# Copyright (c) 2018 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.
"""
All layers just related to metric.
"""
from __future__ import print_function
import warnings
from ..layer_helper import LayerHelper
from ..initializer import Normal, Constant
from ..framework import Variable
from ..param_attr import ParamAttr
from . import nn
__all__ = ['accuracy', 'auc']
def accuracy(input, label, k=1, correct=None, total=None):
"""
accuracy layer.
Refer to the https://en.wikipedia.org/wiki/Precision_and_recall
This function computes the accuracy using the input and label.
If the correct label occurs in top k predictions, then correct will increment by one.
Note: the dtype of accuracy is determined by input. the input and label dtype can be different.
Args:
input(Variable): The input of accuracy layer, which is the predictions of network.
Carry LoD information is supported.
label(Variable): The label of dataset.
k(int): The top k predictions for each class will be checked.
correct(Variable): The correct predictions count.
total(Variable): The total entries count.
Returns:
Variable: The correct rate.
Examples:
.. code-block:: python
data = fluid.layers.data(name="data", shape=[-1, 32, 32], dtype="float32")
label = fluid.layers.data(name="data", shape=[-1,1], dtype="int32")
predict = fluid.layers.fc(input=data, size=10)
acc = fluid.layers.accuracy(input=predict, label=label, k=5)
"""
helper = LayerHelper("accuracy", **locals())
topk_out, topk_indices = nn.topk(input, k=k)
acc_out = helper.create_variable_for_type_inference(dtype="float32")
if correct is None:
correct = helper.create_variable_for_type_inference(dtype="int64")
if total is None:
total = helper.create_variable_for_type_inference(dtype="int64")
helper.append_op(
type="accuracy",
inputs={
"Out": [topk_out],
"Indices": [topk_indices],
"Label": [label]
},
outputs={
"Accuracy": [acc_out],
"Correct": [correct],
"Total": [total],
})
return acc_out
def auc(input,
label,
curve='ROC',
num_thresholds=2**12 - 1,
topk=1,
slide_steps=1):
"""
**Area Under the Curve (AUC) Layer**
This implementation computes the AUC according to forward output and label.
It is used very widely in binary classification evaluation.
Note: If input label contains values other than 0 and 1, it will be cast
to `bool`. Find the relevant definitions `here <https://en.wikipedia.org\
/wiki/Receiver_operating_characteristic#Area_under_the_curve>`_.
There are two types of possible curves:
1. ROC: Receiver operating characteristic;
2. PR: Precision Recall
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.
curve(str): Curve type, can be 'ROC' or 'PR'. Default 'ROC'.
num_thresholds(int): The number of thresholds to use when discretizing
the roc curve. Default 200.
topk(int): only topk number of prediction output will be used for auc.
slide_steps: when calc batch auc, we can not only use step currently but the previous steps can be used. slide_steps=1 means use the current step, slide_steps=3 means use current step and the previous second steps, slide_steps=0 use all of the steps.
Returns:
Variable: A scalar representing the current AUC.
Examples:
.. code-block:: python
# network is a binary classification model and label the ground truth
prediction = network(image, is_infer=True)
auc_out=fluid.layers.auc(input=prediction, label=label)
"""
helper = LayerHelper("auc", **locals())
auc_out = helper.create_variable_for_type_inference(dtype="float64")
batch_auc_out = helper.create_variable_for_type_inference(dtype="float64")
# make tp, tn, fp, fn persistable, so that can accumulate all batches.
# for batch auc
batch_stat_pos = helper.create_global_variable(
persistable=True,
dtype='int64',
shape=[slide_steps, num_thresholds + 1])
batch_stat_neg = helper.create_global_variable(
persistable=True,
dtype='int64',
shape=[slide_steps, num_thresholds + 1])
# for global auc
stat_pos = helper.create_global_variable(
persistable=True, dtype='int64', shape=[1, num_thresholds + 1])
stat_neg = helper.create_global_variable(
persistable=True, dtype='int64', shape=[1, num_thresholds + 1])
for var in [batch_stat_pos, batch_stat_neg, stat_pos, stat_neg]:
helper.set_variable_initializer(
var, Constant(
value=0.0, force_cpu=True))
# Batch AUC
helper.append_op(
type="auc",
inputs={
"Predict": [input],
"Label": [label],
"StatPos": [batch_stat_pos],
"StatNeg": [batch_stat_neg]
},
attrs={
"curve": curve,
"num_thresholds": num_thresholds,
"slide_steps": slide_steps
},
outputs={
"AUC": [batch_auc_out],
"StatPosOut": [batch_stat_pos],
"StatNegOut": [batch_stat_neg]
})
# Global AUC
helper.append_op(
type="auc",
inputs={
"Predict": [input],
"Label": [label],
"StatPos": [stat_pos],
"StatNeg": [stat_neg]
},
attrs={
"curve": curve,
"num_thresholds": num_thresholds,
"slide_steps": 0
},
outputs={
"AUC": [auc_out],
"StatPosOut": [stat_pos],
"StatNegOut": [stat_neg]
})
return auc_out, batch_auc_out, [
batch_stat_pos, batch_stat_neg, stat_pos, stat_neg
]