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+# Tensor Ranks, Shapes, and Types
+
+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
+
+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]]
+
+A rank two tensor is what we typically think of as a matrix, a rank on 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
+--- | --- | ---
+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) | `....`
+
+## Shape
+
+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
+--- | --- | --- | ---
+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).
+
+## Data types
+
+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
+--- | --- | ---
+`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.
+