Accuracy¶

class paddle.metric. Accuracy ( topk=(1,), name=None, *args, **kwargs ) [source]

Encapsulates accuracy metric logic.

Parameters

• topk (list[int]|tuple[int]) – Number of top elements to look at for computing accuracy. Default is (1,).

• name (str, optional) – String name of the metric instance. Default is acc.

Example by standalone:

import numpy as np
import paddle

x = paddle.to_tensor(np.array([
    [0.1, 0.2, 0.3, 0.4],
    [0.1, 0.4, 0.3, 0.2],
    [0.1, 0.2, 0.4, 0.3],
    [0.1, 0.2, 0.3, 0.4]]))
y = paddle.to_tensor(np.array([[0], [1], [2], [3]]))

m = paddle.metric.Accuracy()
correct = m.compute(x, y)
m.update(correct)
res = m.accumulate()
print(res) # 0.75

Example with Model API:

import paddle
from paddle.static import InputSpec
import paddle.vision.transforms as T
from paddle.vision.datasets import MNIST

input = InputSpec([None, 1, 28, 28], 'float32', 'image')
label = InputSpec([None, 1], 'int64', 'label')
transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
train_dataset = MNIST(mode='train', transform=transform)

model = paddle.Model(paddle.vision.models.LeNet(), input, label)
optim = paddle.optimizer.Adam(
    learning_rate=0.001, parameters=model.parameters())
model.prepare(
    optim,
    loss=paddle.nn.CrossEntropyLoss(),
    metrics=paddle.metric.Accuracy())

model.fit(train_dataset, batch_size=64)

compute ( pred, label, *args )

compute¶

Compute the top-k (maximum value in topk) indices.

Parameters

• pred (Tensor) – The predicted value is a Tensor with dtype float32 or float64. Shape is [batch_size, d0, …, dN].

• label (Tensor) – The ground truth value is Tensor with dtype int64. Shape is [batch_size, d0, …, 1], or [batch_size, d0, …, num_classes] in one hot representation.

Returns

Correct mask, a tensor with shape [batch_size, d0, …, topk].

Return type

Tensor

update ( correct, *args )

update¶

Update the metrics states (correct count and total count), in order to calculate cumulative accuracy of all instances. This function also returns the accuracy of current step.

Parameters

correct – Correct mask, a tensor with shape [batch_size, d0, …, topk].

Returns

the accuracy of current step.

Return type

Tensor

reset ( )

reset¶

Resets all of the metric state.

accumulate ( )

accumulate¶

Computes and returns the accumulated metric.

name ( )

name¶

Return name of metric instance.