smallnet_mnist_cifar.py

#!/usr/bin/env python

from paddle.trainer_config_helpers import *

height=32
width=32
num_class = 10

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=5, num_channels=3,
                     num_filters=32, stride=1, padding=2)
net = img_pool_layer(input=net, pool_size=3, stride=2, padding=1)

# conv2
net = img_conv_layer(input=net, filter_size=5, num_filters=32,
                     stride=1, padding=2)
net = img_pool_layer(input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())

# conv3
net = img_conv_layer(input=net, filter_size=3, num_filters=64,
                     stride=1, padding=1)
net = img_pool_layer(input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())

net = fc_layer(input=net, size=64, act=ReluActivation())
net = fc_layer(input=net, size=10, act=SoftmaxActivation())

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