如何使用Tensorboard在同一图上绘制不同的摘要指标？

Question

我希望能够针对同一批次中的验证集绘制每批培训损失和 平均验证损失。张量板。当我的验证集太大而无法放入内存时，我遇到了这个问题，因此需要进行批处理并使用tf.metrics更新操作。

该问题可能适用于您想在Tensorboard的同一张图中显示的任何Tensorflow指标。

我能够

• 分别绘制这两个图（请参见here）
• 在与 training-loss-per-training-batch 相同的图上绘制 validation-per-validation-batch （在验证时可以集可以是一个批次，我可以重复使用下面的训练摘要op train_summ）

在下面的示例代码中，我的问题源于以下事实：我的验证摘要tf.summary.scalar和name=loss被重命名为loss_1，因此被移动到Tensorboard中的单独图形。根据我的判断，Tensorboard接受“同名” 并将其绘制在同一张图上，而不管它们位于哪个文件夹中。这令人沮丧，因为train_summ（名称=损失）是仅写入train文件夹和valid_summ（name = loss）仅被写入valid文件夹-但仍重命名为loss_1。

示例代码：

# View graphs with (Linux): $ tensorboard --logdir=/tmp/my_tf_model

import tensorflow as tf
import numpy as np
import os
import tempfile

def train_data_gen():
    yield np.random.normal(size=[3]), np.array([0.5, 0.5, 0.5])

def valid_data_gen():
    yield np.random.normal(size=[3]), np.array([0.8, 0.8, 0.8])

batch_size = 25
n_training_batches = 4
n_valid_batches = 2
n_epochs = 5
summary_loc = os.path.join(tempfile.gettempdir(), 'my_tf_model')
print("Summaries written to" + summary_loc)

# Dummy data
train_data = tf.data.Dataset.from_generator(train_data_gen, (tf.float32, tf.float32)).repeat().batch(batch_size)
valid_data = tf.data.Dataset.from_generator(valid_data_gen, (tf.float32, tf.float32)).repeat().batch(batch_size)
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(handle, 
train_data.output_types, train_data.output_shapes)
batch_x, batch_y = iterator.get_next()
train_iter = train_data.make_initializable_iterator()
valid_iter = valid_data.make_initializable_iterator()

# Some ops on the data
loss = tf.losses.mean_squared_error(batch_x, batch_y)
valid_loss, valid_loss_update = tf.metrics.mean(loss)

# Write to summaries
train_summ = tf.summary.scalar('loss', loss)
valid_summ = tf.summary.scalar('loss', valid_loss)  # <- will be renamed to "loss_1"

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    train_handle, valid_handle = sess.run([train_iter.string_handle(), valid_iter.string_handle()])
    sess.run([train_iter.initializer, valid_iter.initializer])

    # Summary writers
    writer_train = tf.summary.FileWriter(os.path.join(summary_loc, 'train'), sess.graph)
    writer_valid = tf.summary.FileWriter(os.path.join(summary_loc, 'valid'), sess.graph)

    global_step = 0  # implicit as no actual training
    for i in range(n_epochs):
        # "Training"
        for j in range(n_training_batches):
            global_step += 1
            summ = sess.run(train_summ, feed_dict={handle: train_handle})
            writer_train.add_summary(summary=summ, global_step=global_step)
        # "Validation"
        sess.run(tf.local_variables_initializer())
        for j in range(n_valid_batches):
             _, batch_summ = sess.run([valid_loss_update, train_summ], feed_dict={handle: valid_handle})
            # The following will plot the batch loss for the validation set on the loss plot with the training data:
            # writer_valid.add_summary(summary=batch_summ, global_step=global_step + j + 1)
        summ = sess.run(valid_summ)
        writer_valid.add_summary(summary=summ, global_step=global_step)  # <- I want this on the training loss graph

我尝试过的

• 按照this issue和this question的建议，分离tf.summary.FileWriter个对象（一个用于训练，一个用于验证）（想想我在该问题的注释中提到了什么） ）
• 使用tf.summary.merge将我所有的训练和验证/测试指标合并到总体摘要操作中；做有用的簿记，但没有在同一张图上标出我想要的内容
• 使用tf.summary.scalar family属性（loss仍被重命名为loss_1）
• （完整的hack解决方案）在 training 数据上使用valid_loss, valid_loss_update = tf.metrics.mean(loss)，然后在每个培训批次中运行tf.local_variables_initializer()。这确实为您提供了相同的摘要操作，因此可以将它们放在同一张图上，但是肯定不是您 meant 那样做的吗？它也不会推广到其他指标。

上下文

• Tensorflow 1.9.0
• Tensorboard 1.9.0
• Python 3.5.2

Answer 1

Tensorboard custom_scalar plugin是解决此问题的方法。

这又是一个相同的示例，带有custom_scalar来在同一图上绘制两个损失（每个训练批次+所有验证批次的平均值）：

# View graphs with (Linux): $ tensorboard --logdir=/tmp/my_tf_model

import os
import tempfile
import tensorflow as tf
import numpy as np
from tensorboard import summary as summary_lib
from tensorboard.plugins.custom_scalar import layout_pb2

def train_data_gen():
    yield np.random.normal(size=[3]), np.array([0.5, 0.5, 0.5])

def valid_data_gen():
    yield np.random.normal(size=[3]), np.array([0.8, 0.8, 0.8])

batch_size = 25
n_training_batches = 4
n_valid_batches = 2
n_epochs = 5
summary_loc = os.path.join(tempfile.gettempdir(), 'my_tf_model')
print("Summaries written to " + summary_loc)

# Dummy data
train_data = tf.data.Dataset.from_generator(
    train_data_gen, (tf.float32, tf.float32)).repeat().batch(batch_size)
valid_data = tf.data.Dataset.from_generator(
    valid_data_gen, (tf.float32, tf.float32)).repeat().batch(batch_size)
handle = tf.placeholder(tf.string, shape=[])
iterator = tf.data.Iterator.from_string_handle(handle, train_data.output_types,
                                               train_data.output_shapes)
batch_x, batch_y = iterator.get_next()
train_iter = train_data.make_initializable_iterator()
valid_iter = valid_data.make_initializable_iterator()

# Some ops on the data
loss = tf.losses.mean_squared_error(batch_x, batch_y)
valid_loss, valid_loss_update = tf.metrics.mean(loss)

with tf.name_scope('loss'):
    train_summ = summary_lib.scalar('training', loss)
    valid_summ = summary_lib.scalar('valid', valid_loss)

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    train_handle, valid_handle = sess.run([train_iter.string_handle(), valid_iter.string_handle()])
    sess.run([train_iter.initializer, valid_iter.initializer])

    writer_train = tf.summary.FileWriter(os.path.join(summary_loc, 'train'), sess.graph)
    writer_valid = tf.summary.FileWriter(os.path.join(summary_loc, 'valid'), sess.graph)

    layout_summary = summary_lib.custom_scalar_pb(
        layout_pb2.Layout(category=[
            layout_pb2.Category(
                title='losses',
                chart=[
                    layout_pb2.Chart(
                        title='losses',
                        multiline=layout_pb2.MultilineChartContent(tag=[
                            'loss/training', 'loss/valid'
                        ]))
                ])
        ]))
    writer_train.add_summary(layout_summary)

    global_step = 0
    for i in range(n_epochs):
        for j in range(n_training_batches): # "Training"
            global_step += 1
            summ = sess.run(train_summ, feed_dict={handle: train_handle})
            writer_train.add_summary(summary=summ, global_step=global_step)

        sess.run(tf.local_variables_initializer())
        for j in range(n_valid_batches):  # "Validation"
            _, batch_summ = sess.run([valid_loss_update, train_summ], feed_dict={handle: valid_handle})
        summ = sess.run(valid_summ)
        writer_valid.add_summary(summary=summ, global_step=global_step)

Here's the resulting output在Tensorboard中。