "
"accepts only one input.")
assert input.size == 1, (
"input of kmax_sequence_score_layer is a score"
"input of kmax_seq_score_layer is a score "
"over a sequence or a nested sequence, so its width must be 1.")
Layer(
......
......@@ -8,7 +8,7 @@ test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
test_kmax_seq_socre_layer test_seq_select_layers test_scale_shift_layer
test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer
test_seq_slice_layer test_cross_entropy_over_beam test_pooling3D_layer
test_conv3d_layer test_deconv3d_layer)
......
......@@ -12,7 +12,7 @@ layers {
active_type: ""
}
layers {
name: "__kmax_sequence_score_layer_0__"
name: "__kmax_seq_score_layer_0__"
type: "kmax_seq_score"
active_type: ""
inputs {
......@@ -29,7 +29,7 @@ layers {
input_layer_name: "sentence_states"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_0__"
input_layer_name: "__kmax_seq_score_layer_0__"
}
}
layers {
......@@ -44,7 +44,7 @@ layers {
bias_parameter_name: "___fc_layer_0__.wbias"
}
layers {
name: "__kmax_sequence_score_layer_1__"
name: "__kmax_seq_score_layer_1__"
type: "kmax_seq_score"
active_type: ""
inputs {
......@@ -61,7 +61,7 @@ layers {
input_layer_name: "__sub_nested_seq_layer_0__"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_1__"
input_layer_name: "__kmax_seq_score_layer_1__"
}
select_first: true
}
......@@ -77,7 +77,7 @@ layers {
bias_parameter_name: "___fc_layer_1__.wbias"
}
layers {
name: "__kmax_sequence_score_layer_2__"
name: "__kmax_seq_score_layer_2__"
type: "kmax_seq_score"
active_type: ""
inputs {
......@@ -111,7 +111,7 @@ layers {
input_layer_name: "sentence_scores"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_0__"
input_layer_name: "__kmax_seq_score_layer_0__"
}
inputs {
input_layer_name: "sentences_ids"
......@@ -120,7 +120,7 @@ layers {
input_layer_name: "__fc_layer_0__"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_1__"
input_layer_name: "__kmax_seq_score_layer_1__"
}
inputs {
input_layer_name: "start_ids"
......@@ -129,7 +129,7 @@ layers {
input_layer_name: "__fc_layer_1__"
}
inputs {
input_layer_name: "__kmax_sequence_score_layer_2__"
input_layer_name: "__kmax_seq_score_layer_2__"
}
inputs {
input_layer_name: "end_ids"
......@@ -185,13 +185,13 @@ sub_models {
name: "root"
layer_names: "sentence_states"
layer_names: "sentence_scores"
layer_names: "__kmax_sequence_score_layer_0__"
layer_names: "__kmax_seq_score_layer_0__"
layer_names: "__sub_nested_seq_layer_0__"
layer_names: "__fc_layer_0__"
layer_names: "__kmax_sequence_score_layer_1__"
layer_names: "__kmax_seq_score_layer_1__"
layer_names: "__seq_slice_layer_0__"
layer_names: "__fc_layer_1__"
layer_names: "__kmax_sequence_score_layer_2__"
layer_names: "__kmax_seq_score_layer_2__"
layer_names: "sentences_ids"
layer_names: "start_ids"
layer_names: "end_ids"
......
......@@ -17,7 +17,7 @@ layers {
bias_parameter_name: "___fc_layer_0__.wbias"
}
layers {
name: "__kmax_sequence_score_layer_0__"
name: "__kmax_seq_score_layer_0__"
type: "kmax_seq_score"
active_type: ""
inputs {
......@@ -46,14 +46,14 @@ parameters {
initial_smart: false
}
input_layer_names: "input_seq"
output_layer_names: "__kmax_sequence_score_layer_0__"
output_layer_names: "__kmax_seq_score_layer_0__"
sub_models {
name: "root"
layer_names: "input_seq"
layer_names: "__fc_layer_0__"
layer_names: "__kmax_sequence_score_layer_0__"
layer_names: "__kmax_seq_score_layer_0__"
input_layer_names: "input_seq"
output_layer_names: "__kmax_sequence_score_layer_0__"
output_layer_names: "__kmax_seq_score_layer_0__"
is_recurrent_layer_group: false
}
......@@ -7,14 +7,14 @@ beam_size = 5
# the first beam expansion.
sentence_states = data_layer(name="sentence_states", size=32)
sentence_scores = data_layer(name="sentence_scores", size=1)
topk_sentence_ids = kmax_sequence_score_layer(
topk_sentence_ids = kmax_seq_score_layer(
input=sentence_scores, beam_size=beam_size)
# the second beam expansion.
topk_sen = sub_nested_seq_layer(
input=sentence_states, selected_indices=topk_sentence_ids)
start_pos_scores = fc_layer(input=topk_sen, size=1, act=LinearActivation())
topk_start_pos_ids = kmax_sequence_score_layer(
topk_start_pos_ids = kmax_seq_score_layer(
input=sentence_scores, beam_size=beam_size)
# the final beam expansion.
......@@ -22,7 +22,7 @@ topk_start_spans = seq_slice_layer(
input=topk_sen, starts=topk_start_pos_ids, ends=None)
end_pos_scores = fc_layer(
input=topk_start_spans, size=1, act=LinearActivation())
topk_end_pos_ids = kmax_sequence_score_layer(
topk_end_pos_ids = kmax_seq_score_layer(
input=end_pos_scores, beam_size=beam_size)
# define the cost
......
......@@ -4,6 +4,6 @@ from paddle.trainer_config_helpers import *
data = data_layer(name="input_seq", size=128)
scores = fc_layer(input=data, size=1, act=ExpActivation())
kmax_seq_id = kmax_sequence_score_layer(input=scores, beam_size=5)
kmax_seq_id = kmax_seq_score_layer(input=scores, beam_size=5)
outputs(kmax_seq_id)
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