Source code for mxnet.gluon.model_zoo.vision.alexnet

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

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

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.classifier = nn.HybridSequential(prefix='') with self.classifier.name_scope(): self.classifier.add(nn.Dense(4096, activation='relu')) self.classifier.add(nn.Dropout(0.5)) self.classifier.add(nn.Dense(4096, activation='relu')) self.classifier.add(nn.Dropout(0.5)) self.classifier.add(nn.Dense(classes)) def hybrid_forward(self, F, x): x = self.features(x) x = self.classifier(x) return x
# Constructor
[docs]def alexnet(pretrained=False, ctx=cpu(), root='~/.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