From 84545c0f433cc7f333b02cacb5348d9fec4b953d Mon Sep 17 00:00:00 2001 From: niebin Date: Mon, 23 Nov 2015 23:38:19 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E7=BF=BB=E8=AF=91+=E5=8E=9F?= =?UTF-8?q?=E6=96=87=E5=9C=B0=E5=9D=80=E4=BF=AE=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 添加翻译+原文地址修改 --- SOURCE/resources/dims_types.md | 89 ++++++++++++++-------------------- 1 file changed, 37 insertions(+), 52 deletions(-) diff --git a/SOURCE/resources/dims_types.md b/SOURCE/resources/dims_types.md index 8468768..3d87b93 100755 --- a/SOURCE/resources/dims_types.md +++ b/SOURCE/resources/dims_types.md @@ -1,67 +1,52 @@ -# Tensor Ranks, Shapes, and Types +# 张量的阶，形式，类型 +TensorFlow程序是用张量数据结构来表示所有的数据.你可以把一个TensorFlow张量想成是一个n维的数组或列表.一个张量有一个静态类型和动态类型的维数.只有张量可以在计算图中连通于节点之间. -TensorFlow programs use a tensor data structure to represent all data. You can -think of a TensorFlow tensor as an n-dimensional array or list. -A tensor has a static type and dynamic dimensions. Only tensors may be passed -between nodes in the computation graph. -## Rank +## 阶 +在TensorFlow系统中，张量通过单位维数来被描述为*阶*.但是张量阶并不和矩阵中的阶是同一个概念.张量阶（有时是关于如*顺序*或*度数*或者是*n维*）是张量维数的一个数量.比如，下面的张量（使用Python中list定义的）就是2阶. -In the TensorFlow system, tensors are described by a unit of dimensionality -known as *rank*. Tensor rank is not the same as matrix rank. Tensor rank -(sometimes referred to as *order* or *degree* or *n-dimension*) is the number -of dimensions of the tensor. For example, the following tensor (defined as a -Python list) has a rank of 2: t = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] +一个二阶张量就可以认为是我们平常所说的矩阵，一阶张量可以认为是一个向量.对于一个二阶张量你可以用语句`t[i, j]`来访问其中的任何元素.而对于三阶张量你可以用't[i, j, k]'. -A rank two tensor is what we typically think of as a matrix, a rank one tensor -is a vector. For a rank two tensor you can acccess any element with the syntax -`t[i, j]`. For a rank three tensor you would need to address an element with -'t[i, j, k]'. -Rank | Math entity | Python example +阶 |数学实例| Python 例子 --- | --- | --- -0 | Scalar (magnitude only) | `s = 483` -1 | Vector (magnitude and direction) | `v = [1.1, 2.2, 3.3]` -2 | Matrix (table of numbers) | `m = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]` -3 | 3-Tensor (cube of numbers) | `t = [[[2], [4], [6]], [[8], [10], [12]], [[14], [16], [18]]]` -n | n-Tensor (you get the idea) | `....` +0 | 纯量 (只有大小) | `s = 483` +1 | 向量(大小和方向) | `v = [1.1, 2.2, 3.3]` +2 | 矩阵(数据表) | `m = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]` +3 | 3阶张量 (数据立体) | `t = [[[2], [4], [6]], [[8], [10], [12]], [[14], [16], [18]]]` +n | n阶 (自己想想看) | `....` -## Shape +## 形式 +TensorFlow文档中使用了三种记号来方便地描述张量的维度：阶，形式以及维数.下表展示了他们之间的关系： -The TensorFlow documentation uses three notational conventions to describe -tensor dimensionality: rank, shape, and dimension number. The following table -shows how these relate to one another: - -Rank | Shape | Dimension number | Example +阶 | 形式 | 维数 | 实例 --- | --- | --- | --- -0 | [] | 0-D | A 0-D tensor. A scalar. -1 | [D0] | 1-D | A 1-D tensor with shape [5]. -2 | [D0, D1] | 2-D | A 2-D tensor with shape [3, 4]. -3 | [D0, D1, D2] | 3-D | A 3-D tensor with shape [1, 4, 3]. -n | [D0, D1, ... Dn] | n-D | A tensor with shape [D0, D1, ... Dn]. - -Shapes can be represented via Python lists / tuples of ints, or with the -[`TensorShape` class](../api_docs/python/framework.md#TensorShape). +0 | [] | 0-D | 一个 0维张量. 一个纯量. +1 | [D0] | 1-D | 一个1维张量的形式[5]. +2 | [D0, D1] | 2-D |一个2维张量的形式[3, 4]. +3 | [D0, D1, D2] | 3-D | 一个3维张量的形式 [1, 4, 3]. +n | [D0, D1, ... Dn] | n-D | 一个n维张量的形式 [D0, D1, ... Dn]. +形式可以通过Python中的int list或tuples来表示，也或者用[`TensorShape` class](../api_docs/python/framework.md#TensorShape). -## Data types +## 数据类型 +除了维度，Tensors有一个数据类型.你可以为一个张量指定一个下面数据类型中的任意一个类型： -In addition to dimensionality, Tensors have a data type. You can assign any one -of the following data types to a tensor: -Data type | Python type | Description +数据类型 | Python 类型| 描述 --- | --- | --- -`DT_FLOAT` | `tf.float32` | 32 bits floating point. -`DT_DOUBLE` | `tf.float64` | 64 bits floating point. -`DT_INT64` | `tf.int64` | 64 bits signed integer. -`DT_INT32` | `tf.int32` | 32 bits signed integer. -`DT_INT16` | `tf.int16` | 16 bits signed integer. -`DT_INT8` | `tf.int8` | 8 bits signed integer. -`DT_UINT8` | `tf.uint8` | 8 bits unsigned integer. -`DT_STRING` | `tf.string` | Variable length byte arrays. Each element of a Tensor is a byte array. -`DT_BOOL` | `tf.bool` | Boolean. -`DT_COMPLEX64` | `tf.complex64` | Complex number made of two 32 bits floating points: real and imaginary parts. -`DT_QINT32` | `tf.qint32` | 32 bits signed integer used in quantized Ops. -`DT_QINT8` | `tf.qint8` | 8 bits signed integer used in quantized Ops. -`DT_QUINT8` | `tf.quint8` | 8 bits unsigned integer used in quantized Ops. +`DT_FLOAT` | `tf.float32` | 32 位浮点数. +`DT_DOUBLE` | `tf.float64` | 64 位浮点数. +`DT_INT64` | `tf.int64` | 64 位有符号整型r. +`DT_INT32` | `tf.int32` | 32 位有符号整型. +`DT_INT16` | `tf.int16` | 16 位有符号整型. +`DT_INT8` | `tf.int8` | 8位有符号整型. +`DT_UINT8` | `tf.uint8` | 8位无符号整型. +`DT_STRING` | `tf.string` | 可变长度的字节数组.每一个张量元素都是一个字节数组. +`DT_BOOL` | `tf.bool` |布尔型. +`DT_COMPLEX64` | `tf.complex64` | 由两个32位浮点数组成的复数:实数和虚数. +`DT_QINT32` | `tf.qint32` | 用于量化Ops的32位有符号整型. +`DT_QINT8` | `tf.qint8` | 用于量化Ops的8位有符号整型. +`DT_QUINT8` | `tf.quint8` |用于量化Ops的8位无符号整型. +原文：[Tensor Ranks, Shapes, and Types](http://www.tensorflow.org/resources/dims_types.md) 翻译：[nb312](https://github.com/nb312)