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_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) 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) 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) 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) 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) 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) 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) l4 = MaxPool2DLayer(l4c, ds=(3, 3), strides=(2, 2)) l4r = dihedral.CyclicConvRollLayer(l4) l4f = nn.layers.flatten(l4r) l5 = nn.layers.DenseLayer(l4f, num_units=256, W=nn_plankton.Orthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu) l5r = dihedral.CyclicRollLayer(l5) l6 = nn.layers.DenseLayer(l5r, num_units=256, W=nn_plankton.Orthogonal(1.0), b=nn.init.Constant(0.1), nonlinearity=nn_plankton.leaky_relu) 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=8, 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)) l1r = dihedral.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)) 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)) l2 = MaxPool2DLayer(l2b, ds=(3, 3), strides=(2, 2)) l2r = dihedral.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)) 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=32, 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)) l3r = dihedral.CyclicConvRollLayer(l3) l3f = nn.layers.flatten(l3r) l4 = nn.layers.DenseLayer(l3f, num_units=128, W=nn_plankton.Orthogonal(1.0), b=nn.init.Constant(0.1)) l4r = dihedral.CyclicRollLayer(l4) l5 = nn.layers.DenseLayer(l4r, 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(l_merged, num_units=data.num_classes, nonlinearity=T.nnet.softmax, W=nn_plankton.Orthogonal(1.0)) return [l0_variable, l0_fixed], l7