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)
gp = get_config_arg('layer_num', int, 1)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)

args = {
    'height': height,
    'width': width,
    'color': True,
    'num_class': num_class,
    'is_infer': is_infer,
    'num_samples': num_samples
}
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=gp)
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=gp)

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

if is_infer:
    outputs(net)
else:
    lab = data_layer('label', num_class)
    loss = cross_entropy(input=net, label=lab)
    outputs(loss)