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_operator(self):
ipt0 = layer.data(name='data1', type=data_type.dense_vector(784))
ipt1 = layer.data(name='word1', 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)