Source code for paddle.fluid.layers.metric_op

#   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']


[docs]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 import paddle.fluid as fluid data = fluid.layers.data(name="data", shape=[-1, 32, 32], dtype="float32") label = fluid.layers.data(name="label", shape=[-1,1], dtype="int32") predict = fluid.layers.fc(input=data, size=10) accuracy_out = 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
[docs]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 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.fc(input=data, size=2) auc_out = fluid.layers.auc(input=predict, 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 ]