def generate_mlp_architectures(class_or_reg='reg'):
""" pylint: disable=bad-whitespace. """
# pylint: disable=bad-whitespace
all_layer_label_classes = get_mlp_layer_labels(class_or_reg)
last_layer_label = 'linear' if class_or_reg == 'reg' else 'softmax'
# Network 1
layer_labels = [
'ip', 'op', 'tanh', 'logistic', 'softplus', 'relu', 'elu',
last_layer_label
]
num_units_each_layer = [None, None, 32, 64, 16, 8, 8, None]
A = get_dok_mat_with_set_coords(8, [(0, 4), (2, 3), (3, 7), (4, 5), (4, 6),
(5, 2), (6, 2), (7, 1)])
mlp_1 = MultiLayerPerceptron(class_or_reg, layer_labels, A,
num_units_each_layer, all_layer_label_classes)
# Network 2
layer_labels = [
'ip', 'softplus', 'elu', 'tanh', 'logistic', last_layer_label, 'op'
]
num_units_each_layer = [None, 16, 16, 32, 64, None, None]
A = get_dok_mat_with_set_coords(7, [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5),
(5, 6)])
mlp_2 = MultiLayerPerceptron(class_or_reg, layer_labels, A,
num_units_each_layer, all_layer_label_classes)
# Network 3
layer_labels = [
'ip', 'tanh', 'logistic', 'logistic', 'tanh', 'elu', 'relu',
last_layer_label, 'op'
]
num_units_each_layer = [None, 8, 8, 8, 8, 16, 16, None, None]
A = get_dok_mat_with_set_coords(9, [(0, 1), (0, 2), (1, 3), (2, 4), (3, 5),
(3, 6), (4, 5), (4, 6), (5, 7), (6, 7),
(7, 8)])
mlp_3 = MultiLayerPerceptron(class_or_reg, layer_labels, A,
num_units_each_layer, all_layer_label_classes)
# Network 4
layer_labels = [
'ip', 'logistic', 'relu', 'softplus', last_layer_label, 'op', 'relu',
'tanh'
]
num_units_each_layer = [None, 8, 8, 16, None, None, 16, 8]
A = get_dok_mat_with_set_coords(8, [(0, 1), (0, 7), (1, 2), (2, 3), (2, 6),
(7, 6), (7, 3), (3, 4), (6, 4),
(4, 5)])
mlp_4 = MultiLayerPerceptron(class_or_reg, layer_labels, A,
num_units_each_layer, all_layer_label_classes)
return [
mlp_1,
mlp_2,
mlp_3,
mlp_4,
get_blocked_mlp(class_or_reg, 4, 3),
get_blocked_mlp(class_or_reg, 8, 2),
get_multidepth_mlp_eg1(class_or_reg),
get_multidepth_mlp_eg2(class_or_reg),
]
def get_multidepth_mlp_eg1(class_or_reg):
""" Multi depth MLP eg 1. """
layer_labels, num_units_in_each_layer, edges, mlp_layer_labels = \
_get_multidepth_mlp_eg12_common(class_or_reg)
conn_mat = get_dok_mat_with_set_coords(len(layer_labels), edges)
return MultiLayerPerceptron(class_or_reg, layer_labels, conn_mat,
num_units_in_each_layer, mlp_layer_labels)
def get_multidepth_cnn_eg1():
""" A network with 2 softmax layers mostly for debugging. """
layer_labels, edges, num_filters_each_layer, cnn_layer_labels, strides = \
_get_multidepth_cnn_eg12_common()
edges.append((3, 4))
conn_mat = get_dok_mat_with_set_coords(len(layer_labels), edges)
strides[9] = 2
return ConvNeuralNetwork(layer_labels, conn_mat, num_filters_each_layer,
strides, cnn_layer_labels)
def generate_cnn_architectures():
# pylint: disable=bad-whitespace
""" Generates 4 neural networks. """
all_layer_label_classes = get_cnn_layer_labels()
# Network 1
layer_labels = [
'ip', 'op', 'conv3', 'fc', 'conv3', 'conv3', 'conv3', 'softmax',
'max-pool'
]
num_filters_each_layer = [None, None, 32, 16, 16, 8, 8, None, None]
A = get_dok_mat_with_set_coords(9, [(0, 4), (3, 7), (4, 5), (4, 6), (5, 2),
(6, 2), (7, 1), (2, 8), (8, 3)])
strides = [
None if ll in ['ip', 'op', 'fc', 'softmax', 'max-pool'] else 1
for ll in layer_labels
]
cnn_1 = ConvNeuralNetwork(layer_labels, A, num_filters_each_layer, strides,
all_layer_label_classes)
# Network 2
layer_labels = [
'ip', 'conv3', 'conv3', 'conv3', 'fc', 'softmax', 'op', 'max-pool'
]
num_filters_each_layer = [None, 16, 16, 32, 16, None, None, None]
A = get_dok_mat_with_set_coords(8, [(0, 1), (1, 2), (2, 3), (4, 5), (5, 6),
(3, 7), (7, 4)])
strides = [
None if ll in ['ip', 'op', 'fc', 'softmax', 'max-pool'] else 1
for ll in layer_labels
]
cnn_2 = ConvNeuralNetwork(layer_labels, A, num_filters_each_layer, strides,
all_layer_label_classes)
# Network 3
layer_labels = [
'ip', 'conv3', 'conv3', 'conv5', 'conv3', 'max-pool', 'fc', 'softmax',
'op'
]
num_filters_each_layer = [None, 16, 16, 16, 32, None, 32, None, None]
A = get_dok_mat_with_set_coords(9, [(0, 1), (1, 2), (1, 3), (1, 4), (2, 5),
(3, 5), (4, 6), (5, 6), (6, 7),
(7, 8)])
strides = [
None if ll in ['ip', 'op', 'fc', 'softmax', 'max-pool'] else 1
for ll in layer_labels
]
strides[4] = 2
cnn_3 = ConvNeuralNetwork(layer_labels, A, num_filters_each_layer, strides,
all_layer_label_classes)
# Network 4
layer_labels = [
'ip', 'conv3', 'conv3', 'conv5', 'conv3', 'avg-pool', 'conv5', 'fc',
'softmax', 'op'
]
num_filters_each_layer = [None, 16, 16, 16, 32, None, 32, 32, None, None]
A = get_dok_mat_with_set_coords(10,
[(0, 1), (1, 2), (1, 3), (1, 4), (2, 5),
(3, 5), (4, 6), (5, 7), (6, 7), (7, 8),
(8, 9)])
strides = [
None
if ll in ['ip', 'op', 'fc', 'softmax', 'max-pool', 'avg-pool'] else 1
for ll in layer_labels
]
strides[4] = 2
cnn_4 = ConvNeuralNetwork(layer_labels, A, num_filters_each_layer, strides,
all_layer_label_classes)
# Network 5
layer_labels = [
'ip', 'conv3', 'conv3', 'conv5', 'conv5', 'avg-pool', 'fc', 'softmax',
'op', 'conv3'
]
num_filters_each_layer = [None, 16, 16, 16, 32, None, 32, None, None, 16]
A = get_dok_mat_with_set_coords(10,
[(0, 1), (1, 2), (1, 3), (2, 5), (3, 5),
(4, 6), (5, 6), (6, 7), (7, 8), (0, 9),
(9, 4)])
strides = [
None if ll in ['ip', 'op', 'fc', 'softmax', 'avg-pool'] else 1
for ll in layer_labels
]
strides[4] = 2
cnn_5 = ConvNeuralNetwork(layer_labels, A, num_filters_each_layer, strides,
all_layer_label_classes)
# Network 6
layer_labels = [
'ip', 'conv3', 'conv3', 'conv3', 'fc', 'fc', 'op', 'max-pool', 'fc',
'softmax'
]
num_filters_each_layer = [None, 16, 16, 32, 32, 32, None, None, 32, None]
A = get_dok_mat_with_set_coords(10,
[(0, 1), (1, 2), (2, 3), (4, 5), (5, 9),
(3, 7), (7, 4), (4, 8), (8, 9), (9, 6)])
strides = [
None if ll in ['ip', 'op', 'fc', 'softmax', 'max-pool'] else 1
for ll in layer_labels
]
cnn_6 = ConvNeuralNetwork(layer_labels, A, num_filters_each_layer, strides,
all_layer_label_classes)
# Network 7 - Two softmax layers both pointing to output
layer_labels = [
'ip', 'conv3', 'conv3', 'conv3', 'fc', 'fc', 'op', 'max-pool', 'fc',
'softmax', 'softmax'
]
num_filters_each_layer = [
None, 16, 16, 32, 32, 32, None, None, 32, None, None
]
A = get_dok_mat_with_set_coords(11,
[(0, 1), (1, 2), (2, 3), (4, 5), (5, 9),
(3, 7), (7, 4), (4, 8), (8, 9), (9, 6),
(8, 10), (10, 6)])
strides = [
None if ll in ['ip', 'op', 'fc', 'softmax', 'max-pool'] else 1
for ll in layer_labels
]
cnn_7 = ConvNeuralNetwork(layer_labels, A, num_filters_each_layer, strides,
all_layer_label_classes)
# Network 8 - Similar to previous, except with residual layers
layer_labels = [
'ip', 'conv3', 'res3', 'res5', 'fc', 'fc', 'op', 'max-pool', 'fc',
'softmax', 'softmax'
]
num_filters_each_layer = [
None, 16, 16, 32, 32, 32, None, None, 32, None, None
]
A = get_dok_mat_with_set_coords(11,
[(0, 1), (1, 2), (2, 3), (4, 5), (5, 9),
(3, 7), (7, 4), (4, 8), (8, 9), (9, 6),
(8, 10), (10, 6)])
strides = [
None if ll in ['ip', 'op', 'fc', 'softmax', 'max-pool'] else 1
for ll in layer_labels
]
cnn_8 = ConvNeuralNetwork(layer_labels, A, num_filters_each_layer, strides,
all_layer_label_classes)
# Network 9 - Similar to previous, except decreasing units for residual layers
layer_labels = [
'ip', 'conv3', 'res3', 'res5', 'res7', 'fc', 'op', 'max-pool', 'fc',
'softmax', 'softmax'
]
num_filters_each_layer = [
None, 16, 32, 8, 32, 128, None, None, 256, None, None
]
A = get_dok_mat_with_set_coords(11,
[(0, 1), (1, 2), (2, 3), (4, 5), (5, 9),
(3, 7), (7, 4), (4, 8), (8, 9), (9, 6),
(8, 10), (10, 6)])
strides = [
None if ll in ['ip', 'op', 'fc', 'softmax', 'max-pool'] else 1
for ll in layer_labels
]
cnn_9 = ConvNeuralNetwork(layer_labels, A, num_filters_each_layer, strides,
all_layer_label_classes)
return [
cnn_1,
cnn_2,
cnn_3,
cnn_4,
cnn_5,
cnn_6,
cnn_7,
cnn_8,
cnn_9,
get_vgg_net(2),
get_blocked_cnn(3, 4, 1),
get_resnet_cnn(3, 2, 1),
get_multidepth_cnn_eg1(),
get_multidepth_cnn_eg2(),
]
