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alexnet.py
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import mxnet as mx
import numpy as np
#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.
"""
Reference:
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
"""
def get_symbol(num_classes, dtype='float32', **kwargs):
input_data = mx.sym.Variable(name="data")
if dtype == 'float16':
input_data = mx.sym.Cast(data=input_data, dtype=np.float16)
# stage 1
conv1 = mx.sym.Convolution(name='conv1',
data=input_data, kernel=(11, 11), stride=(4, 4), num_filter=96)
relu1 = mx.sym.Activation(data=conv1, act_type="relu")
lrn1 = mx.sym.LRN(data=relu1, alpha=0.0001, beta=0.75, knorm=2, nsize=5)
pool1 = mx.sym.Pooling(
data=lrn1, pool_type="max", kernel=(3, 3), stride=(2,2))
# stage 2
conv2 = mx.sym.Convolution(name='conv2',
data=pool1, kernel=(5, 5), pad=(2, 2), num_filter=256)
relu2 = mx.sym.Activation(data=conv2, act_type="relu")
lrn2 = mx.sym.LRN(data=relu2, alpha=0.0001, beta=0.75, knorm=2, nsize=5)
pool2 = mx.sym.Pooling(data=lrn2, kernel=(3, 3), stride=(2, 2), pool_type="max")
# stage 3
conv3 = mx.sym.Convolution(name='conv3',
data=pool2, kernel=(3, 3), pad=(1, 1), num_filter=384)
relu3 = mx.sym.Activation(data=conv3, act_type="relu")
conv4 = mx.sym.Convolution(name='conv4',
data=relu3, kernel=(3, 3), pad=(1, 1), num_filter=384)
relu4 = mx.sym.Activation(data=conv4, act_type="relu")
conv5 = mx.sym.Convolution(name='conv5',
data=relu4, kernel=(3, 3), pad=(1, 1), num_filter=256)
relu5 = mx.sym.Activation(data=conv5, act_type="relu")
pool3 = mx.sym.Pooling(data=relu5, kernel=(3, 3), stride=(2, 2), pool_type="max")
# stage 4
flatten = mx.sym.Flatten(data=pool3)
fc1 = mx.sym.FullyConnected(name='fc1', data=flatten, num_hidden=4096)
relu6 = mx.sym.Activation(data=fc1, act_type="relu")
dropout1 = mx.sym.Dropout(data=relu6, p=0.5)
# stage 5
fc2 = mx.sym.FullyConnected(name='fc2', data=dropout1, num_hidden=4096)
relu7 = mx.sym.Activation(data=fc2, act_type="relu")
dropout2 = mx.sym.Dropout(data=relu7, p=0.5)
# stage 6
fc3 = mx.sym.FullyConnected(name='fc3', data=dropout2, num_hidden=num_classes)
if dtype == 'float16':
fc3 = mx.sym.Cast(data=fc3, dtype=np.float32)
softmax = mx.sym.SoftmaxOutput(data=fc3, name='softmax')
return softmax