def test_operator(self):
ipt0 = layer.data(name='data', type=data_type.dense_vector(784))
ipt1 = layer.data(name='word', type=data_type.dense_vector(128))
fc0 = layer.fc(input=ipt0, size=100, act=activation.Sigmoid())
fc1 = layer.fc(input=ipt0, size=100, act=activation.Sigmoid())
dotmul_op = layer.dotmul_operator(a=fc0, b=fc1)
dotmul0 = layer.mixed(input=dotmul_op)
with layer.mixed() as dotmul1:
dotmul1 += dotmul_op
conv = layer.conv_operator(img=ipt0,
filter=ipt1,
filter_size=1,
num_channels=1,
num_filters=128,
stride=1,
padding=0)
conv0 = layer.mixed(input=conv)
with layer.mixed() as conv1:
conv1 += conv
print layer.parse_network(dotmul0)
print layer.parse_network(dotmul1)
print layer.parse_network(conv0)
print layer.parse_network(conv1)
def test_projection(self):
input = layer.data(name='data', type=data_type.dense_vector(784))
word = layer.data(
name='word', type=data_type.integer_value_sequence(10000))
fc0 = layer.fc(input=input, size=100, act=activation.Sigmoid())
fc1 = layer.fc(input=input, size=200, act=activation.Sigmoid())
mixed0 = layer.mixed(
size=256,
input=[
layer.full_matrix_projection(input=fc0),
layer.full_matrix_projection(input=fc1)
])
with layer.mixed(size=200) as mixed1:
mixed1 += layer.full_matrix_projection(input=fc0)
mixed1 += layer.identity_projection(input=fc1)
table = layer.table_projection(input=word)
emb0 = layer.mixed(size=512, input=table)
with layer.mixed(size=512) as emb1:
emb1 += table
scale = layer.scaling_projection(input=fc0)
scale0 = layer.mixed(size=100, input=scale)
with layer.mixed(size=100) as scale1:
scale1 += scale
dotmul = layer.dotmul_projection(input=fc0)
dotmul0 = layer.mixed(size=100, input=dotmul)
with layer.mixed(size=100) as dotmul1:
dotmul1 += dotmul
context = layer.context_projection(input=fc0, context_len=5)
context0 = layer.mixed(size=100, input=context)
with layer.mixed(size=100) as context1:
context1 += context
conv = layer.conv_projection(
input=input,
filter_size=1,
num_channels=1,
num_filters=128,
stride=1,
padding=0)
conv0 = layer.mixed(input=conv, bias_attr=True)
with layer.mixed(bias_attr=True) as conv1:
conv1 += conv
print layer.parse_network(mixed0)
print layer.parse_network(mixed1)
print layer.parse_network(emb0)
print layer.parse_network(emb1)
print layer.parse_network(scale0)
print layer.parse_network(scale1)
print layer.parse_network(dotmul0)
print layer.parse_network(dotmul1)
print layer.parse_network(conv0)
print layer.parse_network(conv1)
import paddle.v2.attr as attr
import paddle.v2.data_type as data_type
import paddle.v2.layer as layer
import paddle.v2.pooling as pooling
import paddle.v2.networks as networks
pixel = layer.data(name='pixel', type=data_type.dense_vector(128))
label = layer.data(name='label', type=data_type.integer_value(10))
weight = layer.data(name='weight', type=data_type.dense_vector(1))
combine_weight = layer.data(name='weight_combine',
type=data_type.dense_vector(10))
score = layer.data(name='score', type=data_type.dense_vector(1))
hidden = layer.fc(input=pixel,
size=100,
act=activation.Sigmoid(),
param_attr=attr.Param(name='hidden'))
inference = layer.fc(input=hidden, size=10, act=activation.Softmax())
conv = layer.img_conv(input=pixel,
filter_size=1,
filter_size_y=1,
num_channels=8,
num_filters=16,
act=activation.Linear())
class ImageLayerTest(unittest.TestCase):
def test_conv_layer(self):
conv_shift = layer.conv_shift(a=pixel, b=score)
print layer.parse_network(conv, conv_shift)