#Tensorflow permute dimensions how to

SparseTensorSpec(TensorShape(), tf.int32) TensorSpec(shape=(100,), dtype=tf.int32, name=None)))ĭataset4 = tf._tensors(tf.SparseTensor(indices=, ], values=, dense_shape=)) (TensorSpec(shape=(10,), dtype=tf.float32, name=None), (TensorSpec(shape=(), dtype=tf.float32, name=None), TensorSpec(shape=(10,), dtype=tf.float32, name=None)ĭataset2 = tf._tensor_slices( For example: dataset1 = tf._tensor_slices(tf.random.uniform()) Which may be a single component, a tuple of components, or a nested

Of tf.TypeSpec objects, matching the structure of the element, The Dataset.element_spec property allows you to inspect the type To be a single component, then you need to explicitly pack it To convert it into tuple and if you would like a list output Would like a list input to be treated as a structure, you need Tensors and list outputs (for example, return values of user-definedįunctions) being coerced into a tuple. To tf._tensors) being automatically packed as This is becauseĮarly tf.data users felt strongly about list inputs (for example, when passed In particular, list is not a valid construct forĮxpressing the structure of dataset elements. Structure of elements include tuple, dict, NamedTuple, and The Python constructs that can be used to express the (nested) Tf.RaggedTensor, tf.TensorArray, or tf.data.Dataset. Tf.TypeSpec, including tf.Tensor, tf.sparse.SparseTensor, Of the structure can be of any type representable by The same (nested) structure of components. print(dataset.reduce(0, lambda state, value: state + value).numpy())Ī dataset produces a sequence of elements, where each element is

#Tensorflow permute dimensions how to

Theįollowing example illustrates how to use the reduce transformation to compute Transformation, which reduces all elements to produce a single result. Or by explicitly creating a Python iterator using iter and consuming itsĪlternatively, dataset elements can be consumed using the reduce This makes it possible to consume itsĮlements using a for loop: dataset = tf._tensor_slices() Tf.data.Dataset for a complete list of transformations. For example, you canĪpply per-element transformations such as Dataset.map, and multi-element Once you have a Dataset object, you can transform it into a new Dataset byĬhaining method calls on the tf.data.Dataset object. TFRecord format, you can use tf.data.TFRecordDataset().

Tf._tensors() or tf._tensor_slices().Īlternatively, if your input data is stored in a file in the recommended To construct a Dataset from data in memory, you can use To create an input pipeline, you must start with a data source. There are two distinct ways to create a dataset:Ī data source constructs a Dataset from data stored in memory or inĪ data transformation constructs a dataset from one or more Sequence of elements, in which each element consists of one or more components.įor example, in an image pipeline, an element might be a single trainingĮxample, with a pair of tensor components representing the image and its label. The tf.data API introduces a tf.data.Dataset abstraction that represents a Handle large amounts of data, read from different data formats, and perform Pipeline for a text model might involve extracting symbols from raw text data,Ĭonverting them to embedding identifiers with a lookup table, and batching Image, and merge randomly selected images into a batch for training. For example, the pipeline for an image model might aggregateĭata from files in a distributed file system, apply random perturbations to each The tf.data API enables you to build complex input pipelines from simple,