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.
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.
The correct rate.
- Return type
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)
auc(input, label, curve='ROC', num_thresholds=4095, 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.
There are two types of possible curves:
ROC: Receiver operating characteristic;
PR: Precision Recall
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.
A scalar representing the current AUC.
- Return type
import paddle.fluid as fluid data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32") label = fluid.layers.data(name="label", shape=, dtype="int32") predict = fluid.layers.fc(input=data, size=2) auc_out = fluid.layers.auc(input=predict, label=label)