def build_model(l_in=None):
l_in = nn.layers.InputLayer((
None,
p_transform['channels'],
) + p_transform['patch_size']) if l_in is None else l_in
l_target = nn.layers.InputLayer((None, p_transform['n_labels']))
l_feat_cube = build_densenet(l_in,
input_var=None,
first_output=64,
growth_rate=32,
num_blocks=4,
dropout=0)
l_d = drop(l_feat_cube)
l_feat = GlobalPoolLayer(l_d, name='post_pool')
l = nn.layers.DenseLayer(l_feat,
num_units=p_transform['n_labels'],
W=nn.init.HeNormal(gain=1),
nonlinearity=nn.nonlinearities.identity)
l_weather = nn.layers.SliceLayer(l, indices=slice(0, 4), axis=-1)
l_weather = nn.layers.NonlinearityLayer(
l_weather, nonlinearity=nn.nonlinearities.softmax)
l_other = nn_planet.MajorExclusivityLayer(l, idx_major=0)
l_other = nn.layers.SliceLayer(l_other, indices=slice(4, None), axis=-1)
l_out = nn.layers.ConcatLayer([l_weather, l_other], axis=-1)
return namedtuple('Model',
['l_in', 'l_out', 'l_target', 'l_feat'])(l_in, l_out,
l_target,
l_feat)
def build_model(l_in=None):
l_in = nn.layers.InputLayer((None, p_transform['channels'],) + p_transform['patch_size']) if l_in is None else l_in
l_target = nn.layers.InputLayer((None,p_transform['n_labels']))
l = conv(l_in, 64)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l_1q = nn.layers.SliceLayer(l, indices=slice(0,l.output_shape[1]/4), axis=1)
l_2q = nn.layers.SliceLayer(l, indices=slice(l.output_shape[1]/4,l.output_shape[1]/2),axis=1)
l_3q = nn.layers.SliceLayer(l, indices=slice(l.output_shape[1]/2,l.output_shape[1]*3/4),axis=1)
l_4q = nn.layers.SliceLayer(l, indices=slice(l.output_shape[1]*3/4,l.output_shape[1]),axis=1)
l_1q = drop(l_1q, p=0.75)
l_1q = nn.layers.GlobalPoolLayer(l_1q, pool_function=T.mean)
l_2q = nn.layers.GlobalPoolLayer(l_2q, pool_function=T.max)
l_2q = drop(l_2q, p=0.75)
l_3q = drop(l_3q, p=0.75)
l_3q = nn.layers.GlobalPoolLayer(l_3q, pool_function=T.var)
l_4q = nn.layers.FeaturePoolLayer(l_4q, pool_size=64, pool_function=T.max)
l_4q = nn.layers.flatten(l_4q)
l_4q = drop(l_4q, p=0.75)
l_feat = nn.layers.ConcatLayer([l_1q, l_2q, l_3q, l_4q])
l = nn.layers.DenseLayer(l_feat, num_units=p_transform['n_labels'],
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.5),
nonlinearity=nn.nonlinearities.identity)
l_weather = nn.layers.SliceLayer(l, indices=slice(0,4), axis=-1)
l_weather = nn.layers.NonlinearityLayer(l_weather, nonlinearity=nn.nonlinearities.softmax)
l_other = nn_planet.MajorExclusivityLayer(l, idx_major=0)
l_other = nn.layers.SliceLayer(l_other, indices=slice(4,None), axis=-1)
l_out = nn.layers.ConcatLayer([l_weather, l_other], axis=-1)
return namedtuple('Model', ['l_in', 'l_out', 'l_target', 'l_feat'])(l_in, l_out, l_target, l_feat)
def build_model(l_in=None):
l_in = nn.layers.InputLayer((None, p_transform['channels'],) + p_transform['patch_size']) if l_in is None else l_in
l_target = nn.layers.InputLayer((None,p_transform['n_labels']))
l = conv(l_in, 64)
l = inrn_v2_red(l)
b1 = inrn_v2(l)
l = inrn_v2_red(b1)
b2 = inrn_v2(l)
l = inrn_v2_red(b2)
b3 = inrn_v2(l)
l = inrn_v2_red(b3)
b4 = inrn_v2(l)
l = inrn_v2_red(b4)
b5 = inrn_v2(l)
l = drop(b5)
l = feat_red(l)
l = nn.layers.GlobalPoolLayer(l)
spp_b1 = nn.layers.FlattenLayer(max_pool(c1(b1,16), pool_size=32))
spp_b2 = nn.layers.FlattenLayer(max_pool(c1(b2,16), pool_size=16))
spp_b3 = nn.layers.FlattenLayer(max_pool(c1(b3,16), pool_size=8))
spp_b4 = nn.layers.FlattenLayer(max_pool(c1(b4,16), pool_size=4))
print 'l', l.output_shape
print 'spp_b1', spp_b1.output_shape
print 'spp_b2', spp_b2.output_shape
print 'spp_b3', spp_b3.output_shape
print 'spp_b4', spp_b4.output_shape
l_feat = lasagne.layers.ConcatLayer([l, spp_b1, spp_b2, spp_b3, spp_b4])
l = nn.layers.DenseLayer(l_feat, num_units=p_transform['n_labels'],
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.5),
nonlinearity=nn.nonlinearities.identity)
l_weather = nn.layers.SliceLayer(l, indices=slice(0,4), axis=-1)
l_weather = nn.layers.NonlinearityLayer(l_weather, nonlinearity=nn.nonlinearities.softmax)
l_other = nn_planet.MajorExclusivityLayer(l, idx_major=0)
l_other = nn.layers.SliceLayer(l_other, indices=slice(4,None), axis=-1)
l_out = nn.layers.ConcatLayer([l_weather, l_other], axis=-1)
return namedtuple('Model', ['l_in', 'l_out', 'l_target', 'l_feat'])(l_in, l_out, l_target, l_feat)
def build_model(l_in=None):
l_in = nn.layers.InputLayer((
None,
p_transform['channels'],
) + p_transform['patch_size']) if l_in is None else l_in
l_target = nn.layers.InputLayer((None, p_transform['n_labels']))
l = conv(l_in, 64)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = inrn_v2(l)
l = inrn_v2_red(l)
l = drop(l, p=0.3)
l = inrn_v2(l)
l = drop(l, p=0.5)
l_feat = nn.layers.GlobalPoolLayer(l)
l = nn.layers.DenseLayer(l_feat,
num_units=p_transform['n_labels'],
W=nn.init.Orthogonal(),
b=nn.init.Constant(0.5),
nonlinearity=nn.nonlinearities.identity)
l_weather = nn.layers.SliceLayer(l, indices=slice(0, 4), axis=-1)
l_weather = nn.layers.NonlinearityLayer(
l_weather, nonlinearity=nn.nonlinearities.softmax)
l_other = nn_planet.MajorExclusivityLayer(l, idx_major=0)
l_other = nn.layers.SliceLayer(l_other, indices=slice(4, None), axis=-1)
l_out = nn.layers.ConcatLayer([l_weather, l_other], axis=-1)
return namedtuple('Model',
['l_in', 'l_out', 'l_target', 'l_feat'])(l_in, l_out,
l_target,
l_feat)
