在计算函数时为什么要添加num_epochs

Question

我是tensorflow的新手，它的第一个教程是下面的代码。我的问题是，为什么使用num_epochs属性创建train_input_fn和eval_input_fn变量。它们只用于评估，所以我认为没有必要运行同一个测试10000次。

谢谢。

import tensorflow as tf
# NumPy is often used to load, manipulate and preprocess data.
import numpy as np

import logging
logging.getLogger("tensorflow").setLevel(logging.WARNING)


# Declare list of features. We only have one numeric feature. There are many
# other types of columns that are more complicated and useful.
feature_columns = [tf.feature_column.numeric_column("x", shape=[1])]

# An estimator is the front end to invoke training (fitting) and evaluation
# (inference). There are many predefined types like linear regression,
# linear classification, and many neural network classifiers and regressors.
# The following code provides an estimator that does linear regression.
estimator = tf.estimator.LinearRegressor(feature_columns=feature_columns)

# TensorFlow provides many helper methods to read and set up data sets.
# Here we use two data sets: one for training and one for evaluation
# We have to tell the function how many batches
# of data (num_epochs) we want and how big each batch should be.
x_train = np.array([1., 2., 3., 4.])
y_train = np.array([0., -1., -2., -3.])
x_eval = np.array([2., 5., 8., 1.])b
y_eval = np.array([-1.01, -4.1, -7, 0.])
input_fn = tf.estimator.inputs.numpy_input_fn(
    {"x": x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)
# WHY THE EPOCH?
train_input_fn = tf.estimator.inputs.numpy_input_fn(
    {"x": x_train}, y_train, batch_size=4, num_epochs=10000, shuffle=False)
# WHY THE EPOCH?
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
    {"x": x_eval}, y_eval, batch_size=4, num_epochs=10000, shuffle=False)

# We can invoke 1000 training steps by invoking the  method and passing the
# training data set.
estimator.train(input_fn=input_fn, steps=1000)

# Here we evaluate how well our model did.
train_metrics = estimator.evaluate(input_fn=train_input_fn, steps=1)
eval_metrics = estimator.evaluate(input_fn=eval_input_fn, steps=1)
print('train eval time', t2 - t1)
print('test eval time', t3 - t2)
print("train metrics: %r"% train_metrics)
print("eval metrics: %r"% eval_metrics)

Answer 1

num_epoch使用最大容量来迭代您的数据。

在您的示例中，训练数据中只有4个示例，批处理大小为4，因此在每个时期只能执行一个步骤。在一个步骤之后，它将在num_epoch = 1的情况下引发tf.errors.OutOfRangeError，因为您的数据只能循环一次。您在代码中执行了1000个步骤，因为您设置了num_epoch=None，这意味着它允许无限迭代。