ProgBarLogger

class paddle.callbacks. ProgBarLogger ( log_freq=1, verbose=2 )

ProgBarLogger 是一个日志回调类。

参数:
  • log_freq (int,可选) - 损失值和指标打印的频率。默认值:1。

  • verbose (int,可选) - 打印信息的模式。设置为0时,不打印信息; 设置为1时,使用进度条的形式打印信息;设置为2时,使用行的形式打印信息。 设置为3时,会在2的基础上打印详细的计时信息,比如 average_reader_cost。 默认值:2。

代码示例

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

inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
labels = [InputSpec([None, 1], 'int64', 'label')]

transform = T.Compose([
    T.Transpose(),
    T.Normalize([127.5], [127.5])
])
train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)

lenet = paddle.vision.LeNet()
model = paddle.Model(lenet,
    inputs, labels)

optim = paddle.optimizer.Adam(0.001, parameters=lenet.parameters())
model.prepare(optimizer=optim,
            loss=paddle.nn.CrossEntropyLoss(),
            metrics=paddle.metric.Accuracy())

callback = paddle.callbacks.ProgBarLogger(log_freq=10)
model.fit(train_dataset, batch_size=64, callbacks=callback)


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

inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
labels = [InputSpec([None, 1], 'int64', 'label')]

transform = T.Compose([
    T.Transpose(),
    T.Normalize([127.5], [127.5])
])
train_dataset = MNIST(mode='train', transform=transform)

lenet = paddle.vision.LeNet()
model = paddle.Model(lenet,
    inputs, labels)

optim = paddle.optimizer.Adam(0.001, parameters=lenet.parameters())
model.prepare(optimizer=optim,
            loss=paddle.nn.CrossEntropyLoss(),
            metrics=paddle.metric.Accuracy())

callback = paddle.callbacks.ProgBarLogger(log_freq=10)
model.fit(train_dataset, batch_size=64, callbacks=callback)