You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
71 lines
2.2 KiB
71 lines
2.2 KiB
from paddle.trainer_config_helpers import *
|
|
|
|
is_predict = get_config_arg("is_predict", bool, False)
|
|
|
|
####################Data Configuration ##################
|
|
|
|
if not is_predict:
|
|
data_dir = './data/'
|
|
define_py_data_sources2(
|
|
train_list=data_dir + 'train.list',
|
|
test_list=data_dir + 'test.list',
|
|
module='mnist_provider',
|
|
obj='process')
|
|
|
|
######################Algorithm Configuration #############
|
|
# settings(
|
|
# batch_size=128,
|
|
# learning_rate=0.1 / 128.0,
|
|
# learning_method=MomentumOptimizer(0.9),
|
|
# regularization=L2Regularization(0.0005 * 128))
|
|
settings(batch_size=50, learning_rate=0.001, learning_method=AdamOptimizer())
|
|
|
|
#######################Network Configuration #############
|
|
|
|
data_size = 1 * 28 * 28
|
|
label_size = 10
|
|
img = data_layer(name='pixel', size=data_size)
|
|
|
|
# small_vgg is predined in trainer_config_helpers.network
|
|
# predict = small_vgg(input_image=img, num_channels=1, num_classes=label_size)
|
|
|
|
|
|
# light cnn
|
|
def light_cnn(input_image, num_channels, num_classes):
|
|
def __light__(ipt,
|
|
num_filter=128,
|
|
times=1,
|
|
conv_filter_size=3,
|
|
dropouts=0,
|
|
num_channels_=None):
|
|
return img_conv_group(
|
|
input=ipt,
|
|
num_channels=num_channels_,
|
|
pool_size=2,
|
|
pool_stride=2,
|
|
conv_padding=0,
|
|
conv_num_filter=[num_filter] * times,
|
|
conv_filter_size=conv_filter_size,
|
|
conv_act=ReluActivation(),
|
|
conv_with_batchnorm=True,
|
|
conv_batchnorm_drop_rate=dropouts,
|
|
pool_type=MaxPooling())
|
|
|
|
tmp = __light__(input_image, num_filter=128, num_channels_=num_channels)
|
|
tmp = __light__(tmp, num_filter=128)
|
|
tmp = __light__(tmp, num_filter=128)
|
|
tmp = __light__(tmp, num_filter=128, conv_filter_size=1)
|
|
|
|
tmp = fc_layer(input=tmp, size=num_classes, act=SoftmaxActivation())
|
|
return tmp
|
|
|
|
|
|
predict = light_cnn(input_image=img, num_channels=1, num_classes=label_size)
|
|
|
|
if not is_predict:
|
|
lbl = data_layer(name="label", size=label_size)
|
|
inputs(img, lbl)
|
|
outputs(classification_cost(input=predict, label=lbl))
|
|
else:
|
|
outputs(predict)
|