说，我们输入了x和标签y:
Say, we have input x and label y:
iterator = tf.data.Iterator.from_structure((x_type, y_type), (x_shape, y_shape))
tf_x, tf_y = iterator.get_next()
现在我使用 generate 函数来创建数据集:
Now I use generate function to create dataset:
def gen():
for ....: yield (x, y)
ds = tf.data.Dataset.from_generator(gen, (x_type, y_type), (x_shape, y_shape))
在我的图表中，我使用 tf_x 和 tf_y 进行训练，这很好.但现在我想做参考，我没有标签 y.我提出的一种解决方法是伪造一个 y(如 tf.zeros(y_shape))，然后使用占位符来初始化迭代器.
In my graph, I use tf_x and tf_y to do training, that is fine. But now I want to do referring, where I don't have label y. One workaround I made is to fake a y (like tf.zeros(y_shape)), then I use a placeholder to init the iterator.
x_placeholder = tf.placeholder(...)
y_placeholder = tf.placeholder(...)
ds = tf.data.Dataset.from_tensors((x_placeholder, y_placeholder))
ds_init_op = iterator.make_initializer(ds)
sess.run(ds_init_op, feed_dict={x_placeholder=x, y_placeholder=fake(y))})
我的问题是，有没有更清洁的方法来做到这一点?在推断期间没有伪造 y?
My question is, is there a cleaner way to do that? without fake a y during inferring time?
更新:
我实验了一下，貌似少了一个数据集操作unzip:
I experiment a little bit, looks like there is one dataset operation unzip missing:
import numpy as np
import tensorflow as tf
x_type = tf.float32
y_type = tf.float32
x_shape = tf.TensorShape([None, 128])
y_shape = tf.TensorShape([None, 10])
x_shape_nobatch = tf.TensorShape([128])
y_shape_nobatch = tf.TensorShape([10])
iterator_x = tf.data.Iterator.from_structure((x_type,), (x_shape,))
iterator_y = tf.data.Iterator.from_structure((y_type,), (y_shape,))
def gen1():
for i in range(100):
yield np.random.randn(128)
ds1 = tf.data.Dataset.from_generator(gen1, (x_type,), (x_shape_nobatch,))
ds1 = ds1.batch(5)
ds1_init_op = iterator_x.make_initializer(ds1)
def gen2():
for i in range(80):
yield np.random.randn(128), np.random.randn(10)
ds2 = tf.data.Dataset.from_generator(gen2, (x_type, y_type), (x_shape_nobatch, y_shape_nobatch))
ds2 = ds2.batch(10)
# my ds2 has two tensors in one element, now the problem is
# how can I unzip this dataset so that I can apply them to iterator_x and iterator_y?
# such as:
ds2_x, ds2_y = tf.data.Dataset.unzip(ds2) #?? missing this unzip operation!
ds2_x_init_op = iterator_x.make_initializer(ds2_x)
ds2_y_init_op = iterator_y.make_initializer(ds2_y)
tf_x = iterator_x.get_next()
tf_y = iterator_y.get_next()
数据集 API 的目的是避免将值直接提供给会话(因为这会导致数据首先流向客户端，然后流向设备).
The purpose of datasets API is to avoid feeding the values directly to session (because that causes the data to flow first to the client, then to a device).
我见过的所有使用数据集 API 的示例也使用 estimator API，您可以在其中可以为训练和推理提供不同的输入函数.
All examples I've seen that use datasets API also use estimator API, where you can provide different input functions for training and inference.
def train_dataset(data_dir):
"""Returns a tf.data.Dataset yielding (image, label) pairs for training."""
data = input_data.read_data_sets(data_dir, one_hot=True).train
return tf.data.Dataset.from_tensor_slices((data.images, data.labels))
def infer_dataset(data_dir):
"""Returns a tf.data.Dataset yielding images for inference."""
data = input_data.read_data_sets(data_dir, one_hot=True).test
return tf.data.Dataset.from_tensors((data.images,))
...
def train_input_fn():
dataset = train_dataset(FLAGS.data_dir)
dataset = dataset.shuffle(buffer_size=50000).batch(1024).repeat(10)
(images, labels) = dataset.make_one_shot_iterator().get_next()
return (images, labels)
mnist_classifier.train(input_fn=train_input_fn)
...
def infer_input_fn():
return infer_dataset(FLAGS.data_dir).make_one_shot_iterator().get_next()
mnist_classifier.predict(input_fn=infer_input_fn)
这篇关于如何在训练和推理中使用 tf.Dataset 设计?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！