NAME

AI::MXNet::Gluon::Block - Base class for all neural network layers and models.

DESCRIPTION

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::

    from mxnet.gluon import Block, nn
    from mxnet import ndarray as F

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

            x = F.relu(self.dense0(x))
            return F.relu(self.dense1(x))

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


Child `Block` assigned this way will be registered and `collect_params`
will collect their Parameters recursively.

Parameters
----------
prefix : str
    Prefix acts like a name space. It will be prepended to the names of all
    Parameters and child `Block`s in this `Block`'s `name_scope`. Prefix
    should be unique within one model to prevent name collisions.
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())

params

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

collect_params

Returns a `ParameterDict` containing this `Block` and all of its
children's Parameters.

save

Save parameters to file.

filename : str
    Path to file.

load

Load parameters from file.

$filename : str
    Path to parameter file.
:$ctx= : Context or list of Context
    Context(s) 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.

register_child

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

initialize

Initializes `Parameter`s of this `Block` and its children.

Equivalent to `block.collect_params().initialize(...)`

hybridize

Activates or deactivates `HybridBlock`s recursively. Has no effect on
non-hybrid children.

Parameters
----------
active : bool, default True
    Whether to turn hybrid on or off.

forward

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

Parameters
----------
@args : array of NDArray
    Input tensors.

NAME

AI::MXNet::Gluon::HybridBlock

DESCRIPTION

`HybridBlock` supports forwarding with both Symbol and NDArray.

Forward computation in `HybridBlock` must be static to work with `Symbol`s,
i.e. you cannot call `.asnumpy()`, `.shape`, `.dtype`, etc on tensors.
Also, you cannot use branching or loop logic that bases on non-constant
expressions like random numbers or intermediate results, since they change
the graph structure for each iteration.

Before activating with `hybridize()`, `HybridBlock` works just like normal
`Block`. After activation, `HybridBlock` will create a symbolic graph
representing the forward computation and cache it. On subsequent forwards,
the cached graph will be used instead of `hybrid_forward`.

Refer `Hybrid tutorial <http://mxnet.io/tutorials/gluon/hybrid.html>`_ to see
the end-to-end usage.

infer_shape

Infers shape of Parameters from inputs.

forward

Defines the forward computation. Arguments can be either
`NDArray` or `Symbol`.

hybrid_forward

Overrides to construct symbolic graph for this `Block`.

Parameters
----------
x : Symbol or NDArray
    The first input tensor.
*args : list of Symbol or list of NDArray
    Additional input tensors.

NAME

AI::MXNet::Gluon::SymbolBlock - Construct block from symbol.

DESCRIPTION

Construct block from symbol. This is useful for using pre-trained models
as feature extractors. For example, you may want to extract get the output
from fc2 layer in AlexNet.

Parameters
----------
outputs : Symbol or list of Symbol
    The desired output for SymbolBlock.
inputs : Symbol or list of Symbol
    The Variables in output's argument that should be used as inputs.
params : ParameterDict
    Parameter dictionary for arguments and auxililary states of outputs
    that are not inputs.

Examples
--------
>>> # To extract the feature from fc1 and fc2 layers of AlexNet:
>>> alexnet = gluon.model_zoo.vision.alexnet(pretrained=True, ctx=mx.cpu(),
                                             prefix='model_')
>>> inputs = mx.sym.var('data')
>>> out = alexnet(inputs)
>>> internals = out.get_internals()
>>> print(internals.list_outputs())
['data', ..., 'model_dense0_relu_fwd_output', ..., 'model_dense1_relu_fwd_output', ...]
>>> outputs = [internals['model_dense0_relu_fwd_output'],
               internals['model_dense1_relu_fwd_output']]
>>> # Create SymbolBlock that shares parameters with alexnet
>>> feat_model = gluon.SymbolBlock(outputs, inputs, params=alexnet.collect_params())
>>> x = mx.nd.random_normal(shape=(16, 3, 224, 224))
>>> print(feat_model(x))