_sources/api/python/ndarray.txt - mxnet-test - Git at Google

 # NDArray API

 ```eval_rst
 .. currentmodule:: mxnet.ndarray
 ```

 ## Overview

 This document lists the routines of the *n*-dimensional array package:

 ```eval_rst
 .. autosummary::
     :nosignatures:

     mxnet.ndarray
 ```

 The `NDArray` API, defined in the `ndarray` (or simply `nd`) package, provides
 imperative tensor operations on CPU/GPU.
 An `NDArray` represents a multi-dimensional, fixed-size homogenous array.

 ```python
 >>> x = mx.nd.array([[1, 2, 3], [4, 5, 6]])
 >>> type(x)
 <class 'mxnet.ndarray.NDArray'>
 >>> x.shape
 (2, 3)
 >>> y = x + mx.nd.ones(x.shape)*3
 >>> print(y.asnumpy())
 [[ 4.  5.  6.]
  [ 7.  8.  9.]]
 >>> z = y.as_in_context(mx.gpu(0))
 >>> print(z)
 <NDArray 2x3 @gpu(0)>
 ```

 A detailed tutorial is available at
 [NDArray - Imperative tensor operations on CPU/GPU](http://mxnet.io/tutorials/basic/ndarray.html).
 <br><br>

 ```eval_rst

 .. note:: ``mxnet.ndarray`` is similar to ``numpy.ndarray`` in some aspects. But the differences are not negligible. For instance:

    - ``mxnet.ndarray.NDArray.T`` does real data transpose to return new a copied
      array, instead of returning a view of the input array.
    - ``mxnet.ndarray.dot`` performs dot product between the last axis of the
      first input array and the first axis of the second input, while `numpy.dot`
      uses the second last axis of the input array.

    In addition, ``mxnet.ndarray.NDArray`` supports GPU computation and various neural
    network layers.

 .. note:: ``ndarray`` provides almost the same routines as ``symbol``. Most
   routines between these two packages share the source code. But ``ndarray``
   differs from ``symbol`` in few aspects:

   - ``ndarray`` adopts imperative programming, namely sentences are executed
     step-by-step so that the results can be obtained immediately whereas
     ``symbol`` adopts declarative programming.

   - Most binary operators in ``ndarray`` such as ``+`` and ``>`` have
     broadcasting enabled by default.
 ```

 In the rest of this document, we first overview the methods provided by the
 `ndarray.NDArray` class, and then list other routines provided by the
 `ndarray` package.


 ## The `NDArray` class

 ### Array attributes

 ```eval_rst
 .. autosummary::
     :nosignatures:

     NDArray.shape
     NDArray.size
     NDArray.context
     NDArray.dtype
 ```

 ### Array conversion

 ```eval_rst
 .. autosummary::
     :nosignatures:

     NDArray.copy
     NDArray.copyto
     NDArray.as_in_context
     NDArray.asnumpy
     NDArray.asscalar
     NDArray.astype
 ```

 ### Array change shape

 ```eval_rst
 .. autosummary::
     :nosignatures:

     NDArray.T
     NDArray.reshape
     NDArray.broadcast_to
 ```

 ### Arithmetic operations

 ```eval_rst
 .. autosummary::
     :nosignatures:

     NDArray.__add__
     NDArray.__sub__
     NDArray.__rsub__
     NDArray.__neg__
     NDArray.__mul__
     NDArray.__div__
     NDArray.__rdiv__
     NDArray.__mod__
     NDArray.__rmod__
     NDArray.__pow__
 ```

 ### In-place arithmetic operations

 ```eval_rst
 .. autosummary::
     :nosignatures:

     NDArray.__iadd__
     NDArray.__isub__
     NDArray.__imul__
     NDArray.__idiv__
     NDArray.__imod__
 ```

 ### Comparison operators

 ```eval_rst
 .. autosummary::
     :nosignatures:

     NDArray.__lt__
     NDArray.__le__
     NDArray.__gt__
     NDArray.__ge__
     NDArray.__eq__
     NDArray.__ne__
 ```

 ### Indexing

 ```eval_rst
 .. autosummary::
     :nosignatures:

     NDArray.__getitem__
     NDArray.__setitem__
 ```

 ### Lazy evaluation

 ```eval_rst
 .. autosummary::
     :nosignatures:

     NDArray.wait_to_read
 ```

 ## Array creation routines

 ```eval_rst
 .. autosummary::
     :nosignatures:

     array
     empty
     zeros
     ones
     full
     arange
     load
     save
 ```

 ## Array manipulation routines

 ### Changing array shape and type

 ```eval_rst
 .. autosummary::
     :nosignatures:

     cast
     reshape
     flatten
     expand_dims
 ```

 ### Expanding array elements

 ```eval_rst
 .. autosummary::
     :nosignatures:

     broadcast_to
     broadcast_axes
     repeat
     tile
     pad
 ```

 ### Rearranging elements

 ```eval_rst
 .. autosummary::
     :nosignatures:

     transpose
     swapaxes
     flip
 ```

 ### Joining and splitting arrays

 ```eval_rst
 .. autosummary::
     :nosignatures:

     concat
     split
 ```

 ### Indexing routines

 ```eval_rst
 .. autosummary::
     :nosignatures:

     slice
     slice_axis
     take
     batch_take
     one_hot
     pick
 ```

 ## Mathematical functions

 ### Arithmetic operations

 ```eval_rst
 .. autosummary::
     :nosignatures:

     add
     subtract
     negative
     multiply
     divide
     modulo
     dot
     batch_dot
     add_n
 ```

 ### Trigonometric functions

 ```eval_rst
 .. autosummary::
     :nosignatures:

     sin
     cos
     tan
     arcsin
     arccos
     arctan
     degrees
     radians
 ```

 ### Hyperbolic functions

 ```eval_rst
 .. autosummary::
     :nosignatures:

     sinh
     cosh
     tanh
     arcsinh
     arccosh
     arctanh
 ```

 ### Reduce functions

 ```eval_rst
 .. autosummary::
     :nosignatures:

     sum
     nansum
     prod
     nanprod
     mean
     max
     min
     norm
 ```

 ### Rounding

 ```eval_rst
 .. autosummary::
     :nosignatures:

     round
     rint
     fix
     floor
     ceil
     trunc
 ```


 ### Exponents and logarithms

 ```eval_rst
 .. autosummary::
     :nosignatures:

     exp
     expm1
     log
     log10
     log2
     log1p
 ```

 ### Powers

 ```eval_rst
 .. autosummary::
     :nosignatures:

     power
     sqrt
     rsqrt
     square
 ```

 ### Logic functions

 ```eval_rst
 .. autosummary::
     :nosignatures:

     equal
     not_equal
     greater
     greater_equal
     lesser
     lesser_equal
 ```
 ### Random sampling

 ```eval_rst
 .. autosummary::
     :nosignatures:

     random_uniform
     random_normal
     random_gamma
     random_exponential
     random_poisson
     random_negative_binomial
     random_generalized_negative_binomial
     mxnet.random.seed
 ```

 ### Sorting and searching

 ```eval_rst
 .. autosummary::
     :nosignatures:

     sort
     topk
     argsort
     argmax
     argmin
 ```

 ### Miscellaneous

 ```eval_rst
 .. autosummary::
     :nosignatures:

     maximum
     minimum
     clip
     abs
     sign
     gamma
     gammaln
 ```

 ## Neural network

 ### Basic

 ```eval_rst
 .. autosummary::
     :nosignatures:

     FullyConnected
     Convolution
     Activation
     BatchNorm
     Pooling
     SoftmaxOutput
     softmax
     log_softmax
 ```

 ### More

 ```eval_rst
 .. autosummary::
     :nosignatures:

     Correlation
     Deconvolution
     RNN
     Embedding
     LeakyReLU
     InstanceNorm
     L2Normalization
     LRN
     ROIPooling
     SoftmaxActivation
     Dropout
     BilinearSampler
     GridGenerator
     UpSampling
     SpatialTransformer
     LinearRegressionOutput
     LogisticRegressionOutput
     MAERegressionOutput
     SVMOutput
     softmax_cross_entropy
     smooth_l1
     IdentityAttachKLSparseReg
     MakeLoss
     BlockGrad
     Custom
 ```

 ## API Reference

 <script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>

 ```eval_rst
 .. automodule:: mxnet.ndarray
     :members:

 .. automodule:: mxnet.random
     :members:

 ```

 <script>auto_index("api-reference");</script>
	# NDArray API

	```eval_rst
	.. currentmodule:: mxnet.ndarray
	```

	## Overview

	This document lists the routines of the n-dimensional array package:

	```eval_rst
	.. autosummary::
	:nosignatures:

	mxnet.ndarray
	```

	The `NDArray` API, defined in the `ndarray` (or simply `nd`) package, provides
	imperative tensor operations on CPU/GPU.
	An `NDArray` represents a multi-dimensional, fixed-size homogenous array.

	```python
	>>> x = mx.nd.array([[1, 2, 3], [4, 5, 6]])
	>>> type(x)
	<class 'mxnet.ndarray.NDArray'>
	>>> x.shape
	(2, 3)
	>>> y = x + mx.nd.ones(x.shape)*3
	>>> print(y.asnumpy())
	[[ 4. 5. 6.]
	[ 7. 8. 9.]]
	>>> z = y.as_in_context(mx.gpu(0))
	>>> print(z)
	<NDArray 2x3 @gpu(0)>
	```

	A detailed tutorial is available at
	[NDArray - Imperative tensor operations on CPU/GPU](http://mxnet.io/tutorials/basic/ndarray.html).
	<br><br>

	```eval_rst

	.. note:: ``mxnet.ndarray`` is similar to ``numpy.ndarray`` in some aspects. But the differences are not negligible. For instance:

	- ``mxnet.ndarray.NDArray.T`` does real data transpose to return new a copied
	array, instead of returning a view of the input array.
	- ``mxnet.ndarray.dot`` performs dot product between the last axis of the
	first input array and the first axis of the second input, while `numpy.dot`
	uses the second last axis of the input array.

	In addition, ``mxnet.ndarray.NDArray`` supports GPU computation and various neural
	network layers.

	.. note:: ``ndarray`` provides almost the same routines as ``symbol``. Most
	routines between these two packages share the source code. But ``ndarray``
	differs from ``symbol`` in few aspects:

	- ``ndarray`` adopts imperative programming, namely sentences are executed
	step-by-step so that the results can be obtained immediately whereas
	``symbol`` adopts declarative programming.

	- Most binary operators in ``ndarray`` such as ``+`` and ``>`` have
	broadcasting enabled by default.
	```

	In the rest of this document, we first overview the methods provided by the
	`ndarray.NDArray` class, and then list other routines provided by the
	`ndarray` package.


	## The `NDArray` class

	### Array attributes

	```eval_rst
	.. autosummary::
	:nosignatures:

	NDArray.shape
	NDArray.size
	NDArray.context
	NDArray.dtype
	```

	### Array conversion

	```eval_rst
	.. autosummary::
	:nosignatures:

	NDArray.copy
	NDArray.copyto
	NDArray.as_in_context
	NDArray.asnumpy
	NDArray.asscalar
	NDArray.astype
	```

	### Array change shape

	```eval_rst
	.. autosummary::
	:nosignatures:

	NDArray.T
	NDArray.reshape
	NDArray.broadcast_to
	```

	### Arithmetic operations

	```eval_rst
	.. autosummary::
	:nosignatures:

	NDArray.__add__
	NDArray.__sub__
	NDArray.__rsub__
	NDArray.__neg__
	NDArray.__mul__
	NDArray.__div__
	NDArray.__rdiv__
	NDArray.__mod__
	NDArray.__rmod__
	NDArray.__pow__
	```

	### In-place arithmetic operations

	```eval_rst
	.. autosummary::
	:nosignatures:

	NDArray.__iadd__
	NDArray.__isub__
	NDArray.__imul__
	NDArray.__idiv__
	NDArray.__imod__
	```

	### Comparison operators

	```eval_rst
	.. autosummary::
	:nosignatures:

	NDArray.__lt__
	NDArray.__le__
	NDArray.__gt__
	NDArray.__ge__
	NDArray.__eq__
	NDArray.__ne__
	```

	### Indexing

	```eval_rst
	.. autosummary::
	:nosignatures:

	NDArray.__getitem__
	NDArray.__setitem__
	```

	### Lazy evaluation

	```eval_rst
	.. autosummary::
	:nosignatures:

	NDArray.wait_to_read
	```

	## Array creation routines

	```eval_rst
	.. autosummary::
	:nosignatures:

	array
	empty
	zeros
	ones
	full
	arange
	load
	save
	```

	## Array manipulation routines

	### Changing array shape and type

	```eval_rst
	.. autosummary::
	:nosignatures:

	cast
	reshape
	flatten
	expand_dims
	```

	### Expanding array elements

	```eval_rst
	.. autosummary::
	:nosignatures:

	broadcast_to
	broadcast_axes
	repeat
	tile
	pad
	```

	### Rearranging elements

	```eval_rst
	.. autosummary::
	:nosignatures:

	transpose
	swapaxes
	flip
	```

	### Joining and splitting arrays

	```eval_rst
	.. autosummary::
	:nosignatures:

	concat
	split
	```

	### Indexing routines

	```eval_rst
	.. autosummary::
	:nosignatures:

	slice
	slice_axis
	take
	batch_take
	one_hot
	pick
	```

	## Mathematical functions

	### Arithmetic operations

	```eval_rst
	.. autosummary::
	:nosignatures:

	add
	subtract
	negative
	multiply
	divide
	modulo
	dot
	batch_dot
	add_n
	```

	### Trigonometric functions

	```eval_rst
	.. autosummary::
	:nosignatures:

	sin
	cos
	tan
	arcsin
	arccos
	arctan
	degrees
	radians
	```

	### Hyperbolic functions

	```eval_rst
	.. autosummary::
	:nosignatures:

	sinh
	cosh
	tanh
	arcsinh
	arccosh
	arctanh
	```

	### Reduce functions

	```eval_rst
	.. autosummary::
	:nosignatures:

	sum
	nansum
	prod
	nanprod
	mean
	max
	min
	norm
	```

	### Rounding

	```eval_rst
	.. autosummary::
	:nosignatures:

	round
	rint
	fix
	floor
	ceil
	trunc
	```


	### Exponents and logarithms

	```eval_rst
	.. autosummary::
	:nosignatures:

	exp
	expm1
	log
	log10
	log2
	log1p
	```

	### Powers

	```eval_rst
	.. autosummary::
	:nosignatures:

	power
	sqrt
	rsqrt
	square
	```

	### Logic functions

	```eval_rst
	.. autosummary::
	:nosignatures:

	equal
	not_equal
	greater
	greater_equal
	lesser
	lesser_equal
	```
	### Random sampling

	```eval_rst
	.. autosummary::
	:nosignatures:

	random_uniform
	random_normal
	random_gamma
	random_exponential
	random_poisson
	random_negative_binomial
	random_generalized_negative_binomial
	mxnet.random.seed
	```

	### Sorting and searching

	```eval_rst
	.. autosummary::
	:nosignatures:

	sort
	topk
	argsort
	argmax
	argmin
	```

	### Miscellaneous

	```eval_rst
	.. autosummary::
	:nosignatures:

	maximum
	minimum
	clip
	abs
	sign
	gamma
	gammaln
	```

	## Neural network

	### Basic

	```eval_rst
	.. autosummary::
	:nosignatures:

	FullyConnected
	Convolution
	Activation
	BatchNorm
	Pooling
	SoftmaxOutput
	softmax
	log_softmax
	```

	### More

	```eval_rst
	.. autosummary::
	:nosignatures:

	Correlation
	Deconvolution
	RNN
	Embedding
	LeakyReLU
	InstanceNorm
	L2Normalization
	LRN
	ROIPooling
	SoftmaxActivation
	Dropout
	BilinearSampler
	GridGenerator
	UpSampling
	SpatialTransformer
	LinearRegressionOutput
	LogisticRegressionOutput
	MAERegressionOutput
	SVMOutput
	softmax_cross_entropy
	smooth_l1
	IdentityAttachKLSparseReg
	MakeLoss
	BlockGrad
	Custom
	```

	## API Reference

	<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>

	```eval_rst
	.. automodule:: mxnet.ndarray
	:members:

	.. automodule:: mxnet.random
	:members:

	```

	<script>auto_index("api-reference");</script>