def create_net(use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
W0_specs = {'init': 'gaussian', 'mean': 0, 'std': 0.0001}
W1_specs = {'init': 'gaussian', 'mean': 0, 'std': 0.01}
W2_specs = {'init': 'gaussian', 'mean': 0, 'std': 0.01, 'decay_mult': 250}
b_specs = {'init': 'constant', 'value': 0, 'lr_mult': 2, 'decay_mult': 0}
net.add(
layer.Conv2D('conv1',
32,
5,
1,
W_specs=W0_specs.copy(),
b_specs=b_specs.copy(),
pad=2,
input_sample_shape=(
3,
32,
32,
)))
net.add(layer.MaxPooling2D('pool1', 3, 2, pad=1))
net.add(layer.Activation('relu1'))
net.add(layer.LRN(name='lrn1', size=3, alpha=5e-5))
net.add(
layer.Conv2D('conv2',
32,
5,
1,
W_specs=W1_specs.copy(),
b_specs=b_specs.copy(),
pad=2))
net.add(layer.Activation('relu2'))
net.add(layer.AvgPooling2D('pool2', 3, 2, pad=1))
net.add(layer.LRN('lrn2', size=3, alpha=5e-5))
net.add(
layer.Conv2D('conv3',
64,
5,
1,
W_specs=W1_specs.copy(),
b_specs=b_specs.copy(),
pad=2))
net.add(layer.Activation('relu3'))
net.add(layer.AvgPooling2D('pool3', 3, 2, pad=1))
net.add(layer.Flatten('flat'))
net.add(
layer.Dense('dense',
10,
W_specs=W2_specs.copy(),
b_specs=b_specs.copy()))
for (p, specs) in zip(net.param_values(), net.param_specs()):
filler = specs.filler
if filler.type == 'gaussian':
p.gaussian(filler.mean, filler.std)
else:
p.set_value(0)
print specs.name, filler.type, p.l1()
return net
def test_lrn(self):
in_sample_shape = (3, 224, 224)
lrn = layer.LRN('lrn', input_sample_shape=in_sample_shape)
out_sample_shape = lrn.get_output_sample_shape()
self.check_shape(out_sample_shape, in_sample_shape)