gluon.Constant

class mxnet.gluon.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([ctx])

Returns a copy of this parameter on one context.

grad([ctx])

Returns a gradient buffer for this parameter on one context.

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

Initializes parameter and gradient arrays.

list_ctx()

Returns a list of contexts this parameter is initialized on.

list_data()

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

list_grad()

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

list_row_sparse_data(row_id)

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

reset_ctx(ctx)

Re-assign Parameter to other contexts.

row_sparse_data(row_id)

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

set_data(data)

Sets this parameter’s value on all contexts.

var()

Returns a symbol representing this parameter.

zero_grad()

Sets gradient buffer on all contexts 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(ctx=None)

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

Parameters

ctx (Context) – Desired context.

Returns

Return type

NDArray on ctx

property dtype

The type of the parameter.

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

grad(ctx=None)

Returns a gradient buffer for this parameter on one context.

Parameters

ctx (Context) – Desired context.

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

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

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

  • ctx (Context or list of Context, defaults to context.current_context().) –

    Initialize Parameter on given context. If ctx is a list of Context, a copy will be made for each context.

    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.

  • structural_name (str, default "") – The structural name for the parameter in the block. The value would be accessed in InitDesc.attrs[‘structure’] by self-defined initializers. Users may want to initialize parameters based on the block’s structure

Examples

>>> weight = mx.gluon.Parameter('weight', shape=(2, 2))
>>> weight.initialize(ctx=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(ctx=[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()

Returns a list of contexts this parameter is initialized on.

list_data()

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

Returns

Return type

list of NDArrays

list_grad()

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

list_row_sparse_data(row_id)

Returns copies of the ‘row_sparse’ parameter on all contexts, 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)

Re-assign Parameter to other contexts.

Parameters

ctx (Context or list of Context, default context.current_context().) – Assign Parameter to given context. If ctx is a list of Context, a copy will be made for each context.

row_sparse_data(row_id)

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

Parameters

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

Returns

Return type

NDArray on row_id’s context

set_data(data)

Sets this parameter’s value on all contexts.

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 contexts to 0. No action is taken if parameter is uninitialized or doesn’t require gradient.