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nn_modifiers.py
1090 lines (1030 loc) · 51 KB
/
nn_modifiers.py
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"""
Utilities to modify a given neural network and obtain a new one.
--kandasamy@cs.cmu.edu
"""
# pylint: disable=invalid-name
# pylint: disable=star-args
# pylint: disable=too-many-branches
from argparse import Namespace
from copy import deepcopy
import numpy as np
# Local imports
from .neural_network import ConvNeuralNetwork, MultiLayerPerceptron, MLP_RECTIFIERS, \
MLP_SIGMOIDS, is_a_pooling_layer_label, is_a_conv_layer_label,\
CNNImageSizeMismatchException, CNNNoConvAfterIPException
from ..utils.general_utils import reorder_list_or_array, reorder_rows_and_cols_in_matrix
from ..utils.option_handler import get_option_specs, load_options
from ..utils.reporters import get_reporter
_DFLT_CHANGE_FRAC = 0.125
_DFLT_CHANGE_NUM_UNITS_SPAWN = 'all'
_DFLT_CHANGE_LAYERS_SPAWN = 20
_DFLT_NUM_SINGLE_STEP_MODIFICATIONS = 'all'
_DFLT_NUM_TWO_STEP_MODIFICATIONS = 0
_DFLT_NUM_THREE_STEP_MODIFICATIONS = 0
_DFLT_WEDGE_LAYER_CNN_CANDIDATES = ['conv3', 'conv5', 'conv7', 'res3', 'res5', 'res7']
_DFLT_WEDGE_LAYER_MLP_CANDIDATES = MLP_RECTIFIERS + MLP_SIGMOIDS
_DFLT_SIGMOID_SWAP = MLP_SIGMOIDS
_DFLT_RECTIFIER_SWAP = MLP_RECTIFIERS
_PRIMITIVE_PROB_MASSES = {'inc_single': 0.1,
'dec_single': 0.1,
'inc_en_masse': 0.1,
'dec_en_masse': 0.1,
'swap_layer': 0.2,
'wedge_layer': 0.1,
'remove_layer': 0.1,
'branch': 0.2,
'skip': 0.2,
}
nn_modifier_args = [
# Change fractions for increasing the number of units in layers.
get_option_specs('single_inc_change_frac', False, _DFLT_CHANGE_FRAC,
'Default change fraction when increasing a single layer.'),
get_option_specs('single_dec_change_frac', False, _DFLT_CHANGE_FRAC,
'Default change fraction when decreasing a single layer.'),
get_option_specs('en_masse_inc_change_frac', False, _DFLT_CHANGE_FRAC,
'Default change fraction when increasing layers en_masse.'),
get_option_specs('en_masse_dec_change_frac', False, _DFLT_CHANGE_FRAC,
'Default change fraction when decreasing layers en_masse.'),
# Number of networks to spawn by changing number of units in a single layer.
get_option_specs('spawn_single_inc_num_units', False, _DFLT_CHANGE_NUM_UNITS_SPAWN,
'Default number of networks to spawn by increasing # units in a single layer.'),
get_option_specs('spawn_single_dec_num_units', False, _DFLT_CHANGE_NUM_UNITS_SPAWN,
'Default number of networks to spawn by decreasing # units in a single layer.'),
# Number of networks to spawn by adding or deleting a single layer.
get_option_specs('spawn_add_layer', False, _DFLT_CHANGE_LAYERS_SPAWN,
'Default number of networks to spawn by adding a layer.'),
get_option_specs('spawn_del_layer', False, _DFLT_CHANGE_LAYERS_SPAWN,
'Default number of networks to spawn by deleting a layer.'),
# Number of double/triple step candidates - i.e. applications of basic primitives
# twice/thrice before executing candidates
get_option_specs('num_single_step_modifications', False,
_DFLT_NUM_SINGLE_STEP_MODIFICATIONS,
'Default number of networks to spawn via single step primitives.'),
get_option_specs('num_two_step_modifications', False,
_DFLT_NUM_TWO_STEP_MODIFICATIONS,
'Default number of networks to spawn via two step primitives.'),
get_option_specs('num_three_step_modifications', False,
_DFLT_NUM_THREE_STEP_MODIFICATIONS,
'Default number of networks to spawn via three step primitives.'),
]
# Generic utilities we will need in all functions below ==================================
def get_copies_from_old_nn(nn):
""" Gets copies of critical parameters of the old network. """
layer_labels = deepcopy(nn.layer_labels)
num_units_in_each_layer = deepcopy(nn.num_units_in_each_layer)
conn_mat = deepcopy(nn.conn_mat)
mandatory_child_attributes = Namespace()
for mca_str in nn.mandatory_child_attributes:
mca_val = deepcopy(getattr(nn, mca_str))
setattr(mandatory_child_attributes, mca_str, mca_val)
return layer_labels, num_units_in_each_layer, conn_mat, mandatory_child_attributes
def get_new_nn(old_nn, layer_labels, num_units_in_each_layer, conn_mat,
mandatory_child_attributes):
""" Returns a new neural network of the same type as old_nn. """
known_nn_class = True
try:
if old_nn.nn_class == 'cnn':
new_cnn = ConvNeuralNetwork(layer_labels, conn_mat, num_units_in_each_layer,
mandatory_child_attributes.strides,
old_nn.all_layer_label_classes,
old_nn.layer_label_similarities)
return new_cnn
elif old_nn.nn_class.startswith('mlp'):
return MultiLayerPerceptron(old_nn.nn_class[4:], layer_labels, conn_mat,
num_units_in_each_layer, old_nn.all_layer_label_classes,
old_nn.layer_label_similarities)
else:
known_nn_class = False
except (CNNImageSizeMismatchException, CNNNoConvAfterIPException, AssertionError):
return None
if not known_nn_class:
raise ValueError('Unidentified nn_class %s.'%(old_nn.nn_class))
def add_layers_to_end_of_conn_mat(conn_mat, num_add_layers):
""" Adds layers with no edges and returns. """
new_num_layers = conn_mat.shape[0] + num_add_layers
conn_mat.resize((new_num_layers, new_num_layers))
return conn_mat
# Change architecture of the network
# ========================================================================================
# Add a layer ----------------------------------------------------------------------------
def wedge_layer(nn, layer_type, units_in_layer, layer_before, layer_after,
new_layer_attributes=None):
""" Wedges a layer of type layer_type after the layer given in layer_before. The
output of the layer in layer_before goes to the new layer and the output of the
new layer goes to layer_after. If an edge existed between layer_before and
layer_after, it is removed. """
layer_labels, num_units_in_each_layer, conn_mat, mandatory_child_attributes = \
get_copies_from_old_nn(nn)
layer_labels.append(layer_type)
num_units_in_each_layer = np.append(num_units_in_each_layer, units_in_layer)
if nn.nn_class == 'cnn':
mandatory_child_attributes.strides.append(new_layer_attributes.stride)
conn_mat = add_layers_to_end_of_conn_mat(conn_mat, 1)
conn_mat[layer_before, -1] = 1
conn_mat[-1, layer_after] = 1
conn_mat[layer_before, layer_after] = 0
return get_new_nn(nn, layer_labels, num_units_in_each_layer, conn_mat,
mandatory_child_attributes)
def _get_non_None_elements(iter_of_vals):
""" Returns non None values. """
return [x for x in iter_of_vals if x is not None]
def _determine_num_units_for_wedge_layer(nn, edge):
""" Determines the number of layers for wedging a layer. This is usually the average
of the parent (edge[0]) and child (edge[1]).
"""
edge_num_layers = _get_non_None_elements(
[nn.num_units_in_each_layer[idx] for idx in edge])
if len(edge_num_layers) > 0:
return round(sum(edge_num_layers) / len(edge_num_layers))
else:
parents = nn.get_parents(edge[0])
if len(parents) == 0:
# Means you have reached the input node
ip_children = nn.get_children(edge[0])
children_num_units = _get_non_None_elements(
[nn.num_units_in_each_layer[ch] for ch in ip_children])
if len(children_num_units) == 0:
# Create a layer with 16 units
children_num_units = [16]
return sum(children_num_units) / len(children_num_units)
else:
parent_num_units = _get_non_None_elements(
[nn.num_units_in_each_layer[par] for par in parents])
if len(parent_num_units) > 0:
return sum(parent_num_units) / len(parent_num_units)
else:
par_num_units = []
for par in parents:
par_num_units.append(_determine_num_units_for_wedge_layer(nn, (par, edge[0])))
par_num_units = _get_non_None_elements(par_num_units)
return sum(par_num_units) / len(par_num_units)
def get_list_of_wedge_layer_modifiers(nn, num_modifications='all',
internal_layer_type_candidates=None,
choose_pool_with_prob=0.05,
choose_stride_2_with_prob=0.05):
""" Returns a list of operations for adding a layer in between two layers. """
# A local function for creating a modifier
def _get_wedge_modifier(_layer_type, _num_units, _edge, _nl_attributes):
""" Returns a modifier which wedges an edge between the edge. """
return lambda arg_nn: wedge_layer(arg_nn, _layer_type, _num_units,
_edge[0], _edge[1], _nl_attributes)
# Pre-process arguments
nn_is_a_cnn = nn.nn_class == 'cnn'
if internal_layer_type_candidates is None:
if nn_is_a_cnn:
internal_layer_type_candidates = _DFLT_WEDGE_LAYER_CNN_CANDIDATES
else:
internal_layer_type_candidates = _DFLT_WEDGE_LAYER_MLP_CANDIDATES
if not nn_is_a_cnn:
choose_pool_with_prob = 0
all_edges = nn.get_edges()
num_modifications = len(all_edges) if num_modifications == 'all' else num_modifications
op_layer_idx = nn.get_op_layer_idx() # Output layer
ip_layer_idx = nn.get_ip_layer_idx() # Input layer
# We won't change this below so keep it as it is
nonconv_nl_attrs = Namespace(stride=None)
conv_nl_attrs_w_stride_1 = Namespace(stride=1)
conv_nl_attrs_w_stride_2 = Namespace(stride=2)
# Iterate through all edges
ret = []
for edge in all_edges:
curr_layer_type = None
# First handle the edges cases
if edge[1] == op_layer_idx:
continue
elif nn_is_a_cnn and nn.layer_labels[edge[0]] == 'fc':
curr_layer_type = 'fc'
curr_num_units = nn.num_units_in_each_layer[edge[0]]
nl_attrs = nonconv_nl_attrs
elif not nn_is_a_cnn and edge[1] == op_layer_idx:
# Don't add new layers just before the output for MLPs
continue
elif edge[0] == ip_layer_idx and nn_is_a_cnn:
curr_pool_prob = 0 # No pooling layer right after the input for a CNN
else:
curr_pool_prob = choose_pool_with_prob
if curr_layer_type is None:
if np.random.random() < curr_pool_prob:
curr_layer_candidates = ['avg-pool', 'max-pool']
else:
curr_layer_candidates = internal_layer_type_candidates
curr_layer_type = np.random.choice(curr_layer_candidates, 1)[0]
if curr_layer_type in ['max-pool', 'avg-pool', 'linear', 'softmax']:
curr_num_units = None
else:
curr_num_units = _determine_num_units_for_wedge_layer(nn, edge)
# Determine stride
if is_a_conv_layer_label(curr_layer_type):
nl_attrs = conv_nl_attrs_w_stride_2 if \
np.random.random() < choose_stride_2_with_prob else conv_nl_attrs_w_stride_1
else:
nl_attrs = nonconv_nl_attrs
ret.append(_get_wedge_modifier(curr_layer_type, curr_num_units, edge, nl_attrs))
# Break if more than the number of modifications
if len(ret) >= num_modifications:
break
return ret
# Removing a layer -----------------------------------------------------------------------
def remove_layer(nn, del_idx, additional_edges, new_strides=None):
""" Deletes the layer indicated in del_idx and adds additional_edges specified
in additional_edges. """
layer_labels, num_units_in_each_layer, conn_mat, mandatory_child_attributes = \
get_copies_from_old_nn(nn)
# First add new edges to conn_mat and remove edges to and from del_idx
for add_edge in additional_edges:
conn_mat[add_edge[0], add_edge[1]] = 1
conn_mat[del_idx, :] = 0
conn_mat[:, del_idx] = 0
# Now reorder everything so that del_idx is at the end
all_idxs = list(range(len(layer_labels)))
new_order = all_idxs[:del_idx] + all_idxs[del_idx+1:] + [del_idx]
# Now reorder everything so that the layer to be remove is at the end
layer_labels = reorder_list_or_array(layer_labels, new_order)
num_units_in_each_layer = reorder_list_or_array(num_units_in_each_layer, new_order)
conn_mat = reorder_rows_and_cols_in_matrix(conn_mat, new_order)
# remove layer
layer_labels = layer_labels[:-1]
num_units_in_each_layer = num_units_in_each_layer[:-1]
conn_mat = conn_mat[:-1, :-1]
# Strides for a convolutional network
if nn.nn_class == 'cnn':
new_strides = new_strides if new_strides is not None else \
mandatory_child_attributes.strides
mandatory_child_attributes.strides = reorder_list_or_array(
new_strides, new_order)
mandatory_child_attributes.strides = mandatory_child_attributes.strides[:-1]
return get_new_nn(nn, layer_labels, num_units_in_each_layer, conn_mat,
mandatory_child_attributes)
def get_list_of_remove_layer_modifiers(old_nn):
""" Returns a list of primitives which remove a layer from a neural network. """
# pylint: disable=too-many-locals
# A local function to return the modifier
if old_nn.num_processing_layers == 0:
# Don't delete any layers if there are no processing layers.
return []
def _get_remove_modifier(_del_idx, _add_edges, *_args, **_kwargs):
""" Returns a modifier which deletes _del_idx and adds _add_edges. """
return lambda arg_nn: remove_layer(arg_nn, _del_idx, _add_edges, *_args, **_kwargs)
# Now check every layer
ret = []
for idx, ll in enumerate(old_nn.layer_labels):
if ll in ['ip', 'op', 'softmax', 'linear']: # Don't delete any of these layers
continue
curr_parents = old_nn.get_parents(idx)
parent_labels = [old_nn.layer_labels[par_idx] for par_idx in curr_parents]
if ll == 'fc' and (not parent_labels == ['fc'] * len(parent_labels)):
# If the parents of a fc layer are also not fc then do not delete
continue
curr_children = old_nn.get_children(idx)
if old_nn.nn_class == 'cnn' and \
old_nn.pre_img_inv_sizes[idx] != old_nn.post_img_inv_sizes[idx]:
change_stride_idxs = None
# First check if the children are modifiable
child_strides = [old_nn.strides[ch_idx] for ch_idx in curr_children]
if child_strides == [1] * len(curr_children):
change_stride_idxs = curr_children # we will change the strides of the children
if change_stride_idxs is None:
parent_strides = [old_nn.strides[par_idx] for par_idx in curr_parents]
if parent_strides == [1] * len(curr_parents):
change_stride_idxs = curr_parents
# If we have successfuly identified children/parents which we can modify, great!
# Otherwise, lets not change anything and hope that it
# does not break anything. If it does, there is an exception to handle this.
if change_stride_idxs is not None:
new_strides = deepcopy(old_nn.strides)
for csi in change_stride_idxs:
new_strides[csi] = 2
else:
new_strides = None
else:
new_strides = None
# Now delete the layer and add new adges
num_children_on_each_parent = [len(old_nn.get_children(par_idx)) for par_idx in
curr_parents]
num_parents_on_each_child = [len(old_nn.get_parents(ch_idx)) for ch_idx in
curr_children]
must_add_children = [curr_children[i] for i in range(len(curr_children)) if
num_parents_on_each_child[i] == 1]
must_add_parents = [curr_parents[i] for i in range(len(curr_parents)) if
num_children_on_each_parent[i] == 1]
num_must_add_children = len(must_add_children)
num_must_add_parents = len(must_add_parents)
np.random.shuffle(must_add_children)
np.random.shuffle(must_add_parents)
add_edges = []
for _ in range(min(num_must_add_children, num_must_add_parents)):
add_edges.append((must_add_parents.pop(), must_add_children.pop()))
# Add edges for left over children/parents
if num_must_add_children > num_must_add_parents:
diff = num_must_add_children - num_must_add_parents
cand_parents = list(np.random.choice(curr_parents, diff))
for _ in range(diff):
add_edges.append((cand_parents.pop(), must_add_children.pop()))
if num_must_add_parents > num_must_add_children:
diff = num_must_add_parents - num_must_add_children
cand_children = list(np.random.choice(curr_children, diff))
for _ in range(diff):
add_edges.append((must_add_parents.pop(), cand_children.pop()))
ret.append(_get_remove_modifier(idx, add_edges, new_strides))
return ret
# Branching modifications ----------------------------------------------------------------
def create_duplicate_branch(nn, path, keep_layer_with_prob=0.5):
""" Creates a new network which creates a new branch between path[0] and path[-1] and
copies all layers between. It keeps a layer in between with probability 0.5. If
in CNNs, the layer shrinks the size of the image, then we keep it with prob 1.
"""
layer_labels, num_units_in_each_layer, conn_mat, mandatory_child_attributes = \
get_copies_from_old_nn(nn)
# First decide which nodes in the path to keep
branched_path = [path[0]]
fc_encountered = False
for idx in path[1: -1]:
if idx == path[1] and nn.get_ip_layer_idx() == path[0]:
branched_path.append(idx) # Append if the branch starts at ip and this is a child.
elif idx == path[-2] and len(branched_path) == 1:
branched_path.append(idx) # If this is the last layer and we have not appended yet.
elif is_a_pooling_layer_label(nn.layer_labels[idx]) or \
nn.layer_labels[idx] in ['linear', 'softmax'] or \
(hasattr(nn, 'strides') and nn.strides[idx] == 2):
branched_path.append(idx)
elif nn.layer_labels[idx] == 'fc' and not fc_encountered:
branched_path.append(idx)
fc_encountered = True
elif np.random.random() < keep_layer_with_prob:
branched_path.append(idx)
branched_path.append(path[-1])
# Now create additional nodes
num_new_nodes = len(branched_path) - 2
layer_labels.extend([nn.layer_labels[idx] for idx in branched_path[1:-1]])
num_units_in_each_layer = np.concatenate((num_units_in_each_layer,
[nn.num_units_in_each_layer[idx] for idx in branched_path[1:-1]]))
# Add edges
new_idxs = list(range(nn.num_layers, nn.num_layers + num_new_nodes))
conn_mat = add_layers_to_end_of_conn_mat(conn_mat, num_new_nodes)
if num_new_nodes == 0:
conn_mat[branched_path[0], branched_path[1]] = 1
else:
conn_mat[branched_path[0], new_idxs[0]] = 1
conn_mat[new_idxs[-1], branched_path[-1]] = 1
for new_idx in new_idxs[:-1]:
conn_mat[new_idx, new_idx + 1] = 1
# Add strides
if nn.nn_class == 'cnn':
mandatory_child_attributes.strides.extend([nn.strides[idx] for idx in
branched_path[1:-1]])
return get_new_nn(nn, layer_labels, num_units_in_each_layer, conn_mat,
mandatory_child_attributes)
def _get_path_for_branching_from_start_layer(nn, start_layer, min_path_length=4,
end_path_prob=0.20):
""" Returns a path which starts at start layer. """
path = [start_layer]
while True:
curr_layer = path[-1]
curr_children = nn.get_children(curr_layer)
next_layer = int(np.random.choice(curr_children, 1))
path.append(next_layer)
if nn.layer_labels[next_layer] == 'op':
break
elif len(path) < min_path_length:
pass
elif np.random.random() < end_path_prob:
break
return path
def _get_start_layer_probs_for_branching_and_skipping(nn):
""" Returns probabilities for the start layer to be used in branching and
skipping primitives.
"""
dists_from_ip = nn.get_distances_from_ip()
start_layer_prob = []
for layer_idx, layer_label in enumerate(nn.layer_labels):
# We pick the first layer with distance inversely proportional to its distance from ip
curr_layer_prob = 0 if layer_label in ['op', 'softmax', 'fc', 'linear'] else \
1.0 / np.sqrt(1 + dists_from_ip[layer_idx])
start_layer_prob.append(curr_layer_prob)
start_layer_prob = np.array(start_layer_prob)
return start_layer_prob
def get_list_of_branching_modifiers(nn, num_modifiers=None, **kwargs):
""" Returns a list of operators for Neural networks that create branches in the
architecture.
"""
if nn.num_processing_layers == 0:
# Don't create any branches if there are no processing layers.
return []
# Define a local function to return the modifier
def _get_branching_modifier(_path, *_args, **_kwargs):
""" Returns a modifier which duplicates the path along _path. """
return lambda arg_nn: create_duplicate_branch(arg_nn, _path, *_args, **_kwargs)
# Some preprocessing
num_modifiers = num_modifiers if num_modifiers is not None else 2 * nn.num_layers
start_layer_prob = _get_start_layer_probs_for_branching_and_skipping(nn)
ret = []
if sum(start_layer_prob) <= 0.0:
return ret # return immediately with an empty list
while len(ret) < num_modifiers:
start_layer_prob = start_layer_prob / sum(start_layer_prob)
start_layer = int(np.random.choice(nn.num_layers, 1, p=start_layer_prob))
path = _get_path_for_branching_from_start_layer(nn, start_layer)
start_layer_prob[start_layer] *= 0.9 # shrink probability of picking this layer again.
ret.append(_get_branching_modifier(path, **kwargs))
return ret
# Skipping modifications -----------------------------------------------------------------
def create_skipped_network(nn, start_layer, end_layer, pool_layer_type='avg'):
""" Creates a new layer with a skip connection from start_layer to end_layer.
In a CNN, if the image sizes do not match, this creates additional pooling layers
(either avg-pool or max-pool) to make them match.
"""
layer_labels, num_units_in_each_layer, conn_mat, mandatory_child_attributes = \
get_copies_from_old_nn(nn)
if nn.nn_class != 'cnn' or \
nn.post_img_inv_sizes[start_layer] == nn.pre_img_inv_sizes[end_layer]:
conn_mat[start_layer, end_layer] = 1
else:
# Determine number of new layers, the number of units and strides in each layer.
num_new_pool_layers = int(np.log2(nn.pre_img_inv_sizes[end_layer] /
nn.post_img_inv_sizes[start_layer]))
new_layer_idxs = list(range(nn.num_layers, nn.num_layers + num_new_pool_layers))
num_units_in_each_layer = np.concatenate((num_units_in_each_layer,
[None] * num_new_pool_layers))
mandatory_child_attributes.strides.extend([None] * num_new_pool_layers)
# Determine layer labels
if pool_layer_type.lower().startswith('avg'):
new_layer_type = 'avg-pool'
elif pool_layer_type.lower().startswith('max'):
new_layer_type = 'max-pool'
else:
raise ValueError('pool_layer_type should be \'avg\' or \'max\'.')
new_layer_labels = [new_layer_type for _ in range(num_new_pool_layers)]
layer_labels.extend(new_layer_labels)
conn_mat = add_layers_to_end_of_conn_mat(conn_mat, num_new_pool_layers)
# Finally, the conn_mat
conn_mat[start_layer, new_layer_idxs[0]] = 1
conn_mat[new_layer_idxs[-1], end_layer] = 1
for new_idx in new_layer_idxs[:-1]:
conn_mat[new_idx, new_idx + 1] = 1
return get_new_nn(nn, layer_labels, num_units_in_each_layer, conn_mat,
mandatory_child_attributes)
def _get_end_layer_probs_for_skipping(nn, start_layer):
""" Returns the end layer probabilities to be used in skipping. """
dists_from_ip = nn.get_distances_from_ip()
dists_to_op = nn.get_distances_to_op()
is_a_cnn = nn.nn_class.startswith('cnn')
end_layer_prob = []
for layer_idx, layer_label in enumerate(nn.layer_labels):
curr_layer_prob = 'assign'
if dists_from_ip[layer_idx] - 1 <= dists_from_ip[start_layer] or \
dists_to_op[layer_idx] + 1 >= dists_to_op[start_layer] or \
layer_label in ['ip', 'op', 'softmax']:
curr_layer_prob = 'no-assign'
elif is_a_cnn and \
nn.post_img_inv_sizes[start_layer] > nn.pre_img_inv_sizes[layer_idx]:
# If the layer has an input image size *larger* than the output of the
# start layer, then do not assign.
curr_layer_prob = 'no-assign'
elif layer_label == 'fc':
# If its a fully connected layer, connect with this only if it is the first
# fc layer.
curr_layer_parent_labels = [nn.layer_labels[x] for x in nn.get_parents(layer_idx)]
if not all([(is_a_pooling_layer_label(clpl) or is_a_conv_layer_label(clpl)) for
clpl in curr_layer_parent_labels]):
curr_layer_prob = 'no-assign'
curr_layer_prob = 0.0 if curr_layer_prob == 'no-assign' else 1.0
end_layer_prob.append(curr_layer_prob)
if sum(end_layer_prob) == 0:
return None
else:
end_layer_prob = np.array(end_layer_prob)
end_layer_prob = end_layer_prob / end_layer_prob.sum()
return end_layer_prob
return
def get_list_of_skipping_modifiers(nn, num_modifiers=None, **kwargs):
""" Returns a list of operators for Neural networks that create branches in the
architecture.
"""
# Define a local function to return the modifier
def _get_skipping_modifier(_start_layer, _end_layer, **_kwargs):
""" Returns a modifier which adds a skip connected from start_layer to end_layer. """
return lambda arg_nn: create_skipped_network(arg_nn, _start_layer, _end_layer,
**_kwargs)
# Some preprocessing
num_modifiers = num_modifiers if num_modifiers is not None else nn.num_layers
max_num_tries = 2 * num_modifiers
start_layer_prob = _get_start_layer_probs_for_branching_and_skipping(nn)
start_layer_prob[0] = 0.0
ret = []
if sum(start_layer_prob) <= 0.0:
return ret # return immediately with an empty list
for _ in range(max_num_tries):
start_layer_prob = start_layer_prob / sum(start_layer_prob)
start_layer = int(np.random.choice(nn.num_layers, 1, p=start_layer_prob))
end_layer_prob = _get_end_layer_probs_for_skipping(nn, start_layer)
if end_layer_prob is None:
continue
end_layer = int(np.random.choice(nn.num_layers, 1, p=end_layer_prob))
ret.append(_get_skipping_modifier(start_layer, end_layer, **kwargs))
if len(ret) >= num_modifiers:
break
return ret
# Swapping -------------------------------------------------------------------------------
def swap_layer_type(nn, layer_idx, replace_with, new_stride):
""" Swaps out the type of layer in layer_idx with replace_with """
if nn.layer_labels[layer_idx] == replace_with:
raise ValueError('Cannot replace layer %d with the same layer type (%s).'%(
layer_idx, replace_with))
layer_labels, num_units_in_each_layer, conn_mat, mandatory_child_attributes = \
get_copies_from_old_nn(nn)
layer_labels[layer_idx] = replace_with
if nn.nn_class == 'cnn':
if is_a_pooling_layer_label(replace_with):
num_units_in_each_layer[layer_idx] = None
mandatory_child_attributes.strides[layer_idx] = new_stride
return get_new_nn(nn, layer_labels, num_units_in_each_layer, conn_mat,
mandatory_child_attributes)
def get_list_of_swap_layer_modifiers(nn, num_modifications='all',
change_stride_with_prob=0.05,
rectifier_swap_candidates=None,
sigmoid_swap_candidates=None):
""" Returns a list of modifiers for swapping a layer with another. """
# pylint: disable=too-many-statements
# Define a local function to return the modifier
def _get_swap_layer_modifier(_layer_idx, _replace_with, _new_stride):
""" Returns a modifier for swapping a layer. """
return lambda arg_nn: swap_layer_type(arg_nn, _layer_idx, _replace_with, _new_stride)
# Preprocessing
if nn.nn_class.startswith('mlp'):
rectifier_swap_candidates = rectifier_swap_candidates if \
rectifier_swap_candidates is not None else _DFLT_RECTIFIER_SWAP
sigmoid_swap_candidates = sigmoid_swap_candidates if \
sigmoid_swap_candidates is not None else _DFLT_SIGMOID_SWAP
# Determine the order of the layers
layer_order = list(range(nn.num_layers))
if num_modifications == 'all' or num_modifications >= nn.num_layers:
num_modifications = nn.num_layers
else:
np.random.shuffle(layer_order)
# iterate through the layers and return
ret = []
for idx in layer_order:
ll = nn.layer_labels[idx]
if ll in ['ip', 'op', 'fc', 'softmax', 'linear']:
continue # don't swap out any of these
# Determine candidates for swapping out
if ll == 'conv3':
candidates = ['res3', 'res5', 'conv5', 'conv7', 'max-pool', 'avg-pool']
cand_probs = [0.25, 0.25, 0.15, 0.25, 0.05, 0.05]
elif ll == 'conv5':
candidates = ['res3', 'res5', 'conv3', 'conv7', 'max-pool', 'avg-pool']
cand_probs = [0.25, 0.25, 0.2, 0.2, 0.05, 0.05]
elif ll == 'conv7':
candidates = ['res3', 'res5', 'conv3', 'conv5', 'max-pool', 'avg-pool']
cand_probs = [0.25, 0.25, 0.25, 0.15, 0.05, 0.05]
elif ll == 'conv9':
candidates = ['res3', 'res5', 'conv3', 'conv5', 'conv7', 'max-pool', 'avg-pool']
cand_probs = [0.2, 0.2, 0.2, 0.2, 0.1, 0.05, 0.05]
elif ll == 'res3':
candidates = ['conv3', 'conv5', 'res5', 'res7', 'max-pool', 'avg-pool']
cand_probs = [0.25, 0.25, 0.15, 0.25, 0.05, 0.05]
elif ll == 'res5':
candidates = ['conv3', 'conv5', 'res3', 'res7', 'max-pool', 'avg-pool']
cand_probs = [0.25, 0.25, 0.2, 0.2, 0.05, 0.05]
elif ll == 'res7':
candidates = ['conv3', 'conv5', 'res3', 'res5', 'max-pool', 'avg-pool']
cand_probs = [0.25, 0.25, 0.25, 0.15, 0.05, 0.05]
elif ll == 'res9':
candidates = ['conv3', 'conv5', 'res3', 'res5', 'res7', 'max-pool', 'avg-pool']
cand_probs = [0.2, 0.2, 0.2, 0.2, 0.1, 0.05, 0.05]
elif ll == 'avg-pool':
candidates = ['max-pool']
cand_probs = None
elif ll == 'max-pool':
candidates = ['avg-pool']
cand_probs = None
elif ll in MLP_RECTIFIERS:
candidates = sigmoid_swap_candidates
cand_probs = None
elif ll in MLP_SIGMOIDS:
candidates = rectifier_swap_candidates
cand_probs = None
else:
raise ValueError('Unidentified layer_type: %s.'%(ll))
# I am determining the probabilities above completely ad-hoc for reasons I don't
# know why.
# Choose replace_with
if cand_probs is not None:
cand_probs = np.array(cand_probs)
cand_probs = cand_probs / cand_probs.sum()
replace_with = np.random.choice(candidates, 1, p=cand_probs)[0]
# Determine the stride
if nn.nn_class == 'cnn':
if is_a_pooling_layer_label(replace_with):
new_stride = None
elif is_a_conv_layer_label(replace_with) and is_a_pooling_layer_label(ll):
new_stride = 2
elif is_a_conv_layer_label(ll) and np.random.random() < change_stride_with_prob:
new_stride = 1 if nn.strides[idx] == 2 else 2
else:
new_stride = nn.strides[idx]
else:
new_stride = None
# Create modifier and append
ret.append(_get_swap_layer_modifier(idx, replace_with, new_stride))
if len(ret) >= num_modifications:
break # Check if you have exceeded the maximum amount
return ret
# Change number of units in a layer
# ========================================================================================
def change_num_units_in_layers(nn, change_layer_idxs, change_layer_vals):
""" Changes the number of units in change_layer_idxs to change_layer_vals. """
layer_labels, num_units_in_each_layer, conn_mat, mandatory_child_attributes = \
get_copies_from_old_nn(nn)
for i, ch_idx in enumerate(change_layer_idxs):
if is_a_pooling_layer_label(layer_labels[ch_idx]):
raise ValueError('Asked to change a pooling layer value. This is not allowed.')
else:
num_units_in_each_layer[ch_idx] = change_layer_vals[i]
return get_new_nn(nn, layer_labels, num_units_in_each_layer, conn_mat,
mandatory_child_attributes)
def _get_directly_modifable_layer_idxs(nn):
""" Returns indices that are directly modifiable in the network. """
if isinstance(nn, ConvNeuralNetwork):
return [i for i in range(nn.num_layers) if (nn.layer_labels[i].startswith('res') or
nn.layer_labels[i].startswith('conv') or
nn.layer_labels[i] == 'fc')]
elif isinstance(nn, MultiLayerPerceptron):
return [i for i in range(nn.num_layers) if nn.layer_labels[i] in
MLP_RECTIFIERS + MLP_SIGMOIDS]
else:
raise ValueError('Unidentified nn type: %s'%(nn.nn_class))
def _get_change_ratio_from_change_frac(change_frac, inc_or_dec):
""" Gets the change ratio from the change fraction, i.e. 1 +/- change_frac depending
on inc_or_dec. """
if inc_or_dec.lower() == 'increase':
return 1 + abs(change_frac)
elif inc_or_dec.lower() == 'decrease':
return 1 - abs(change_frac)
else:
raise ValueError('change_ratio should be one of \'increase\' or \'decrease\'.')
def modify_several_layers(nn, inc_or_dec, layer_group_desc,
change_frac=_DFLT_CHANGE_FRAC):
""" A function to increase or decrease several layers at the same time. inc_or_dec
is a string with values 'increase' or 'decrease' and layer_group_desc is a string
with one of the following values: '1/2', '2/2', '1/4', ... '4/4', '1/8', ... 8/8."""
change_ratio = _get_change_ratio_from_change_frac(change_frac, inc_or_dec)
modifiable_layers = _get_directly_modifable_layer_idxs(nn)
num_modifiable_layers = len(modifiable_layers)
# Now decide which groups to change
num_groups = int(layer_group_desc[-1])
group_idx = int(layer_group_desc[0])
start_idx = (group_idx - 1) * num_modifiable_layers // num_groups
end_idx = group_idx * num_modifiable_layers // num_groups
modify_layer_idxs = modifiable_layers[start_idx:end_idx]
modify_vals = [round(change_ratio * nn.num_units_in_each_layer[i])
for i in modify_layer_idxs]
return change_num_units_in_layers(nn, modify_layer_idxs, modify_vals)
def modify_num_units_on_random_nodes(nn, num_units, inc_or_dec,
change_frac=_DFLT_CHANGE_FRAC):
""" A function to increase or decrase the number of units in a single layer. """
change_ratio = _get_change_ratio_from_change_frac(change_frac, inc_or_dec)
modifiable_layers = _get_directly_modifable_layer_idxs(nn)
modify_layer_idxs = np.random.choice(modifiable_layers,
max(num_units, modifiable_layers))
modify_vals = [round(change_ratio * nn.num_units_in_each_layer[i])
for i in modify_layer_idxs]
return change_num_units_in_layers(nn, modify_layer_idxs, modify_vals)
def _get_candidate_layers_for_modifying_num_units(nn, num_candidates='all'):
""" Returns a set of candidate layers for modifying the number of units. """
modifiable_layers = _get_directly_modifable_layer_idxs(nn)
if num_candidates == 'all' or num_candidates >= len(modifiable_layers):
return modifiable_layers
else:
np.random.shuffle(modifiable_layers)
return modifiable_layers[:num_candidates]
def get_list_of_single_layer_modifiers(old_nn, inc_or_dec, num_layers_to_modify='all',
change_frac=_DFLT_CHANGE_FRAC):
""" Returns a list of primitives which change old_nn in one layer. """
# Define a local function to obtain the modifier
def _get_modifier(_ltm, _change_val):
""" Gets a modifier with the current values of ltm and change_val. """
return lambda nn: change_num_units_in_layers(nn, [_ltm], [_change_val])
# The problem with doing ret.append(lamda nn ....(nn, ltm, change_val)) is that python
# uses the current values of the variables ltm and change_val when calling - and this
# turns out to be the last value in layers_to_modify. i.e. Python looks up the
# variable name at the time the function is called, not when it is created.
# See this:
# https://stackoverflow.com/questions/10452770/python-lambdas-binding-to-local-values
change_ratio = _get_change_ratio_from_change_frac(change_frac, inc_or_dec)
layers_to_modify = _get_candidate_layers_for_modifying_num_units(old_nn,
num_layers_to_modify)
ret = []
for ltm in layers_to_modify:
change_val = round(change_ratio * old_nn.num_units_in_each_layer[ltm])
ret.append(_get_modifier(ltm, change_val))
return ret
# Define the following for convenience
# Increase num units en masse
increase_en_masse_1_2 = lambda nn, *a: modify_several_layers(nn, 'increase', '1/2', *a)
increase_en_masse_2_2 = lambda nn, *a: modify_several_layers(nn, 'increase', '2/2', *a)
increase_en_masse_1_4 = lambda nn, *a: modify_several_layers(nn, 'increase', '1/4', *a)
increase_en_masse_2_4 = lambda nn, *a: modify_several_layers(nn, 'increase', '2/4', *a)
increase_en_masse_3_4 = lambda nn, *a: modify_several_layers(nn, 'increase', '3/4', *a)
increase_en_masse_4_4 = lambda nn, *a: modify_several_layers(nn, 'increase', '4/4', *a)
increase_en_masse_1_8 = lambda nn, *a: modify_several_layers(nn, 'increase', '1/8', *a)
increase_en_masse_2_8 = lambda nn, *a: modify_several_layers(nn, 'increase', '2/8', *a)
increase_en_masse_3_8 = lambda nn, *a: modify_several_layers(nn, 'increase', '3/8', *a)
increase_en_masse_4_8 = lambda nn, *a: modify_several_layers(nn, 'increase', '4/8', *a)
increase_en_masse_5_8 = lambda nn, *a: modify_several_layers(nn, 'increase', '5/8', *a)
increase_en_masse_6_8 = lambda nn, *a: modify_several_layers(nn, 'increase', '6/8', *a)
increase_en_masse_7_8 = lambda nn, *a: modify_several_layers(nn, 'increase', '7/8', *a)
increase_en_masse_8_8 = lambda nn, *a: modify_several_layers(nn, 'increase', '8/8', *a)
# Decrease num units en masse
decrease_en_masse_1_2 = lambda nn, *a: modify_several_layers(nn, 'decrease', '1/2', *a)
decrease_en_masse_2_2 = lambda nn, *a: modify_several_layers(nn, 'decrease', '2/2', *a)
decrease_en_masse_1_4 = lambda nn, *a: modify_several_layers(nn, 'decrease', '1/4', *a)
decrease_en_masse_2_4 = lambda nn, *a: modify_several_layers(nn, 'decrease', '2/4', *a)
decrease_en_masse_3_4 = lambda nn, *a: modify_several_layers(nn, 'decrease', '3/4', *a)
decrease_en_masse_4_4 = lambda nn, *a: modify_several_layers(nn, 'decrease', '4/4', *a)
decrease_en_masse_1_8 = lambda nn, *a: modify_several_layers(nn, 'decrease', '1/8', *a)
decrease_en_masse_2_8 = lambda nn, *a: modify_several_layers(nn, 'decrease', '2/8', *a)
decrease_en_masse_3_8 = lambda nn, *a: modify_several_layers(nn, 'decrease', '3/8', *a)
decrease_en_masse_4_8 = lambda nn, *a: modify_several_layers(nn, 'decrease', '4/8', *a)
decrease_en_masse_5_8 = lambda nn, *a: modify_several_layers(nn, 'decrease', '5/8', *a)
decrease_en_masse_6_8 = lambda nn, *a: modify_several_layers(nn, 'decrease', '6/8', *a)
decrease_en_masse_7_8 = lambda nn, *a: modify_several_layers(nn, 'decrease', '7/8', *a)
decrease_en_masse_8_8 = lambda nn, *a: modify_several_layers(nn, 'decrease', '8/8', *a)
def get_list_of_en_masse_change_primitives(nn, inc_or_dec='incdec'):
""" Returns the list of primitives which changes the number of units en masse. """
ret = []
# Change 1/2
if nn.num_internal_layers >= 4:
if 'inc' in inc_or_dec:
ret.extend([increase_en_masse_1_2, # increase
increase_en_masse_2_2,
])
if 'dec' in inc_or_dec:
ret.extend([decrease_en_masse_1_2, # decrease
decrease_en_masse_2_2,
])
# Change 1/4
if nn.num_internal_layers >= 8:
if 'inc' in inc_or_dec:
ret.extend([increase_en_masse_1_4, # increase
increase_en_masse_2_4,
increase_en_masse_3_4,
increase_en_masse_4_4,
])
if 'dec' in inc_or_dec:
ret.extend([decrease_en_masse_1_4, # decrease
decrease_en_masse_2_4,
decrease_en_masse_3_4,
decrease_en_masse_4_4,
])
# Change 1/8
if nn.num_internal_layers >= 16:
if 'inc' in inc_or_dec:
ret.extend([increase_en_masse_1_8, # increase
increase_en_masse_2_8,
increase_en_masse_3_8,
increase_en_masse_4_8,
increase_en_masse_5_8,
increase_en_masse_6_8,
increase_en_masse_7_8,
increase_en_masse_8_8,
])
if 'dec' in inc_or_dec:
ret.extend([decrease_en_masse_1_8, # decrease
decrease_en_masse_2_8,
decrease_en_masse_3_8,
decrease_en_masse_4_8,
decrease_en_masse_5_8,
decrease_en_masse_6_8,
decrease_en_masse_7_8,
decrease_en_masse_8_8,
])
return ret
# A class to put it all together
# ========================================================================================
class NNModifier(object):
""" A class for modifying a neural nework using many of the operations above. """
def __init__(self, constraint_checker=None, options=None, reporter=None):
""" Constructor. """
self.reporter = get_reporter(reporter)
self.constraint_checker = constraint_checker
if options is None:
options = load_options(nn_modifier_args)
self.options = options
def __call__(self, list_of_nns, num_modifications, num_steps_probs, max_num_steps=None,
**kwargs):
""" Takes a list of neural network nns and applies the library of changes to it to
produce a list of modifications.
num_steps_probs is a list of probabilities that indicate with what probability
we want to use a certain number of steps.
"""
# Preprocessing
if not hasattr(list_of_nns, '__iter__'):
list_of_nns = [list_of_nns]
# determine num_steps_probs
if num_steps_probs is None and isinstance(max_num_steps, (int, float)):
num_steps_probs = np.ones((max_num_steps,))/float(max_num_steps)
elif isinstance(num_steps_probs, (int, float)):
num_steps_probs = np.zeros((num_steps_probs,))
num_steps_probs[-1] = 1.0
# Determine how many modifications per nn
nn_idxs = list(range(len(list_of_nns)))
if hasattr(num_modifications, '__iter__'):
num_modifs_for_each_nn = num_modifications
else:
nn_choices_for_modif = np.random.choice(nn_idxs, num_modifications, replace=True)
num_modifs_for_each_nn = [np.sum(nn_choices_for_modif == i) for i in nn_idxs]
# Create a list
ret = []
for idx in nn_idxs:
ret.extend(self.get_modifications_for_a_single_nn(list_of_nns[idx],
num_modifs_for_each_nn[idx], num_steps_probs, **kwargs))
return ret
def get_modifications_for_a_single_nn(self, nn, num_modifications, num_steps_probs,
**kwargs):
""" Takes a neural network nn and applies the library of changes to it to produce
a list of modifiers.
num_steps_probs is a list of probabilities that indicate with what probability
we want to use a certain number of steps.
"""
max_num_steps = len(num_steps_probs)
num_step_choices = np.random.choice(list(range(max_num_steps)), num_modifications,
replace=True, p=num_steps_probs)
num_modifs_per_step = [np.sum(num_step_choices == i) for i in range(max_num_steps)]
ret = []
for num_step_val_minus1, num_modifs_this_step in enumerate(num_modifs_per_step):
if num_modifs_this_step == 0:
continue
new_nns = self.get_multi_step_modifications(nn, num_step_val_minus1 + 1,
num_modifs_this_step, **kwargs)
ret.extend(new_nns)
return ret
@classmethod
def _get_num_modifications(cls, list_of_opers, passed_num_modifs, dflt_num_modifs):
""" Returns the number of modifications depending on the passed value. """
if passed_num_modifs == 'all':
return len(list_of_opers)
elif passed_num_modifs is None:
return dflt_num_modifs
elif passed_num_modifs >= 0:
return passed_num_modifs
else:
raise ValueError('num_*_step_modifications should be \'all\', ' +
'None or a positive integer. ')
def _is_a_valid_network(self, nn):
""" Returns true if it is a valid network. """
if nn is None:
return False
elif self.constraint_checker is None:
return True
else:
return self.constraint_checker(nn)
def get_primitives_grouped_by_type(self, nn, types_of_primitives=None):
""" Returns the list of primitives grouped by type. """
# pylint: disable=bare-except
# doing a bare except only for testing. Will remove soon.
types_of_primitives = types_of_primitives if types_of_primitives is not None \
else _PRIMITIVE_PROB_MASSES.keys()
ret = {}
primitive_type_prob_masses = {}
for top in types_of_primitives:
top_is_of_known_type = True
try:
if top == 'inc_single':
ret[top] = get_list_of_single_layer_modifiers(nn, 'increase',
self.options.spawn_single_inc_num_units,
self.options.single_inc_change_frac)
elif top == 'dec_single':
ret[top] = get_list_of_single_layer_modifiers(nn, 'decrease',
self.options.spawn_single_dec_num_units,
self.options.single_dec_change_frac)
elif top == 'inc_en_masse':
ret[top] = get_list_of_en_masse_change_primitives(nn, 'inc')
elif top == 'dec_en_masse':
ret[top] = get_list_of_en_masse_change_primitives(nn, 'dec')
elif top == 'swap_layer':
ret[top] = get_list_of_swap_layer_modifiers(nn)
elif top == 'wedge_layer':
ret[top] = get_list_of_wedge_layer_modifiers(nn)
elif top == 'remove_layer':
ret[top] = get_list_of_remove_layer_modifiers(nn)
elif top == 'branch':
ret[top] = get_list_of_branching_modifiers(nn)
elif top == 'skip':
ret[top] = get_list_of_skipping_modifiers(nn)
else:
top_is_of_known_type = False
except:
pass
if not top_is_of_known_type:
raise ValueError('All values in types_of_primitives should be in %s. Given %s'%(
str(_PRIMITIVE_PROB_MASSES.keys()), top))
# Finally also add the probability mass
primitive_type_prob_masses[top] = _PRIMITIVE_PROB_MASSES[top]
return ret, primitive_type_prob_masses
def get_single_step_modifications(self, nn, num_single_step_modifications='all',
**kwargs):
""" Returns a list of new neural networks which have undergone a single modification
from nn. """
prims_by_type, type_prob_masses = self.get_primitives_grouped_by_type(nn, **kwargs)
groups = list(prims_by_type.keys())
if num_single_step_modifications == 'all':
prims_by_group = prims_by_type.values()
modifiers = [elem for group_prims in prims_by_group for elem in group_prims]
num_single_step_modifications = len(modifiers)
else:
prob_masses = np.array([type_prob_masses[key] for key in groups])
prob_masses = prob_masses/prob_masses.sum()
modif_groups = np.random.choice(groups, max(2*num_single_step_modifications, 20),
p=prob_masses)
# Shuffle each list
for grp in groups:
np.random.shuffle(prims_by_type[grp])
# Now create a list of modifiers
modifiers = []
for grp in modif_groups: