gluon.Block

class mxnet.gluon.Block(prefix=None, params=None)[source]

Bases: object

Base class for all neural network layers and models. Your models should subclass this class.

Block can be nested recursively in a tree structure. You can create and assign child Block as regular attributes:

import mxnet as mx
from mxnet.gluon import Block, nn

class Model(Block):
    def __init__(self, **kwargs):
        super(Model, self).__init__(**kwargs)
        # use name_scope to give child Blocks appropriate names.
        with self.name_scope():
            self.dense0 = nn.Dense(20)
            self.dense1 = nn.Dense(20)

    def forward(self, x):
        x = mx.nd.relu(self.dense0(x))
        return mx.nd.relu(self.dense1(x))

model = Model()
model.initialize(ctx=mx.cpu(0))
model(mx.nd.zeros((10, 10), ctx=mx.cpu(0)))

Methods

apply(fn)

Applies fn recursively to every child block as well as self.

cast(dtype)

Cast this Block to use another data type.

collect_params([select])

Returns a ParameterDict containing this Block and all of its children’s Parameters(default), also can returns the select ParameterDict which match some given regular expressions.

forward(*args)

Overrides to implement forward computation using NDArray.

hybridize([active])

Please refer description of HybridBlock hybridize().

initialize([init, ctx, verbose, force_reinit])

Initializes Parameter s of this Block and its children.

load_parameters(filename[, ctx, …])

Load parameters from file previously saved by save_parameters.

load_params(filename[, ctx, allow_missing, …])

[Deprecated] Please use load_parameters.

name_scope()

Returns a name space object managing a child Block and parameter names.

register_child(block[, name])

Registers block as a child of self.

register_forward_hook(hook)

Registers a forward hook on the block.

register_forward_pre_hook(hook)

Registers a forward pre-hook on the block.

register_op_hook(callback[, monitor_all])

Install callback monitor.

save_parameters(filename[, deduplicate])

Save parameters to file.

save_params(filename)

[Deprecated] Please use save_parameters. Note that if you want load

summary(*inputs)

Print the summary of the model’s output and parameters.

Attributes

name

Name of this Block, without ‘_’ in the end.

params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

prefix

Prefix of this Block.

Child Block assigned this way will be registered and collect_params() will collect their Parameters recursively. You can also manually register child blocks with register_child().

Parameters
  • prefix (str) – Prefix acts like a name space. All children blocks created in parent block’s name_scope() will have parent block’s prefix in their name. Please refer to naming tutorial for more info on prefix and naming.

  • params (ParameterDict or None) –

    ParameterDict for sharing weights with the new Block. For example, if you want dense1 to share dense0’s weights, you can do:

    dense0 = nn.Dense(20)
    dense1 = nn.Dense(20, params=dense0.collect_params())
    

apply(fn)[source]

Applies fn recursively to every child block as well as self.

Parameters

fn (callable) – Function to be applied to each submodule, of form fn(block).

Returns

Return type

this block

cast(dtype)[source]

Cast this Block to use another data type.

Parameters

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

collect_params(select=None)[source]

Returns a ParameterDict containing this Block and all of its children’s Parameters(default), also can returns the select ParameterDict which match some given regular expressions.

For example, collect the specified parameters in [‘conv1_weight’, ‘conv1_bias’, ‘fc_weight’, ‘fc_bias’]:

model.collect_params('conv1_weight|conv1_bias|fc_weight|fc_bias')

or collect all parameters whose names end with ‘weight’ or ‘bias’, this can be done using regular expressions:

model.collect_params('.*weight|.*bias')
Parameters

select (str) – regular expressions

Returns

Return type

The selected ParameterDict

forward(*args)[source]

Overrides to implement forward computation using NDArray. Only accepts positional arguments.

Parameters

*args (list of NDArray) – Input tensors.

hybridize(active=True, **kwargs)[source]

Please refer description of HybridBlock hybridize().

initialize(init=<mxnet.initializer.Uniform object>, ctx=None, verbose=False, force_reinit=False)[source]

Initializes Parameter s of this Block and its children. Equivalent to block.collect_params().initialize(...)

Parameters
  • init (Initializer) – Global default Initializer to be used when Parameter.init() is None. Otherwise, Parameter.init() takes precedence.

  • ctx (Context or list of Context) – Keeps a copy of Parameters on one or many context(s).

  • verbose (bool, default False) – Whether to verbosely print out details on initialization.

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

load_parameters(filename, ctx=None, allow_missing=False, ignore_extra=False, cast_dtype=False, dtype_source='current')[source]

Load parameters from file previously saved by save_parameters.

Parameters
  • filename (str) – Path to parameter file.

  • ctx (Context or list of Context, default cpu()) – Context(s) to initialize loaded parameters on.

  • allow_missing (bool, default False) – Whether to silently skip loading parameters not represents in the file.

  • ignore_extra (bool, default False) – Whether to silently ignore parameters from the file that are not present in this Block.

  • cast_dtype (bool, default False) – Cast the data type of the NDArray loaded from the checkpoint to the dtype provided by the Parameter if any.

  • dtype_source (str, default 'current') – must be in {‘current’, ‘saved’} Only valid if cast_dtype=True, specify the source of the dtype for casting the parameters

References

Saving and Loading Gluon Models

load_params(filename, ctx=None, allow_missing=False, ignore_extra=False)[source]

[Deprecated] Please use load_parameters.

Load parameters from file.

filenamestr

Path to parameter file.

ctxContext or list of Context, default cpu()

Context(s) to initialize loaded parameters on.

allow_missingbool, default False

Whether to silently skip loading parameters not represents in the file.

ignore_extrabool, default False

Whether to silently ignore parameters from the file that are not present in this Block.

property name

Name of this Block, without ‘_’ in the end.

name_scope()[source]

Returns a name space object managing a child Block and parameter names. Should be used within a with statement:

with self.name_scope():
    self.dense = nn.Dense(20)

Please refer to the naming tutorial for more info on prefix and naming.

property params

Returns this Block’s parameter dictionary (does not include its children’s parameters).

property prefix

Prefix of this Block.

register_child(block, name=None)[source]

Registers block as a child of self. Block s assigned to self as attributes will be registered automatically.

register_forward_hook(hook)[source]

Registers a forward hook on the block.

The hook function is called immediately after forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input, output) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_forward_pre_hook(hook)[source]

Registers a forward pre-hook on the block.

The hook function is called immediately before forward(). It should not modify the input or output.

Parameters

hook (callable) – The forward hook function of form hook(block, input) -> None.

Returns

Return type

mxnet.gluon.utils.HookHandle

register_op_hook(callback, monitor_all=False)[source]

Install callback monitor.

Parameters
  • callback (function) – Takes a string and a NDArrayHandle.

  • monitor_all (bool, default False) – If true, monitor both input and output, otherwise monitor output only.

save_parameters(filename, deduplicate=False)[source]

Save parameters to file.

Saved parameters can only be loaded with load_parameters. Note that this method only saves parameters, not model structure. If you want to save model structures, please use HybridBlock.export().

Parameters
  • filename (str) – Path to file.

  • deduplicate (bool, default False) – If True, save shared parameters only once. Otherwise, if a Block contains multiple sub-blocks that share parameters, each of the shared parameters will be separately saved for every sub-block.

References

Saving and Loading Gluon Models

save_params(filename)[source]

[Deprecated] Please use save_parameters. Note that if you want load from SymbolBlock later, please use export instead.

Save parameters to file.

filenamestr

Path to file.

summary(*inputs)[source]

Print the summary of the model’s output and parameters.

The network must have been initialized, and must not have been hybridized.

Parameters

inputs (object) – Any input that the model supports. For any tensor in the input, only mxnet.ndarray.NDArray is supported.