def build_model():
l0 = nn.layers.InputLayer((batch_size, data.num_classes))
l0_size = nn.layers.InputLayer((batch_size, 7))
l1_size = nn.layers.DenseLayer(l0_size,
num_units=80,
W=nn_plankton.Orthogonal('relu'),
b=nn.init.Constant(0.1))
l2_size = nn.layers.DenseLayer(l1_size,
num_units=80,
W=nn_plankton.Orthogonal('relu'),
b=nn.init.Constant(0.1))
l3_size = nn.layers.DenseLayer(l2_size,
num_units=data.num_classes,
W=nn_plankton.Orthogonal(),
b=nn.init.Constant(0.1),
nonlinearity=None)
l1 = nn_plankton.NonlinLayer(l0, T.log)
ltot = nn.layers.ElemwiseSumLayer([l1, l3_size])
# norm_by_sum = lambda x: x / x.sum(1).dimshuffle(0, "x")
lout = nn_plankton.NonlinLayer(ltot, nonlinearity=T.nnet.softmax)
return [l0, l0_size], lout
def build_model():
l0 = nn.layers.InputLayer((batch_size, 1, patch_size[0], patch_size[1]))
l0c = dihedral.CyclicSliceLayer(l0)
l1 = convroll(conv(l0c, num_filters=16, filter_size=(7, 7),
strides=(2, 2)))
l2 = convroll(conv(l1, num_filters=32, filter_size=(7, 7), strides=(2, 2)))
l3a = convroll(conv(l2, num_filters=32, filter_size=(3, 3)))
l3b = convroll(conv(l3a, num_filters=32, filter_size=(3, 3)))
l3c = convroll(conv(l3b, num_filters=32, filter_size=(3, 3)))
l3d = conv(l3c, num_filters=64, filter_size=(3, 3))
l3 = convroll(pool(l3d))
l4a = convroll(conv(l3, num_filters=64, filter_size=(3, 3)))
l4b = convroll(conv(l4a, num_filters=64, filter_size=(3, 3)))
l4c = convroll(conv(l4b, num_filters=64, filter_size=(3, 3)))
l4d = conv(l4c, num_filters=128, filter_size=(3, 3))
l4 = convroll(pool(l4d))
l5a = convroll(conv(l4, num_filters=64, filter_size=(3, 3)))
l5b = convroll(conv(l5a, num_filters=64, filter_size=(3, 3)))
l5c = convroll(conv(l5b, num_filters=64, filter_size=(3, 3)))
l5d = conv(l5c, num_filters=128, filter_size=(3, 3))
l5 = convroll(pool(l5d))
l5f = nn.layers.flatten(l5)
l6 = nn.layers.DenseLayer(nn.layers.dropout(l5f, p=0.5),
num_units=256,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
l6r = dihedral_fast.CyclicRollLayer(l6)
l7 = nn.layers.DenseLayer(nn.layers.dropout(l6r, p=0.5),
num_units=256,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
l7m = dihedral.CyclicPoolLayer(l7, pool_function=nn_plankton.rms)
l8 = nn.layers.DenseLayer(nn.layers.dropout(l7m, p=0.5),
num_units=data.num_classes,
nonlinearity=T.nnet.softmax,
W=nn_plankton.Orthogonal(1.0))
return [l0], l8
def __init__(self, classes = 20):
self.output_dim = classes
self.batch_size = 128
#layers
self.l_in = nn.layers.InputLayer(shape=(self.batch_size, 3, 300, 300))
self.C1 = Conv2DLayer(self.l_in, num_filters=32, filter_size=(3, 3), border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
self.C2 = Conv2DLayer(self.C1, num_filters=16, filter_size=(3, 3), border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
self.M1 = MaxPool2DLayer(self.C2, ds=(3, 3), strides=(2, 2))
self.D1 = nn.layers.DropoutLayer(self.M1, p=0.5)
self.C3 = Conv2DLayer(self.D1, num_filters=32, filter_size=(3, 3), border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
self.C4 = Conv2DLayer(self.C3, num_filters=16, filter_size=(3, 3), border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
self.M2 = MaxPool2DLayer(self.C4, ds=(3, 3), strides=(2, 2))
self.D2 = nn.layers.DropoutLayer(self.M2, p=0.5)
self.C5 = Conv2DLayer(self.D2, num_filters=32, filter_size=(3, 3), border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
self.C6 = Conv2DLayer(self.C5, num_filters=16, filter_size=(3, 3), border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
self.M3 = MaxPool2DLayer(self.C6, ds=(3, 3), strides=(2, 2))
self.D3 = nn.layers.DropoutLayer(self.M3, p=0.5)
self.FC1 = nn.layers.DenseLayer(self.D3, num_units=256, W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
self.D4 = nn.layers.DropoutLayer(self.FC1, p=0.5)
self.FC2 = nn.layers.DenseLayer(self.D4, num_units=256, W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
self.output = nn.layers.DenseLayer(self.FC2, num_units=self.output_dim, W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1), nonlinearity=nn.nonlinearities.softmax)
self.objective = nn.objectives.Objective(self.output,
loss_function=nn.objectives.categorical_crossentropy)
self.loss = self.objective.get_loss()#self.x, target=self.y)
self.probabilities = self.output.get_output(self.l_in.input_var, deterministic=True)
self.pred = T.argmax(self.output.get_output(self.l_in.input_var, deterministic=True), axis=1)
self.errors = T.mean(T.eq(self.pred, self.objective.target_var), dtype=theano.config.floatX)
#get weights
self.all_params = nn.layers.get_all_params(self.output)
def build_model():
l0 = nn.layers.InputLayer((batch_size, 1, patch_size[0], patch_size[1]))
l0c = dihedral.CyclicSliceLayer(l0)
l1a = Conv2DLayer(l0c, num_filters=32, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
l1b = Conv2DLayer(l1a, num_filters=16, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
l1 = MaxPool2DLayer(l1b, ds=(3, 3), strides=(2, 2))
l1r = dihedral.CyclicConvRollLayer(l1)
l2a = Conv2DLayer(l1r, num_filters=64, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
l2b = Conv2DLayer(l2a, num_filters=32, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
l2 = MaxPool2DLayer(l2b, ds=(3, 3), strides=(2, 2))
l2r = dihedral.CyclicConvRollLayer(l2)
l3a = Conv2DLayer(l2r, num_filters=128, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l3b = Conv2DLayer(l3a, num_filters=128, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l3c = Conv2DLayer(l3b, num_filters=64, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l3 = MaxPool2DLayer(l3c, ds=(3, 3), strides=(2, 2))
l3r = dihedral.CyclicConvRollLayer(l3)
l4a = Conv2DLayer(l3r, num_filters=256, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l4b = Conv2DLayer(l4a, num_filters=256, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l4c = Conv2DLayer(l4b, num_filters=128, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l4 = MaxPool2DLayer(l4c, ds=(3, 3), strides=(2, 2))
l4r = dihedral.CyclicConvRollLayer(l4)
l5a = Conv2DLayer(l4r, num_filters=256, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l5b = Conv2DLayer(l5a, num_filters=256, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l5c = Conv2DLayer(l5b, num_filters=128, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l5 = MaxPool2DLayer(l5c, ds=(3, 3), strides=(2, 2))
l5r = dihedral.CyclicConvRollLayer(l5)
l5f = nn.layers.flatten(l5r)
l6 = nn.layers.DenseLayer(nn.layers.dropout(l5f, p=0.5), num_units=256, W=nn_plankton.Orthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
l6r = dihedral.CyclicRollLayer(l6)
l7 = nn.layers.DenseLayer(nn.layers.dropout(l6r, p=0.5), num_units=256, W=nn_plankton.Orthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
l7m = dihedral.CyclicPoolLayer(l7, pool_function=nn_plankton.rms)
l8 = nn.layers.DenseLayer(nn.layers.dropout(l7m, p=0.5), num_units=data.num_classes, nonlinearity=T.nnet.softmax, W=nn_plankton.Orthogonal(1.0))
l_resume = l2
l_exclude = l2
return [l0], l8, l_resume, l_exclude
def build_model():
l0 = nn.layers.InputLayer((batch_size, 1, patch_sizes[0][0], patch_sizes[0][1]))
l0_45 = nn.layers.InputLayer((batch_size, 1, patch_sizes[1][0], patch_sizes[1][1]))
l0_both = nn.layers.concat([l0, l0_45], axis=0) # stack both
l0c = dihedral.CyclicSliceLayer(l0_both)
l1a = Conv2DLayer(l0c, num_filters=32, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l1b = Conv2DLayer(l1a, num_filters=16, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l1 = MaxPool2DLayer(l1b, ds=(3, 3), strides=(2, 2))
l1r = dihedral_fast.CyclicConvRollLayer(l1)
l2a = Conv2DLayer(l1r, num_filters=64, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l2b = Conv2DLayer(l2a, num_filters=32, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l2 = MaxPool2DLayer(l2b, ds=(3, 3), strides=(2, 2))
l2r = dihedral_fast.CyclicConvRollLayer(l2)
l3a = Conv2DLayer(l2r, num_filters=128, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l3b = Conv2DLayer(l3a, num_filters=128, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l3c = Conv2DLayer(l3b, num_filters=64, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l3 = MaxPool2DLayer(l3c, ds=(3, 3), strides=(2, 2))
l3r = dihedral_fast.CyclicConvRollLayer(l3)
l4a = Conv2DLayer(l3r, num_filters=256, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l4b = Conv2DLayer(l4a, num_filters=256, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l4c = Conv2DLayer(l4b, num_filters=128, filter_size=(3, 3), border_mode="same", W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu, untie_biases=True)
l4 = MaxPool2DLayer(l4c, ds=(3, 3), strides=(2, 2))
l4r = dihedral_fast.CyclicConvRollLayer(l4)
l4f = nn.layers.flatten(l4r)
l5 = nn.layers.DenseLayer(nn.layers.dropout(l4f, p=0.5), num_units=1024, W=nn_plankton.Orthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
l5fp = nn.layers.FeaturePoolLayer(l5, ds=2)
l5m = dihedral.DihedralPoolLayer(l5fp, pool_function=nn_plankton.rms) # reusing the dihedral pool layer here for 8-way cyclic pooling. Ew!
l6 = nn.layers.DenseLayer(nn.layers.dropout(l5m, p=0.5), num_units=1024, W=nn_plankton.Orthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
l6fp = nn.layers.FeaturePoolLayer(l6, ds=2)
l7 = nn.layers.DenseLayer(nn.layers.dropout(l6fp, p=0.5), num_units=data.num_classes, nonlinearity=T.nnet.softmax, W=nn_plankton.Orthogonal(1.0))
return [l0, l0_45], l7
def __init__(self, classes=212):
self.output_dim = classes
self.batch_size = 128
#layers using amazon instance with GRID K520
self.l_in = nn.layers.InputLayer(shape=(self.batch_size, 3, 100, 100))
#self.C1 = nn.layers.Conv2DLayer(self.l_in, num_filters=64, filter_size=(3, 3), border_mode="same",
# W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
#self.C2 = nn.layers.Conv2DLayer(self.C1, num_filters=64, filter_size=(3, 3), border_mode="same",
# W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
#self.M1 = nn.layers.pool.MaxPool2DLayer(self.C2, pool_size=3, stride=2)
##self.D1 = nn.layers.DropoutLayer(self.M1, p=0.5)
#self.C3 = nn.layers.Conv2DLayer(self.M1, num_filters=128, filter_size=(3, 3), border_mode="same",
# W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
#self.C4 = nn.layers.Conv2DLayer(self.C3, num_filters=128, filter_size=(3, 3), border_mode="same",
# W=nn_plankton.Conv2DOrthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu)
#self.M2 = nn.layers.pool.MaxPool2DLayer(self.C4, pool_size=3, stride=2)
##self.D2 = nn.layers.DropoutLayer(self.M2, p=0.5)
# due to mem contrainst, this went from 64,128,256,512,512 to what it is now
#self.C5 = nn.layers.Conv2DLayer(self.M2, num_filters=256, filter_size=(3, 3), border_mode="same",
self.C5 = nn.layers.Conv2DLayer(self.l_in,
num_filters=16,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
self.C6 = nn.layers.Conv2DLayer(self.C5,
num_filters=16,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
self.C7 = nn.layers.Conv2DLayer(self.C6,
num_filters=16,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
self.M3 = nn.layers.pool.MaxPool2DLayer(self.C7, pool_size=3, stride=2)
self.C8 = nn.layers.Conv2DLayer(self.M3,
num_filters=32,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
self.C9 = nn.layers.Conv2DLayer(self.C8,
num_filters=32,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
self.C10 = nn.layers.Conv2DLayer(self.C9,
num_filters=32,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
self.M4 = nn.layers.pool.MaxPool2DLayer(self.C10,
pool_size=3,
stride=2)
self.C11 = nn.layers.Conv2DLayer(self.M4,
num_filters=64,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
self.C12 = nn.layers.Conv2DLayer(self.C11,
num_filters=64,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
self.C13 = nn.layers.Conv2DLayer(self.C12,
num_filters=64,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
self.M5 = nn.layers.pool.MaxPool2DLayer(self.C13,
pool_size=3,
stride=2)
self.FC1 = nn.layers.DenseLayer(self.M5,
num_units=4096,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
self.D1 = nn.layers.DropoutLayer(self.FC1, p=0.5)
self.FC2 = nn.layers.DenseLayer(self.D1,
num_units=4096,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
self.D2 = nn.layers.DropoutLayer(self.FC2, p=0.5)
self.FC3 = nn.layers.DenseLayer(self.D2,
num_units=4096,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
self.D3 = nn.layers.DropoutLayer(self.FC3, p=0.5)
self.output_layer = nn.layers.DenseLayer(
self.D3,
num_units=self.output_dim,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn.nonlinearities.softmax)
self.objective = nn.objectives.Objective(
self.output_layer,
loss_function=nn.objectives.categorical_crossentropy)
self.loss = self.objective.get_loss() #self.x, target=self.y)
self.probabilities = self.output_layer.get_output(self.l_in.input_var,
deterministic=True)
self.pred = T.argmax(self.output_layer.get_output(self.l_in.input_var,
deterministic=True),
axis=1)
#get weights
self.all_params = nn.layers.get_all_params(self.output_layer)
def build_model():
# variable scale part
l0_variable = nn.layers.InputLayer(
(batch_size, 1, patch_sizes[0][0], patch_sizes[0][1]))
l0c = dihedral.CyclicSliceLayer(l0_variable)
l1a = Conv2DLayer(l0c,
num_filters=32,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l1b = Conv2DLayer(l1a,
num_filters=16,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l1 = MaxPool2DLayer(l1b, ds=(3, 3), strides=(2, 2))
l1r = dihedral_fast.CyclicConvRollLayer(l1)
l2a = Conv2DLayer(l1r,
num_filters=64,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l2b = Conv2DLayer(l2a,
num_filters=32,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l2 = MaxPool2DLayer(l2b, ds=(3, 3), strides=(2, 2))
l2r = dihedral_fast.CyclicConvRollLayer(l2)
l3a = Conv2DLayer(l2r,
num_filters=128,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l3b = Conv2DLayer(l3a,
num_filters=128,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l3c = Conv2DLayer(l3b,
num_filters=64,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l3 = MaxPool2DLayer(l3c, ds=(3, 3), strides=(2, 2))
l3r = dihedral_fast.CyclicConvRollLayer(l3)
l4a = Conv2DLayer(l3r,
num_filters=256,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l4b = Conv2DLayer(l4a,
num_filters=256,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l4c = Conv2DLayer(l4b,
num_filters=128,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l4 = MaxPool2DLayer(l4c, ds=(3, 3), strides=(2, 2))
l4r = dihedral_fast.CyclicConvRollLayer(l4)
l4f = nn.layers.flatten(l4r)
l5 = nn.layers.DenseLayer(nn.layers.dropout(l4f, p=0.5),
num_units=1024,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
l5fp = nn.layers.FeaturePoolLayer(l5, ds=2)
l5m = dihedral.CyclicPoolLayer(l5fp, pool_function=nn_plankton.rms)
l6 = nn.layers.DenseLayer(nn.layers.dropout(l5m, p=0.5),
num_units=1024,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
l6fp = nn.layers.FeaturePoolLayer(l6, ds=2)
l_variable = l6fp
# fixed scale part
l0_fixed = nn.layers.InputLayer(
(batch_size, 1, patch_sizes[1][0], patch_sizes[1][1]))
l0c = dihedral.CyclicSliceLayer(l0_fixed)
l1a = Conv2DLayer(l0c,
num_filters=16,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l1b = Conv2DLayer(l1a,
num_filters=8,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l1 = MaxPool2DLayer(l1b, ds=(3, 3), strides=(2, 2))
l1r = dihedral_fast.CyclicConvRollLayer(l1)
l2a = Conv2DLayer(l1r,
num_filters=32,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l2b = Conv2DLayer(l2a,
num_filters=16,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l2 = MaxPool2DLayer(l2b, ds=(3, 3), strides=(2, 2))
l2r = dihedral_fast.CyclicConvRollLayer(l2)
l3a = Conv2DLayer(l2r,
num_filters=64,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l3b = Conv2DLayer(l3a,
num_filters=64,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l3c = Conv2DLayer(l3b,
num_filters=32,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu,
untie_biases=True)
l3 = MaxPool2DLayer(l3c, ds=(3, 3), strides=(2, 2))
l3r = dihedral_fast.CyclicConvRollLayer(l3)
l3f = nn.layers.flatten(l3r)
l4 = nn.layers.DenseLayer(nn.layers.dropout(l3f, p=0.5),
num_units=512,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
l4fp = nn.layers.FeaturePoolLayer(l4, ds=2)
l4m = dihedral.CyclicPoolLayer(l4fp, pool_function=nn_plankton.rms)
l5 = nn.layers.DenseLayer(nn.layers.dropout(l4m, p=0.5),
num_units=512,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1),
nonlinearity=nn_plankton.leaky_relu)
l5fp = nn.layers.FeaturePoolLayer(l5, ds=2)
l_fixed = l5fp
# merge the parts
l_merged = nn.layers.concat([l_variable, l_fixed])
l7 = nn.layers.DenseLayer(nn.layers.dropout(l_merged, p=0.5),
num_units=data.num_classes,
nonlinearity=T.nnet.softmax,
W=nn_plankton.Orthogonal(1.0))
return [l0_variable, l0_fixed], l7
def build_model():
# variable scale part
l0_variable = nn.layers.InputLayer(
(batch_size, 1, patch_sizes[0][0], patch_sizes[0][1]))
l0c = dihedral.CyclicSliceLayer(l0_variable)
l1a = Conv2DLayer(l0c,
num_filters=32,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l1b = Conv2DLayer(l1a,
num_filters=32,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l1 = MaxPool2DLayer(l1b, ds=(3, 3), strides=(2, 2))
l2a = Conv2DLayer(l1,
num_filters=64,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l2b = Conv2DLayer(l2a,
num_filters=64,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l2 = MaxPool2DLayer(l2b, ds=(3, 3), strides=(2, 2))
l3a = Conv2DLayer(l2,
num_filters=128,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l3b = Conv2DLayer(l3a,
num_filters=128,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l3c = Conv2DLayer(l3b,
num_filters=128,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l3 = MaxPool2DLayer(l3c, ds=(3, 3), strides=(2, 2))
l4a = Conv2DLayer(l3,
num_filters=256,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l4b = Conv2DLayer(l4a,
num_filters=256,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l4c = Conv2DLayer(l4b,
num_filters=256,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l4 = MaxPool2DLayer(l4c, ds=(3, 3), strides=(2, 2))
l4f = nn.layers.flatten(l4)
l5 = nn.layers.DenseLayer(nn.layers.dropout(l4f, p=0.5),
num_units=256,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1))
l5r = dihedral.CyclicRollLayer(l5)
l6 = nn.layers.DenseLayer(nn.layers.dropout(l5r, p=0.5),
num_units=256,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1))
l_variable = dihedral.CyclicPoolLayer(l6, pool_function=nn_plankton.rms)
# fixed scale part
l0_fixed = nn.layers.InputLayer(
(batch_size, 1, patch_sizes[1][0], patch_sizes[1][1]))
l0c = dihedral.CyclicSliceLayer(l0_fixed)
l1a = Conv2DLayer(l0c,
num_filters=16,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l1b = Conv2DLayer(l1a,
num_filters=16,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l1 = MaxPool2DLayer(l1b, ds=(3, 3), strides=(2, 2))
l2a = Conv2DLayer(l1,
num_filters=32,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l2b = Conv2DLayer(l2a,
num_filters=32,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l2 = MaxPool2DLayer(l2b, ds=(3, 3), strides=(2, 2))
l3a = Conv2DLayer(l2,
num_filters=64,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l3b = Conv2DLayer(l3a,
num_filters=64,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l3c = Conv2DLayer(l3b,
num_filters=64,
filter_size=(3, 3),
border_mode="same",
W=nn_plankton.Conv2DOrthogonal(1.0),
b=nn.init.Constant(0.1))
l3 = MaxPool2DLayer(l3c, ds=(3, 3), strides=(2, 2))
l3f = nn.layers.flatten(l3)
l4 = nn.layers.DenseLayer(nn.layers.dropout(l3f, p=0.5),
num_units=128,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1))
l4r = dihedral.CyclicRollLayer(l4)
l5 = nn.layers.DenseLayer(nn.layers.dropout(l4r, p=0.5),
num_units=128,
W=nn_plankton.Orthogonal(1.0),
b=nn.init.Constant(0.1))
l_fixed = dihedral.CyclicPoolLayer(l5, pool_function=nn_plankton.rms)
# merge the parts
l_merged = nn.layers.concat([l_variable, l_fixed])
l7 = nn.layers.DenseLayer(nn.layers.dropout(l_merged, p=0.5),
num_units=data.num_classes,
nonlinearity=T.nnet.softmax,
W=nn_plankton.Orthogonal(1.0))
return [l0_variable, l0_fixed], l7
