ProgBarLogger¶

class paddle.callbacks. ProgBarLogger ( log_freq=1, verbose=2 ) [源代码] ¶: ProgBarLogger 是一个日志回调类，用来打印损失函数和评估指标。支持静默模式、进度条模式、每次打印一行三种模式，详细的参考下面参数注释。

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)

        