Tensorflow:如何在Tensorboard中显示自定义图像(例如Matplotlib图) [英] Tensorflow: How to Display Custom Images in Tensorboard (e.g. Matplotlib Plots)

问题描述

图像仪表板部分Tensorboard自述文件的作者说:

The Image Dashboard section of the Tensorboard ReadMe says:

由于图像仪表板支持任意png，因此您可以使用它将自定义可视化效果(例如matplotlib散点图)嵌入到TensorBoard中.

Since the image dashboard supports arbitrary pngs, you can use this to embed custom visualizations (e.g. matplotlib scatterplots) into TensorBoard.

我看到如何将pyplot图像写入文件，作为张量读回，然后与tf.image_summary()一起使用将其写入TensorBoard，但是自述文件中的此语句表明有一种更直接的方法.在那儿?如果是这样，是否还有其他文档和/或示例来说明如何有效地做到这一点?

I see how a pyplot image could be written to file, read back in as a tensor, and then used with tf.image_summary() to write it to TensorBoard, but this statement from the readme suggests there is a more direct way. Is there? If so, is there any further documentation and/or examples of how to do this efficiently?

推荐答案

如果图像在内存缓冲区中，则很容易做到.下面，我显示一个示例，其中将pyplot保存到缓冲区，然后转换为TF图像表示形式，然后将其发送到图像摘要.

It is quite easy to do if you have the image in a memory buffer. Below, I show an example, where a pyplot is saved to a buffer and then converted to a TF image representation which is then sent to an image summary.

import io
import matplotlib.pyplot as plt
import tensorflow as tf


def gen_plot():
    """Create a pyplot plot and save to buffer."""
    plt.figure()
    plt.plot([1, 2])
    plt.title("test")
    buf = io.BytesIO()
    plt.savefig(buf, format='png')
    buf.seek(0)
    return buf


# Prepare the plot
plot_buf = gen_plot()

# Convert PNG buffer to TF image
image = tf.image.decode_png(plot_buf.getvalue(), channels=4)

# Add the batch dimension
image = tf.expand_dims(image, 0)

# Add image summary
summary_op = tf.summary.image("plot", image)

# Session
with tf.Session() as sess:
    # Run
    summary = sess.run(summary_op)
    # Write summary
    writer = tf.train.SummaryWriter('./logs')
    writer.add_summary(summary)
    writer.close()

这提供了以下TensorBoard可视化效果:

This gives the following TensorBoard visualization:

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