gluon.Constant

class Constant(value)[source]

Bases: mxnet.gluon.parameter.Parameter

A constant parameter for holding immutable tensors. Constant`s are ignored by `autograd and Trainer, thus their values will not change during training. But you can still update their values manually with the set_data method.

Constant s can be created with either:

const = mx.gluon.Constant([[1,2],[3,4]])

Methods

cast(dtype)

Cast data and gradient of this Parameter to a new data type.

data([device])

Returns a copy of this parameter on one device.

grad([device])

Returns a gradient buffer for this parameter on one device.

initialize([init, device, default_init, …])

Initializes parameter and gradient arrays.

list_ctx()

This function has been deprecated.

list_data()

Returns copies of this parameter on all devices, in the same order as creation.

list_device()

Returns a list of devices this parameter is initialized on.

list_grad()

Returns gradient buffers on all devices, in the same order as values().

list_row_sparse_data(row_id)

Returns copies of the ‘row_sparse’ parameter on all devices, in the same order as creation.

reset_ctx(ctx)

This function has been deprecated.

reset_device(device)

Re-assign Parameter to other devices.

row_sparse_data(row_id)

Returns a copy of the ‘row_sparse’ parameter on the same device as row_id’s.

set_data(data)

Sets this parameter’s value on all devices.

var()

Returns a symbol representing this parameter.

zero_grad()

Sets gradient buffer on all devices to 0.

Attributes

dtype

The type of the parameter.

shape

The shape of the parameter.

or:

class Block(gluon.Block):
    def __init__(self, **kwargs):
        super(Block, self).__init__(**kwargs)
        self.const = mx.gluon.Constant([[1,2],[3,4]])
Parameters

value (array-like) – Initial value for the constant.

cast(dtype)

Cast data and gradient of this Parameter to a new data type.

Parameters

dtype (str or numpy.dtype) – The new data type.

data(device=None)

Returns a copy of this parameter on one device. Must have been initialized on this device before. For sparse parameters, use Parameter.row_sparse_data() instead.

Parameters

device (Device) – Desired device.

Returns

Return type

NDArray on device

property dtype

The type of the parameter.

Setting the dtype value is equivalent to casting the value of the parameter

grad(device=None)

Returns a gradient buffer for this parameter on one device.

Parameters

device (Device) – Desired device.

initialize(init=None, device=None, default_init=<mxnet.initializer.Uniform object>, force_reinit=False)

Initializes parameter and gradient arrays. Only used for NDArray API.

Parameters
  • init (Initializer) – The initializer to use. Overrides Parameter.init() and default_init.

  • device (Device or list of Device, default device.current_device().) –

    Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

    Note

    Copies are independent arrays. User is responsible for keeping their values consistent when updating. Normally gluon.Trainer does this for you.

  • default_init (Initializer) – Default initializer is used when both init() and Parameter.init() are None.

  • force_reinit (bool, default False) – Whether to force re-initialization if parameter is already initialized.

Examples

>>> weight = mx.gluon.Parameter('weight', shape=(2, 2))
>>> weight.initialize(device=mx.cpu(0))
>>> weight.data()
[[-0.01068833  0.01729892]
 [ 0.02042518 -0.01618656]]
<NDArray 2x2 @cpu(0)>
>>> weight.grad()
[[ 0.  0.]
 [ 0.  0.]]
<NDArray 2x2 @cpu(0)>
>>> weight.initialize(device=[mx.gpu(0), mx.gpu(1)])
>>> weight.data(mx.gpu(0))
[[-0.00873779 -0.02834515]
 [ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(0)>
>>> weight.data(mx.gpu(1))
[[-0.00873779 -0.02834515]
 [ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(1)>
list_ctx()

This function has been deprecated. Please refer to Parameter.list_device.

list_data()

Returns copies of this parameter on all devices, in the same order as creation. For sparse parameters, use Parameter.list_row_sparse_data() instead.

Returns

Return type

list of NDArrays

list_device()

Returns a list of devices this parameter is initialized on.

list_grad()

Returns gradient buffers on all devices, in the same order as values().

list_row_sparse_data(row_id)

Returns copies of the ‘row_sparse’ parameter on all devices, in the same order as creation. The copy only retains rows whose ids occur in provided row ids. The parameter must have been initialized before.

Parameters

row_id (NDArray) – Row ids to retain for the ‘row_sparse’ parameter.

Returns

Return type

list of NDArrays

reset_ctx(ctx)

This function has been deprecated. Please refer to Parameter.reset_device.

reset_device(device)

Re-assign Parameter to other devices.

Parameters

device (Device or list of Device, default device.current_device().) – Assign Parameter to given device. If device is a list of Device, a copy will be made for each device.

row_sparse_data(row_id)

Returns a copy of the ‘row_sparse’ parameter on the same device as row_id’s. The copy only retains rows whose ids occur in provided row ids. The parameter must have been initialized on this device before.

Parameters

row_id (NDArray) – Row ids to retain for the ‘row_sparse’ parameter.

Returns

Return type

NDArray on row_id’s device

set_data(data)

Sets this parameter’s value on all devices.

property shape

The shape of the parameter.

By default, an unknown dimension size is 0. However, when the NumPy semantic is turned on, unknown dimension size is -1.

var()

Returns a symbol representing this parameter.

zero_grad()

Sets gradient buffer on all devices to 0. No action is taken if parameter is uninitialized or doesn’t require gradient.