add save cache model api in fleet& add slots shuffle in dataset module & add metric op to calculate ctr related metrics (#18871)
* add ctr related metric layer test=develop * add save cache and slots shuffle test=develop * add save cache and slots shuffle test=develop * fix error * fix error * fix style for ci * fix for comments * change SlotsShuffle input to std::strinf for generality * fix style * fix style * fix style * fix style * fix style * fix style * fix stylr * fix style * fix style * fix style * fix style * fix style * fix style * fix style * fix style * fix style * fix style * fix style * fix style * fix style * change non-const reference to pointer * fix style * fix style * fix style test=develop * fix style test=develop * add return ins num in ctr metric op * change dtype to float in metric_op.py * fix error test=develop * fix style test=develop * fix API spec * fix API spec * fix API spec test=develop * add UT test=developpadding_in_crf
yaoxuefeng
6 years ago
committed by
GitHub
parent
b7b584b020
commit
9150cf50fc
@ -0,0 +1,188 @@
|
||||
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Contrib layers just related to metric.
|
||||
"""
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import warnings
|
||||
from paddle.fluid.layer_helper import LayerHelper
|
||||
from paddle.fluid.initializer import Normal, Constant
|
||||
from paddle.fluid.framework import Variable
|
||||
from paddle.fluid.param_attr import ParamAttr
|
||||
from paddle.fluid.layers import nn
|
||||
|
||||
__all__ = ['ctr_metric_bundle']
|
||||
|
||||
|
||||
def ctr_metric_bundle(input, label):
|
||||
"""
|
||||
ctr related metric layer
|
||||
|
||||
This function help compute the ctr related metrics: RMSE, MAE, predicted_ctr, q_value.
|
||||
To compute the final values of these metrics, we should do following computations using
|
||||
total instance number:
|
||||
MAE = local_abserr / instance number
|
||||
RMSE = sqrt(local_sqrerr / instance number)
|
||||
predicted_ctr = local_prob / instance number
|
||||
q = local_q / instance number
|
||||
Note that if you are doing distribute job, you should all reduce these metrics and instance
|
||||
number first
|
||||
|
||||
Args:
|
||||
input(Variable): A floating-point 2D Variable, values are in the range
|
||||
[0, 1]. Each row is sorted in descending order. This
|
||||
input should be the output of topk. Typically, this
|
||||
Variable indicates the probability of each label.
|
||||
label(Variable): A 2D int Variable indicating the label of the training
|
||||
data. The height is batch size and width is always 1.
|
||||
|
||||
Returns:
|
||||
local_sqrerr(Variable): Local sum of squared error
|
||||
local_abserr(Variable): Local sum of abs error
|
||||
local_prob(Variable): Local sum of predicted ctr
|
||||
local_q(Variable): Local sum of q value
|
||||
|
||||
Examples:
|
||||
.. code-block:: python
|
||||
|
||||
import paddle.fluid as fluid
|
||||
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
|
||||
label = fluid.layers.data(name="label", shape=[1], dtype="int32")
|
||||
predict = fluid.layers.sigmoid(fluid.layers.fc(input=data, size=1))
|
||||
auc_out = fluid.contrib.layers.ctr_metric_bundle(input=predict, label=label)
|
||||
"""
|
||||
assert input.shape == label.shape
|
||||
helper = LayerHelper("ctr_metric_bundle", **locals())
|
||||
|
||||
local_abserr = helper.create_global_variable(
|
||||
persistable=True, dtype='float32', shape=[1])
|
||||
local_sqrerr = helper.create_global_variable(
|
||||
persistable=True, dtype='float32', shape=[1])
|
||||
local_prob = helper.create_global_variable(
|
||||
persistable=True, dtype='float32', shape=[1])
|
||||
local_q = helper.create_global_variable(
|
||||
persistable=True, dtype='float32', shape=[1])
|
||||
local_pos_num = helper.create_global_variable(
|
||||
persistable=True, dtype='float32', shape=[1])
|
||||
local_ins_num = helper.create_global_variable(
|
||||
persistable=True, dtype='float32', shape=[1])
|
||||
|
||||
tmp_res_elesub = helper.create_global_variable(
|
||||
persistable=False, dtype='float32', shape=[-1])
|
||||
tmp_res_sigmoid = helper.create_global_variable(
|
||||
persistable=False, dtype='float32', shape=[-1])
|
||||
tmp_ones = helper.create_global_variable(
|
||||
persistable=False, dtype='float32', shape=[-1])
|
||||
|
||||
batch_prob = helper.create_global_variable(
|
||||
persistable=False, dtype='float32', shape=[1])
|
||||
batch_abserr = helper.create_global_variable(
|
||||
persistable=False, dtype='float32', shape=[1])
|
||||
batch_sqrerr = helper.create_global_variable(
|
||||
persistable=False, dtype='float32', shape=[1])
|
||||
batch_q = helper.create_global_variable(
|
||||
persistable=False, dtype='float32', shape=[1])
|
||||
batch_pos_num = helper.create_global_variable(
|
||||
persistable=False, dtype='float32', shape=[1])
|
||||
batch_ins_num = helper.create_global_variable(
|
||||
persistable=False, dtype='float32', shape=[1])
|
||||
for var in [
|
||||
local_abserr, batch_abserr, local_sqrerr, batch_sqrerr, local_prob,
|
||||
batch_prob, local_q, batch_q, batch_pos_num, batch_ins_num,
|
||||
local_pos_num, local_ins_num
|
||||
]:
|
||||
helper.set_variable_initializer(
|
||||
var, Constant(
|
||||
value=0.0, force_cpu=True))
|
||||
|
||||
helper.append_op(
|
||||
type="elementwise_sub",
|
||||
inputs={"X": [input],
|
||||
"Y": [label]},
|
||||
outputs={"Out": [tmp_res_elesub]})
|
||||
|
||||
helper.append_op(
|
||||
type="squared_l2_norm",
|
||||
inputs={"X": [tmp_res_elesub]},
|
||||
outputs={"Out": [batch_sqrerr]})
|
||||
helper.append_op(
|
||||
type="elementwise_add",
|
||||
inputs={"X": [batch_sqrerr],
|
||||
"Y": [local_sqrerr]},
|
||||
outputs={"Out": [local_sqrerr]})
|
||||
|
||||
helper.append_op(
|
||||
type="l1_norm",
|
||||
inputs={"X": [tmp_res_elesub]},
|
||||
outputs={"Out": [batch_abserr]})
|
||||
helper.append_op(
|
||||
type="elementwise_add",
|
||||
inputs={"X": [batch_abserr],
|
||||
"Y": [local_abserr]},
|
||||
outputs={"Out": [local_abserr]})
|
||||
|
||||
helper.append_op(
|
||||
type="reduce_sum", inputs={"X": [input]},
|
||||
outputs={"Out": [batch_prob]})
|
||||
helper.append_op(
|
||||
type="elementwise_add",
|
||||
inputs={"X": [batch_prob],
|
||||
"Y": [local_prob]},
|
||||
outputs={"Out": [local_prob]})
|
||||
helper.append_op(
|
||||
type="sigmoid",
|
||||
inputs={"X": [input]},
|
||||
outputs={"Out": [tmp_res_sigmoid]})
|
||||
helper.append_op(
|
||||
type="reduce_sum",
|
||||
inputs={"X": [tmp_res_sigmoid]},
|
||||
outputs={"Out": [batch_q]})
|
||||
helper.append_op(
|
||||
type="elementwise_add",
|
||||
inputs={"X": [batch_q],
|
||||
"Y": [local_q]},
|
||||
outputs={"Out": [local_q]})
|
||||
|
||||
helper.append_op(
|
||||
type="reduce_sum",
|
||||
inputs={"X": [label]},
|
||||
outputs={"Out": [batch_pos_num]})
|
||||
helper.append_op(
|
||||
type="elementwise_add",
|
||||
inputs={"X": [batch_pos_num],
|
||||
"Y": [local_pos_num]},
|
||||
outputs={"Out": [local_pos_num]})
|
||||
|
||||
helper.append_op(
|
||||
type='fill_constant_batch_size_like',
|
||||
inputs={"Input": label},
|
||||
outputs={'Out': [tmp_ones]},
|
||||
attrs={
|
||||
'shape': [-1, 1],
|
||||
'dtype': tmp_ones.dtype,
|
||||
'value': float(1.0),
|
||||
})
|
||||
helper.append_op(
|
||||
type="reduce_sum",
|
||||
inputs={"X": [tmp_ones]},
|
||||
outputs={"Out": [batch_ins_num]})
|
||||
helper.append_op(
|
||||
type="elementwise_add",
|
||||
inputs={"X": [batch_ins_num],
|
||||
"Y": [local_ins_num]},
|
||||
outputs={"Out": [local_ins_num]})
|
||||
|
||||
return local_sqrerr, local_abserr, local_prob, local_q, local_pos_num, local_ins_num
|
||||
File diff suppressed because one or more lines are too long
Loading…
Reference in new issue