Block

class Block(prefix=None, params=None)

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.
        with self.name_scope():
            self.dense0 = nn.Dense(20)
            self.dense1 = nn.Dense(20)

    def forward(self, x):
        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. 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())