This project has retired. For details please refer to its Attic page. — mxnet documentation

Source code for

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# coding: utf-8
# pylint: disable= arguments-differ
"""Alexnet, implemented in Gluon."""
__all__ = ['AlexNet', 'alexnet']

import os

from ....context import cpu
from ...block import HybridBlock
from ... import nn

# Net
[docs]class AlexNet(HybridBlock): r"""AlexNet model from the `"One weird trick..." `_ paper. Parameters ---------- classes : int, default 1000 Number of classes for the output layer. """ def __init__(self, classes=1000, **kwargs): super(AlexNet, self).__init__(**kwargs) with self.name_scope(): self.features = nn.HybridSequential(prefix='') with self.features.name_scope(): self.features.add(nn.Conv2D(64, kernel_size=11, strides=4, padding=2, activation='relu')) self.features.add(nn.MaxPool2D(pool_size=3, strides=2)) self.features.add(nn.Conv2D(192, kernel_size=5, padding=2, activation='relu')) self.features.add(nn.MaxPool2D(pool_size=3, strides=2)) self.features.add(nn.Conv2D(384, kernel_size=3, padding=1, activation='relu')) self.features.add(nn.Conv2D(256, kernel_size=3, padding=1, activation='relu')) self.features.add(nn.Conv2D(256, kernel_size=3, padding=1, activation='relu')) self.features.add(nn.MaxPool2D(pool_size=3, strides=2)) self.features.add(nn.Flatten()) self.features.add(nn.Dense(4096, activation='relu')) self.features.add(nn.Dropout(0.5)) self.features.add(nn.Dense(4096, activation='relu')) self.features.add(nn.Dropout(0.5)) self.output = nn.Dense(classes) def hybrid_forward(self, F, x): x = self.features(x) x = self.output(x) return x
# Constructor
[docs]def alexnet(pretrained=False, ctx=cpu(), root=os.path.join('~', '.mxnet', 'models'), **kwargs): r"""AlexNet model from the `"One weird trick..." `_ paper. Parameters ---------- pretrained : bool, default False Whether to load the pretrained weights for model. ctx : Context, default CPU The context in which to load the pretrained weights. root : str, default '~/.mxnet/models' Location for keeping the model parameters. """ net = AlexNet(**kwargs) if pretrained: from ..model_store import get_model_file net.load_params(get_model_file('alexnet', root=root), ctx=ctx) return net