模型可视化¶

add_scalar(tag, value, step, walltime=None)

from visualdl import LogWriter

if __name__ == '__main__':
    value = [i/1000.0 for i in range(1000)]
    # 初始化一个记录器
    with LogWriter(logdir="./log/scalar_test/train") as writer:
        for step in range(1000):
            # 向记录器添加一个tag为`acc`的数据
            writer.add_scalar(tag="acc", step=step, value=value[step])
            # 向记录器添加一个tag为`loss`的数据
            writer.add_scalar(tag="loss", step=step, value=1/(value[step] + 1))

from visualdl import LogWriter

if __name__ == '__main__':
    value = [i/1000.0 for i in range(1000)]
    # 步骤一：创建父文件夹：log与子文件夹：scalar_test
    with LogWriter(logdir="./log/scalar_test") as writer:
        for step in range(1000):
            # 步骤二：向记录器添加一个tag为`train/acc`的数据
            writer.add_scalar(tag="train/acc", step=step, value=value[step])
            # 步骤二：向记录器添加一个tag为`train/loss`的数据
            writer.add_scalar(tag="train/loss", step=step, value=1/(value[step] + 1))
    # 步骤一：创建第二个子文件夹scalar_test2  
    value = [i/500.0 for i in range(1000)]
    with LogWriter(logdir="./log/scalar_test2") as writer:
        for step in range(1000):
            # 步骤二：在同样名为`train/acc`下添加scalar_test2的accuracy的数据
            writer.add_scalar(tag="train/acc", step=step, value=value[step])
            # 步骤二：在同样名为`train/loss`下添加scalar_test2的loss的数据
            writer.add_scalar(tag="train/loss", step=step, value=1/(value[step] + 1))

add_image(tag, img, step, walltime=None)

import numpy as np
from PIL import Image
from visualdl import LogWriter


def random_crop(img):
    """获取图片的随机 100x100 分片
    """
    img = Image.open(img)
    w, h = img.size
    random_w = np.random.randint(0, w - 100)
    random_h = np.random.randint(0, h - 100)
    r = img.crop((random_w, random_h, random_w + 100, random_h + 100))
    return np.asarray(r)


if __name__ == '__main__':
    # 初始化一个记录器
    with LogWriter(logdir="./log/image_test/train") as writer:
        for step in range(6):
            # 添加一个图片数据
            writer.add_image(tag="eye",
                             img=random_crop("../../docs/images/eye.jpg"),
                             step=step)

add_audio(tag, audio_array, step, sample_rate)

from visualdl import LogWriter
import numpy as np
import wave


def read_audio_data(audio_path):
    """
    Get audio data.
    """
    CHUNK = 4096
    f = wave.open(audio_path, "rb")
    wavdata = []
    chunk = f.readframes(CHUNK)
    while chunk:
        data = np.frombuffer(chunk, dtype='uint8')
        wavdata.extend(data)
        chunk = f.readframes(CHUNK)
    # 8k sample rate, 16bit frame, 1 channel
    shape = [8000, 2, 1]
    return shape, wavdata


if __name__ == '__main__':
    with LogWriter(logdir="./log") as writer:
        audio_shape, audio_data = read_audio_data("./testing.wav")
        audio_data = np.array(audio_data)
        writer.add_audio(tag="audio_tag",
                         audio_array=audio_data,
                         step=0,
                         sample_rate=8000)

add_histogram(tag, values, step, walltime=None, buckets=10)

from visualdl import LogWriter
import numpy as np


if __name__ == '__main__':
    values = np.arange(0, 1000)
    with LogWriter(logdir="./log/histogram_test/train") as writer:
        for index in range(1, 101):
            interval_start = 1 + 2 * index / 100.0
            interval_end = 6 - 2 * index / 100.0
            data = np.random.uniform(interval_start, interval_end, size=(10000))
            writer.add_histogram(tag='default tag',
                                 values=data,
                                 step=index,
                                 buckets=10)

add_pr_curve(tag, labels, predictions, step=None, num_thresholds=10)

from visualdl import LogWriter
import numpy as np

with LogWriter("./log/pr_curve_test/train") as writer:
    for step in range(3):
        labels = np.random.randint(2, size=100)
        predictions = np.random.rand(100)
        writer.add_pr_curve(tag='pr_curve',
                            labels=labels,
                            predictions=predictions,
                            step=step,
                            num_thresholds=5)

add_embeddings(tag, labels, hot_vectors, walltime=None)

from visualdl import LogWriter


if __name__ == '__main__':
    hot_vectors = [
        [1.3561076367500755, 1.3116267195134017, 1.6785401875616097],
        [1.1039614644440658, 1.8891609992484688, 1.32030488587171],
        [1.9924524852447711, 1.9358920727142739, 1.2124401279391606],
        [1.4129542689796446, 1.7372166387197474, 1.7317806077076527],
        [1.3913371800587777, 1.4684674577930312, 1.5214136352476377]]

    labels = ["label_1", "label_2", "label_3", "label_4", "label_5"]
    # 初始化一个记录器
    with LogWriter(logdir="./log/high_dimensional_test/train") as writer:
        # 将一组labels和对应的hot_vectors传入记录器进行记录
        writer.add_embeddings(tag='default',
                              labels=labels,
                              hot_vectors=hot_vectors)