使用 Tensorflow Dataset API 时如何使用设置提供标量

Question

我使用 TF 数据集 API 和文件名的占位符，我在初始化迭代器时提供文件名（不同的文件取决于它是训练集还是验证集）。我还想使用额外的占位符来指示我们是在训练还是验证（包括在 dropout 层中）。但是，我无法使用数据集初始值设定项将值提供给这个占位符（这是有道理的，因为这不是数据集的一部分）。那么如何在使用 Dataset API 时提供额外的变量呢？

关键代码片段：

filenames_placeholder = tf.placeholder(tf.string, shape = (None))

is_training = tf.placeholder(tf.bool, shape = ()) # Error: You must feed a value for placeholder tensor 'Placeholder_1' with dtype bool

dataset = tf.data.TFRecordDataset(filenames_placeholder)

# (...) Many other dataset operations

iterator = dataset.make_initializable_iterator()

next_element = iterator.get_next()

# Model code using "next_element"  as inputs including the dropout layer at some point 

# where I would like to let the model know if we're training or validating

tf.layers.dropout(x, training = is_training)

# Model execution

with tf.Session() as sess:

sess.run(tf.global_variables_initializer())

sess.run(iterator.initializer, feed_dict = {filenames_placeholder: training_files, is_training: True})

# (...) Performing training

sess.run(iterator.initializer, feed_dict = {filenames_placeholder: training_files, is_training: False})

# (...) Performing validadtion