它提供了与 reinitilizable iterator 类似的功能,并且在切换数据集的时候不需要在开始的时候初始化iterator,还是上面的例子,通过tf.data.Iterator.from_string_handle来定义一个 feedable iterator,达到切换数据集的目的: # Define training and validation datasets with the same structure. training_dataset = tf.data...
可以初始化具有相同structure的Dataset. 一般步骤: 创建迭代器:iterator=tf.data.Iterator.from_structure(...) 初始化迭代器:iterator.make_initializer(...) # Define training and validation datasets with the same structure.training_dataset=tf.data.Dataset.range(100).map(lambdax:x+tf.random_uniform([],...
output_types: A nested structure oftf.DTypeobjects corresponding to each component of an element of this iterator. output_shapes: A nested structure oftf.TensorShapeobjects corresponding to each component of an element of this iterator. output_classes: A nested structure of Pythontypeobjects correspo...
例如:使用上述相同的training和validation样本,你可以使用tf.data.Iterator.from_string_handle来定义一个feedable iterator,并允许你在两个datasets间切换: # Define training and validation datasets with the same structure. training_dataset = tf.data.Dataset.range(100).map( lambda x: x + tf.random_uniform...
在Tensorflow 的程序代码中,正是通过 Iterator 这根水管,才可以源源不断地从 Dataset 中取出数据。 但为了应付多变的环境,水管也需要变化,Iterator 也有许多种类。 四种方式: 单次迭代:(创建迭代器)tf.data.Dataset.make_one_shot_iterator(), 调用 iterator 的 get_next() 就可以轻松地取出数据了。[但这种方...
iterator = tf.data.Iterator.from_structure(output_types, output_shapes) img, label = iterator.get_next() train_init = iterator.make_initializer(training_data_set) with tf.Session() as sess: for i in range(epoches): sess.run(train_init) ...
需要首先运行初始化指令iterator.initializer(),支持参数化,使用tf.placeholder()可以在管道内传参 x=[[2.0,3.3],[1.2,3.2],[1.0,-2.3],[1.0,2.1],[-1.5,0.7],[1.9,-0.2],[1.9,-0.3]]y=[1,0,1,1,0,1,0]z=tf.placeholder(tf.float32,shape=[])data2=tf.data.Dataset.from_tensor_slices({"...
# Dictionary structure is also preserved.dataset = tf.data.Dataset.from_tensor_slices({"a":[1,2],"b":[3,4]}) list(dataset.as_numpy_iterator()) == [{'a':1,'b':3}, {'a':2,'b':4}]True # Two tensors can be combined into one Dataset object.features = tf.constant([[1,...
tf.data.Dataset.range(50)# A reinitializable iterator is defined by its structure. We could use the# `output_types` and `output_shapes` properties of either `training_dataset`# or `validation_dataset` here, because they are compatible.iterator = tf.data.Iterator.from_structure(training_...
dataset=tf.data.FixedLengthRecordDataset([file1,file2,...]) 将数据转换成TF Dataset对象后,我们可以用一个迭代器iterator对数据集进行遍历。每次调用get_next()函数,迭代器迭代Dataset对象,并返回一个样本或者一个批量的样本数据。我们先介绍make_one_shot_iterator(), ...