.conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')

AlexNet implementation in TensorFlow (Python3), with pre-trained weights (from Caffe):

Required libraries: scipy, numpy and tensorflow
Each layer was saved to a seperate .mat file
Credit to: http://www.cs.toronto.edu/~guerzhoy/tf_alexnet/
Model: https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet

Layers:

.conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')

.lrn(2, 2e-05, 0.75, name='norm1')

.max_pool(3, 3, 2, 2, padding='VALID', name='pool1')

.conv(5, 5, 256, 1, 1, group=2, name='conv2')

.lrn(2, 2e-05, 0.75, name='norm2')

.max_pool(3, 3, 2, 2, padding='VALID', name='pool2')

.conv(3, 3, 384, 1, 1, name='conv3')

.conv(3, 3, 384, 1, 1, group=2, name='conv4')

.conv(3, 3, 256, 1, 1, group=2, name='conv5')

.fc(4096, name='fc6')

.fc(4096, name='fc7')

.fc(1000, relu=False, name='fc8')

.softmax(name='prob'))

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
README.md		README.md
caffe_classes.py		caffe_classes.py
conv1.mat		conv1.mat
conv2.mat		conv2.mat
conv3.mat		conv3.mat
conv4.mat		conv4.mat
conv5.mat		conv5.mat
fc6.mat		fc6.mat
fc7.mat		fc7.mat
fc8.mat		fc8.mat
input.mat		input.mat
lrn1.mat		lrn1.mat
lrn2.mat		lrn2.mat
maxpool1.mat		maxpool1.mat
maxpool2.mat		maxpool2.mat
maxpool5.mat		maxpool5.mat
myalexnet_forward.py		myalexnet_forward.py
poodle.png		poodle.png
prob.mat		prob.mat

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.conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')

.lrn(2, 2e-05, 0.75, name='norm1')

.max_pool(3, 3, 2, 2, padding='VALID', name='pool1')

.conv(5, 5, 256, 1, 1, group=2, name='conv2')

.lrn(2, 2e-05, 0.75, name='norm2')

.max_pool(3, 3, 2, 2, padding='VALID', name='pool2')

.conv(3, 3, 384, 1, 1, name='conv3')

.conv(3, 3, 384, 1, 1, group=2, name='conv4')

.conv(3, 3, 256, 1, 1, group=2, name='conv5')

.fc(4096, name='fc6')

.fc(4096, name='fc7')

.fc(1000, relu=False, name='fc8')

.softmax(name='prob'))

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.conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')

.lrn(2, 2e-05, 0.75, name='norm1')

.max_pool(3, 3, 2, 2, padding='VALID', name='pool1')

.conv(5, 5, 256, 1, 1, group=2, name='conv2')

.lrn(2, 2e-05, 0.75, name='norm2')

.max_pool(3, 3, 2, 2, padding='VALID', name='pool2')

.conv(3, 3, 384, 1, 1, name='conv3')

.conv(3, 3, 384, 1, 1, group=2, name='conv4')

.conv(3, 3, 256, 1, 1, group=2, name='conv5')

.fc(4096, name='fc6')

.fc(4096, name='fc7')

.fc(1000, relu=False, name='fc8')

.softmax(name='prob'))

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