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如何使用Tensorboard在同一图表上绘制不同的汇总指标？

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我希望能够在Tensorboard的同一个绘图上绘制 training loss per batch 和 average validation loss 作为验证集 . 当我的验证集太大而无法放入内存时，我遇到了这个问题，因此需要批处理并使用 tf.metrics update ops .

此问题可能适用于您希望在Tensorboard中显示在同一图表上的任何Tensorflow指标 .

我能够

  • 分别绘制这两个图表（见here）

  • 在每个培训批次的培训损失相同的图表上绘制验证 - 丢失 - 验证 - 批次（当验证集可以是单个批次时，这是可以的，我可以重用下面的培训摘要op train_summ ）

在下面的示例代码中，我的问题源于这样的事实：我的验证摘要 tf.summary.scalar 与 name=loss 被重命名为 loss_1 ，因此被移动到Tensorboard中的单独图形 . 从我可以解决的问题来看，Tensorboard需要"same name"并将它们绘制在同一个图表上，无论它们位于什么文件夹中 . 这是令人沮丧的，因为 train_summ （名称=丢失）只写入 train 文件夹和 valid_summ （名称=丢失）只会写入 valid 文件夹 - 但仍会重命名为 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的建议分开 tf.summary.FileWriter 个对象（一个用于培训，一个用于验证）（想想我所追求的是在该问题的评论中提到的）

  • 使用 tf.summary.merge 将我的所有培训和验证/测试指标合并到总体摘要操作中;有用的簿记，但没有在同一个图表上绘制我想要的东西

  • 使用 tf.summary.scalar family 属性（ loss 仍然被重命名为 loss_1 ）

  • (Complete hack solution) 在训练数据上使用 valid_loss, valid_loss_update = tf.metrics.mean(loss) ，然后在每个训练批次中运行 tf.local_variables_initializer() . 这确实给你相同的摘要操作，因此把事情放在同一个图表上，但肯定不是你打算如何做到这一点？它也没有推广到其他指标 .

上下文

  • Tensorflow 1.9.0

  • Tensorboard 1.9.0

  • Python 3.5.2

python tensorflow tensorboard

1 回答

  • 1

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

    以下是同样的示例，使用 custom_scalar 绘制同一图表中的两个损失（每个训练批次在所有验证批次上的平均值）：

    # 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 .

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