Tensorflow Estimator API:总结 [英] Tensorflow Estimator API: Summaries

问题描述

我无法使用 Tensorflow 的 Estimator API 进行摘要.

I can't achieve to make summaries work with the Estimator API of Tensorflow.

Estimator 类非常有用，原因有很多:我已经实现了自己的类，它们非常相似，但我正在尝试切换到这个类.

The Estimator class is very useful for many reasons: I have already implemented my own classes which are really similar but I am trying to switch to this one.

这是代码示例:

import tensorflow as tf
import tensorflow.contrib.layers as layers
import tensorflow.contrib.learn as learn
import numpy as np

 # To reproduce the error: docker run --rm -w /algo -v $(pwd):/algo tensorflow/tensorflow bash -c "python sample.py"

def model_fn(x, y, mode):
    logits = layers.fully_connected(x, 12, scope="dense-1")
    logits = layers.fully_connected(logits, 56, scope="dense-2")
    logits = layers.fully_connected(logits, 4, scope="dense-3")

    loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y), name="xentropy")

    return {"predictions":logits}, loss, tf.train.AdamOptimizer(0.001).minimize(loss)


def input_fun():
    """ To be completed for a 4 classes classification problem """

    feature = tf.constant(np.random.rand(100,10))
    labels = tf.constant(np.random.random_integers(0,3, size=(100,)))

    return feature, labels

estimator = learn.Estimator(model_fn=model_fn, )

trainingConfig = tf.contrib.learn.RunConfig(save_checkpoints_secs=60)

estimator = learn.Estimator(model_fn=model_fn, model_dir="./tmp", config=trainingConfig)

# Works
estimator.fit(input_fn=input_fun, steps=2)

# The following code does not work

# Can't initialize saver

# saver = tf.train.Saver(max_to_keep=10) # Error: No variables to save

# The following fails because I am missing a saver... :(

hooks=[
        tf.train.LoggingTensorHook(["xentropy"], every_n_iter=100),
        tf.train.CheckpointSaverHook("./tmp", save_steps=1000, checkpoint_basename='model.ckpt'),
        tf.train.StepCounterHook(every_n_steps=100, output_dir="./tmp"),
        tf.train.SummarySaverHook(save_steps=100, output_dir="./tmp"),
]

estimator.fit(input_fn=input_fun, steps=2, monitors=hooks)

如您所见，我可以创建一个 Estimator 并使用它，但我可以实现在拟合过程中添加钩子.

As you can see, I can create an Estimator and use it but I can achieve to add hooks to the fitting process.

日志挂钩工作正常，但其他挂钩需要张量和我无法提供的保护程序.

The logging hooks works just fine but the others require both tensors and a saver which I can't provide.

张量在模型函数中定义，因此我无法将它们传递给 SummaryHook 并且 Saver 无法初始化，因为没有张量保存...

The tensors are defined in the model function, thus I can't pass them to the SummaryHook and the Saver can't be initialized because there is no tensor to save...

我的问题有解决方案吗?(我猜是的，但 tensorflow 文档中缺少这部分的文档)

Is there a solution to my problem? (I am guessing yes but there is a lack of documentation of this part in the tensorflow documentation)

• 如何初始化我的保护程序?还是应该使用其他对象，例如 Scaffold?
• 如何将 summaries 传递给 SummaryHook，因为它们是在我的模型函数中定义的?

• How can I initialized my saver? Or should I use other objects such as Scaffold?
• How can I pass summaries to the SummaryHook since they are defined in my model function?

提前致谢.

PS:我已经看过 DNNClassifier API，但我想将 estimator API 用于卷积网络和其他网络.我需要为任何估算器创建摘要.

推荐答案

预期用例是让 Estimator 为您保存摘要.RunConfig 中有选项 用于配置摘要写作.RunConfigs 在 构建时通过估算器.

The intended use case is that you let the Estimator save summaries for you. There are options in RunConfig for configuring summary writing. RunConfigs get passed when constructing the Estimator.

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