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A. Unique TensorFlower
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# Tensor Ranks, Shapes, and Types
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TensorFlow programs use a tensor data structure to represent all data. You can
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think of a TensorFlow tensor as an n-dimensional array or list.
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A tensor has a static type and dynamic dimensions. Only tensors may be passed
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between nodes in the computation graph.
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## Rank
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In the TensorFlow system, tensors are described by a unit of dimensionality
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known as *rank*. Tensor rank is not the same as matrix rank. Tensor rank
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(sometimes referred to as *order* or *degree* or *n-dimension*) is the number
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of dimensions of the tensor. For example, the following tensor (defined as a
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Python list) has a rank of 2:
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t = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
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A rank two tensor is what we typically think of as a matrix, a rank one tensor
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is a vector. For a rank two tensor you can access any element with the syntax
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`t[i, j]`. For a rank three tensor you would need to address an element with
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`t[i, j, k]`.
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Rank | Math entity | Python example
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--- | --- | ---
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0 | Scalar (magnitude only) | `s = 483`
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1 | Vector (magnitude and direction) | `v = [1.1, 2.2, 3.3]`
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2 | Matrix (table of numbers) | `m = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]`
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3 | 3-Tensor (cube of numbers) | `t = [[[2], [4], [6]], [[8], [10], [12]], [[14], [16], [18]]]`
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n | n-Tensor (you get the idea) | `....`
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## Shape
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The TensorFlow documentation uses three notational conventions to describe
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tensor dimensionality: rank, shape, and dimension number. The following table
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shows how these relate to one another:
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Rank | Shape | Dimension number | Example
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--- | --- | --- | ---
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0 | [] | 0-D | A 0-D tensor. A scalar.
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1 | [D0] | 1-D | A 1-D tensor with shape [5].
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2 | [D0, D1] | 2-D | A 2-D tensor with shape [3, 4].
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3 | [D0, D1, D2] | 3-D | A 3-D tensor with shape [1, 4, 3].
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n | [D0, D1, ... Dn-1] | n-D | A tensor with shape [D0, D1, ... Dn-1].
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Shapes can be represented via Python lists / tuples of ints, or with the
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@{tf.TensorShape}.
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## Data types
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In addition to dimensionality, Tensors have a data type. You can assign any one
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of the following data types to a tensor:
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Data type | Python type | Description
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--- | --- | ---
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`DT_FLOAT` | `tf.float32` | 32 bits floating point.
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`DT_DOUBLE` | `tf.float64` | 64 bits floating point.
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`DT_INT8` | `tf.int8` | 8 bits signed integer.
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`DT_INT16` | `tf.int16` | 16 bits signed integer.
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`DT_INT32` | `tf.int32` | 32 bits signed integer.
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`DT_INT64` | `tf.int64` | 64 bits signed integer.
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`DT_UINT8` | `tf.uint8` | 8 bits unsigned integer.
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`DT_UINT16` | `tf.uint16` | 16 bits unsigned integer.
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`DT_STRING` | `tf.string` | Variable length byte arrays. Each element of a Tensor is a byte array.
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`DT_BOOL` | `tf.bool` | Boolean.
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`DT_COMPLEX64` | `tf.complex64` | Complex number made of two 32 bits floating points: real and imaginary parts.
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`DT_COMPLEX128` | `tf.complex128` | Complex number made of two 64 bits floating points: real and imaginary parts.
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`DT_QINT8` | `tf.qint8` | 8 bits signed integer used in quantized Ops.
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`DT_QINT32` | `tf.qint32` | 32 bits signed integer used in quantized Ops.
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`DT_QUINT8` | `tf.quint8` | 8 bits unsigned integer used in quantized Ops.
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@{$deep_cnn$CIFAR-10 tutorial} for an example model that
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uses multiple GPUs.
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#### What are the different types of tensors that are available?
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TensorFlow supports a variety of different data types and tensor shapes. See the
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@{$dims_types$ranks, shapes, and types reference} for more details.
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## Running a TensorFlow computation
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@ -171,7 +167,8 @@ available. These operations allow you to build sophisticated
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@{$reading_data$input pipelines}, at the cost of making the
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TensorFlow computation somewhat more complicated. See the how-to documentation
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for
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@{$reading_data#creating-threads-to-prefetch-using-queuerunner-objects$using `QueueRunner` objects to drive queues and readers}
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@{$reading_data#creating-threads-to-prefetch-using-queuerunner-objects$using
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`QueueRunner` objects to drive queues and readers}
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for more information on how to use them.
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## Variables
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@ -240,11 +237,6 @@ to encode the batch size as a Python constant, but instead to use a symbolic
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* Use @{tf.reduce_mean} instead
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of `tf.reduce_sum(...) / batch_size`.
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* If you use
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@{$reading_data#feeding$placeholders for feeding input},
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you can specify a variable batch dimension by creating the placeholder with
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[`tf.placeholder(..., shape=[None, ...])`](../api_docs/python/io_ops.md#placeholder). The
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`None` element of the shape corresponds to a variable-sized dimension.
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## TensorBoard
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@ -269,36 +261,33 @@ the flag --host=localhost. This should quiet any security warnings.
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## Extending TensorFlow
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See also the how-to documentation for
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See the how-to documentation for
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@{$adding_an_op$adding a new operation to TensorFlow}.
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#### My data is in a custom format. How do I read it using TensorFlow?
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There are two main options for dealing with data in a custom format.
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There are three main options for dealing with data in a custom format.
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The easier option is to write parsing code in Python that transforms the data
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into a numpy array, then feed a
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@{tf.placeholder} a tensor with
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that data. See the documentation on
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@{$reading_data#feeding$using placeholders for input} for
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more details. This approach is easy to get up and running, but the parsing can
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be a performance bottleneck.
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The easiest option is to write parsing code in Python that transforms the data
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into a numpy array. Then use @{tf.contrib.data.Dataset.from_tensor_slices} to
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create an input pipeline from the in-memory data.
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The more efficient option is to
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If your data doesn't fit in memory, try doing the parsing in the Dataset
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pipeline. Start with an appropriate file reader, like
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@{tf.contrib.data.TextLineDataset}. Then convert the dataset by mapping
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@{tf.contrib.data.Dataset.map$mapping} appropriate operations over it.
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Prefer predefined TensorFlow operations such as @{tf.decode_raw},
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@{tf.decode_csv}, @{tf.parse_example}, or @{tf.image.decode_png}.
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If your data is not easily parsable with the built-in TensorFlow operations,
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consider converting it, offline, to a format that is easily parsable, such
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as ${tf.python_io.TFRecordWriter$`TFRecord`} format.
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The more efficient method to customize the parsing behavior is to
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@{$adding_an_op$add a new op written in C++} that parses your
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data format. The
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@{$new_data_formats$guide to handling new data formats} has
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data format. The @{$new_data_formats$guide to handling new data formats} has
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more information about the steps for doing this.
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#### How do I define an operation that takes a variable number of inputs?
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The TensorFlow op registration mechanism allows you to define inputs that are a
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single tensor, a list of tensors with the same type (for example when adding
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together a variable-length list of tensors), or a list of tensors with different
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types (for example when enqueuing a tuple of tensors to a queue). See the
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how-to documentation for
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@{$adding_an_op#list-inputs-and-outputs$adding an op with a list of inputs or outputs}
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for more details of how to define these different input types.
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## Miscellaneous
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# Programmer's Guide
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The documents in this unit dive into the details of writing TensorFlow
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code. This section begins with the following guides, each of which
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explain a particular aspect of TensorFlow:
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code. For TensorFlow 1.3, we revised this document extensively.
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The units are now as follows:
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* @{$variables$Variables: Creation, Initialization, Saving, Loading, and
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Sharing}, which details the mechanics of TensorFlow Variables.
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* @{$dims_types$Tensor Ranks, Shapes, and Types}, which explains Tensor
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rank (the number of dimensions), shape (the size of each dimension),
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and datatypes.
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* @{$threading_and_queues$Threading and Queues}, which explains TensorFlow's
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rich queuing system.
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* @{$reading_data$Reading Data}, which documents three different mechanisms
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for getting data into a TensorFlow program.
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The following guide is helpful when training a complex model over multiple
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days:
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* @{$supervisor$Supervisor: Training Helper for Days-Long Trainings}, which
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explains how to gracefully handle system crashes during a lengthy training
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session.
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TensorFlow provides a debugger named `tfdbg`, which is documented in the
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following guide:
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* @{$debugger$Debugging TensorFlow Programs},
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which walks you through the use of `tfdbg` within an application. It covers
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using `tfdbg` with both the low-level TensorFlow API and the Estimator API.
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To learn about the TensorFlow versioning scheme consult:
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* @{$version_compat$The TensorFlow Version Compatibility Guide}, which explains
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TensorFlow's versioning nomenclature and compatibility rules.
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We conclude this section with a FAQ about TensorFlow programming:
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* @{$faq$Frequently Asked Questions}
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* @{$programmers_guide/tensors$Tensors}, which explains how to create,
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manipulate, and access Tensors--the fundamental object in TensorFlow.
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* @{$programmers_guide/variables$Variables}, which details how
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to represent shared, persistent state in your program.
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* @{$programmers_guide/graphs$Graphs and Sessions}, which explains:
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* dataflow graphs, which are TensorFlow's representation of computations
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as dependencies between operations.
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* sessions, which are TensorFlow's mechanism for running dataflow graphs
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across one or more local or remote devices.
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If you are programming with the low-level TensorFlow API, this unit
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is essential. If you are programming with a high-level TensorFlow API
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such as Estimators or Keras, the high-level API creates and manages
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graphs and sessions for you, but understanding graphs and sessions
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can still be helpful.
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* @{$programmers_guide/estimators$Estimators}, which introduces a high-level
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TensorFlow API that greatly simplifies ML programming.
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* @{$programmers_guide/saved_model$Saving and Restoring}, which
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explains how to save and restore variables and models.
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* @{$programmers_guide/datasets$Input Pipelines}, which explains how to
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set up data pipelines to read data sets into your TensorFlow program.
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* @{$programmers_guide/threading_and_queues$Threading and Queues}, which
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explains TensorFlow's older system for multi-threaded, queue-based input
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pipelines. Beginning with TensorFlow 1.2, we recommend using the
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`tf.contrib.data` module instead, which is documented in the
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"Input Pipelines" unit.
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* @{$programmers_guide/embedding$Embeddings}, which introduces the concept
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of embeddings, provides a simple example of training an embedding in
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TensorFlow, and explains how to view embeddings with the TensorBoard
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Embedding Projector.
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* @{$programmers_guide/debugger$Debugging TensorFlow Programs}, which
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explains how to use the TensorFlow debugger (tfdbg).
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* @{$programmers_guide/supervisor$Supervisor: Training Helper for Days-Long Trainings},
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which explains how to gracefully handle system crashes during lengthy
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training sessions. (We have not revised this document for v1.3.)
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* @{$programmers_guide/version_compat$TensorFlow Version Compatibility},
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which explains backward compatibility guarantees and non-guarantees.
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* @{$programmers_guide/faq$FAQ}, which contains frequently asked
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questions about TensorFlow. (We have not revised this document for v1.3,
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except to remove some obsolete information.)
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@ -1,15 +1,13 @@
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index.md
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tensors.md
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variables.md
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dims_types.md
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graphs.md
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estimators.md
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saved_model.md
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datasets.md
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threading_and_queues.md
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reading_data.md
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embedding.md
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debugger.md
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supervisor.md
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saved_model.md
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meta_graph.md
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version_compat.md
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faq.md
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