Example #1
0
    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()
Example #2
0
 def test_get_layer(self):
     pixel = layer.data(name='pixel2', type=data_type.dense_vector(784))
     label = layer.data(name='label2', 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())
     cost = layer.classification_cost(input=inference, label=label)
     topo = topology.Topology(cost)
     pixel_layer = topo.get_layer("pixel2")
     label_layer = topo.get_layer("label2")
     self.assertEqual(pixel_layer, pixel)
     self.assertEqual(label_layer, label)
Example #3
0
    def test_data_type(self):
        pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
        label = layer.data(name='label', 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())
        cost = layer.classification_cost(input=inference, label=label)
        topo = topology.Topology(cost)
        data_types = topo.data_type()
        self.assertEqual(len(data_types), 2)
        pixel_data_type = filter(lambda type: type[0] == "pixel", data_types)
        self.assertEqual(len(pixel_data_type), 1)
        pixel_data_type = pixel_data_type[0]
        self.assertEqual(pixel_data_type[1].type, pydp2.DataType.Dense)
        self.assertEqual(pixel_data_type[1].dim, 784)

        label_data_type = filter(lambda type: type[0] == "label", data_types)
        self.assertEqual(len(label_data_type), 1)
        label_data_type = label_data_type[0]
        self.assertEqual(label_data_type[1].type, pydp2.DataType.Index)
        self.assertEqual(label_data_type[1].dim, 10)