def test_parse(self):
pixel = layer.data(name='pixel3', type=data_type.dense_vector(784))
label = layer.data(name='label3', type=data_type.integer_value(10))
hidden = layer.fc(input=pixel,
size=100,
act=conf_helps.SigmoidActivation())
inference = layer.fc(input=hidden,
size=10,
act=conf_helps.SoftmaxActivation())
maxid = layer.max_id(input=inference)
cost1 = layer.classification_cost(input=inference, label=label)
cost2 = layer.cross_entropy_cost(input=inference, label=label)
topology.Topology(cost2).proto()
topology.Topology([cost1]).proto()
topology.Topology([cost1, cost2]).proto()
topology.Topology([inference, maxid]).proto()
def test_sampling_layer(self):
maxid = layer.max_id(input=inference)
sampling_id = layer.sampling_id(input=inference)
eos = layer.eos(input=maxid, eos_id=5)
print layer.parse_network(maxid, sampling_id, eos)
def test_sampling_layer(self):
maxid = layer.max_id(input=inference)
sampling_id = layer.sampling_id(input=inference)
eos = layer.eos(input=maxid, eos_id=5)
layer.printer(maxid)
print layer.parse_network([maxid, sampling_id, eos])