alexnet.py

#!/usr/bin/env python

from paddle.trainer_config_helpers import *

height=227
width=227
num_class = 1000
batch_size = get_config_arg('batch_size', int, 128) 

args={'height':height, 'width':width, 'color':True, 'num_class':num_class}
define_py_data_sources2("train.list",
                        None,
                        module="provider",
                        obj="process",
                        args=args)


settings(
    batch_size = batch_size,
    learning_rate = 0.01 / batch_size,
    learning_method = MomentumOptimizer(0.9),
    regularization = L2Regularization(0.0005 * batch_size)
)


# conv1
net = data_layer('data', size=height * width * 3)
net = img_conv_layer(input=net, filter_size=11, num_channels=3,
      num_filters=96, stride=4, padding=1)
net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
net = img_pool_layer(input=net, pool_size=3, stride=2) 

# conv2
net = img_conv_layer(input=net, filter_size=5, num_filters=256,
      stride=1, padding=2, groups=1)
net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
net = img_pool_layer(input=net, pool_size=3, stride=2)

# conv3
net = img_conv_layer(input=net, filter_size=3, num_filters=384,
      stride=1, padding=1)
# conv4
net = img_conv_layer(input=net, filter_size=3, num_filters=384,
      stride=1, padding=1, groups=1)

# conv5
net = img_conv_layer(input=net, filter_size=3, num_filters=256,
      stride=1, padding=1, groups=1)
net = img_pool_layer(input=net, pool_size=3, stride=2)

net = fc_layer(input=net, size=4096, act=ReluActivation(), layer_attr=ExtraAttr(drop_rate=0.5))
net = fc_layer(input=net, size=4096, act=ReluActivation(), layer_attr=ExtraAttr(drop_rate=0.5))
net = fc_layer(input=net, size=1000, act=SoftmaxActivation())

lab = data_layer('label', num_class)
loss = cross_entropy(input=net, label=lab) 
outputs(loss)