INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
|---|---|
Generate the scenario. The scenario - object ( smac. scenario. scenario. Scenario ) is used to configure SMAC and can be constructed either by providing an actual scenario - object or by specifing the options in a scenario file. Reference: https:// automl. github. io/ SMAC3/ stable/ options. html | def generate_scenario(ss_content):
"""Generate the scenario. The scenario-object (smac.scenario.scenario.Scenario) is used to configure SMAC and
can be constructed either by providing an actual scenario-object, or by specifing the options in a scenario file.
Reference: https://automl.github.io/SMAC3/s... |
Load or create dataset | def load_data(train_path='./data/regression.train', test_path='./data/regression.test'):
'''
Load or create dataset
'''
print('Load data...')
df_train = pd.read_csv(train_path, header=None, sep='\t')
df_test = pd.read_csv(test_path, header=None, sep='\t')
num = len(df_train)
split_num = ... |
The distance between two layers. | def layer_distance(a, b):
"""The distance between two layers."""
# pylint: disable=unidiomatic-typecheck
if type(a) != type(b):
return 1.0
if is_layer(a, "Conv"):
att_diff = [
(a.filters, b.filters),
(a.kernel_size, b.kernel_size),
(a.stride, b.stride)... |
The attribute distance. | def attribute_difference(att_diff):
''' The attribute distance.
'''
ret = 0
for a_value, b_value in att_diff:
if max(a_value, b_value) == 0:
ret += 0
else:
ret += abs(a_value - b_value) * 1.0 / max(a_value, b_value)
return ret * 1.0 / len(att_diff) |
The distance between the layers of two neural networks. | def layers_distance(list_a, list_b):
"""The distance between the layers of two neural networks."""
len_a = len(list_a)
len_b = len(list_b)
f = np.zeros((len_a + 1, len_b + 1))
f[-1][-1] = 0
for i in range(-1, len_a):
f[i][-1] = i + 1
for j in range(-1, len_b):
f[-1][j] = j + ... |
The distance between two skip - connections. | def skip_connection_distance(a, b):
"""The distance between two skip-connections."""
if a[2] != b[2]:
return 1.0
len_a = abs(a[1] - a[0])
len_b = abs(b[1] - b[0])
return (abs(a[0] - b[0]) + abs(len_a - len_b)) / (max(a[0], b[0]) + max(len_a, len_b)) |
The distance between the skip - connections of two neural networks. | def skip_connections_distance(list_a, list_b):
"""The distance between the skip-connections of two neural networks."""
distance_matrix = np.zeros((len(list_a), len(list_b)))
for i, a in enumerate(list_a):
for j, b in enumerate(list_b):
distance_matrix[i][j] = skip_connection_distance(a, ... |
The distance between two neural networks. Args: x: An instance of NetworkDescriptor. y: An instance of NetworkDescriptor Returns: The edit - distance between x and y. | def edit_distance(x, y):
"""The distance between two neural networks.
Args:
x: An instance of NetworkDescriptor.
y: An instance of NetworkDescriptor
Returns:
The edit-distance between x and y.
"""
ret = layers_distance(x.layers, y.layers)
ret += Constant.KERNEL_LAMBDA * ... |
Calculate the edit distance. Args: train_x: A list of neural architectures. train_y: A list of neural architectures. Returns: An edit - distance matrix. | def edit_distance_matrix(train_x, train_y=None):
"""Calculate the edit distance.
Args:
train_x: A list of neural architectures.
train_y: A list of neural architectures.
Returns:
An edit-distance matrix.
"""
if train_y is None:
ret = np.zeros((train_x.shape[0], train_x... |
The Euclidean distance between two vectors. | def vector_distance(a, b):
"""The Euclidean distance between two vectors."""
a = np.array(a)
b = np.array(b)
return np.linalg.norm(a - b) |
Use Bourgain algorithm to embed the neural architectures based on their edit - distance. Args: distance_matrix: A matrix of edit - distances. Returns: A matrix of distances after embedding. | def bourgain_embedding_matrix(distance_matrix):
"""Use Bourgain algorithm to embed the neural architectures based on their edit-distance.
Args:
distance_matrix: A matrix of edit-distances.
Returns:
A matrix of distances after embedding.
"""
distance_matrix = np.array(distance_matrix)... |
Check if the target descriptor is in the descriptors. | def contain(descriptors, target_descriptor):
"""Check if the target descriptor is in the descriptors."""
for descriptor in descriptors:
if edit_distance(descriptor, target_descriptor) < 1e-5:
return True
return False |
Fit the regressor with more data. Args: train_x: A list of NetworkDescriptor. train_y: A list of metric values. | def fit(self, train_x, train_y):
""" Fit the regressor with more data.
Args:
train_x: A list of NetworkDescriptor.
train_y: A list of metric values.
"""
if self.first_fitted:
self.incremental_fit(train_x, train_y)
else:
self.first_f... |
Incrementally fit the regressor. | def incremental_fit(self, train_x, train_y):
""" Incrementally fit the regressor. """
if not self._first_fitted:
raise ValueError("The first_fit function needs to be called first.")
train_x, train_y = np.array(train_x), np.array(train_y)
# Incrementally compute K
up... |
Fit the regressor for the first time. | def first_fit(self, train_x, train_y):
""" Fit the regressor for the first time. """
train_x, train_y = np.array(train_x), np.array(train_y)
self._x = np.copy(train_x)
self._y = np.copy(train_y)
self._distance_matrix = edit_distance_matrix(self._x)
k_matrix = bourgain_e... |
Predict the result. Args: train_x: A list of NetworkDescriptor. Returns: y_mean: The predicted mean. y_std: The predicted standard deviation. | def predict(self, train_x):
"""Predict the result.
Args:
train_x: A list of NetworkDescriptor.
Returns:
y_mean: The predicted mean.
y_std: The predicted standard deviation.
"""
k_trans = np.exp(-np.power(edit_distance_matrix(train_x, self._x), ... |
Generate new architecture. Args: descriptors: All the searched neural architectures. Returns: graph: An instance of Graph. A morphed neural network with weights. father_id: The father node ID in the search tree. | def generate(self, descriptors):
"""Generate new architecture.
Args:
descriptors: All the searched neural architectures.
Returns:
graph: An instance of Graph. A morphed neural network with weights.
father_id: The father node ID in the search tree.
"""
... |
estimate the value of generated graph | def acq(self, graph):
''' estimate the value of generated graph
'''
mean, std = self.gpr.predict(np.array([graph.extract_descriptor()]))
if self.optimizemode is OptimizeMode.Maximize:
return mean + self.beta * std
return mean - self.beta * std |
add child to search tree itself. Arguments: u { int } -- father id v { int } -- child id | def add_child(self, u, v):
''' add child to search tree itself.
Arguments:
u {int} -- father id
v {int} -- child id
'''
if u == -1:
self.root = v
self.adj_list[v] = []
return
if v not in self.adj_list[u]:
s... |
A recursive function to return the content of the tree in a dict. | def get_dict(self, u=None):
""" A recursive function to return the content of the tree in a dict."""
if u is None:
return self.get_dict(self.root)
children = []
for v in self.adj_list[u]:
children.append(self.get_dict(v))
ret = {"name": u, "children": chil... |
Train a network from a specific graph. | def train_with_graph(p_graph, qp_pairs, dev_qp_pairs):
'''
Train a network from a specific graph.
'''
global sess
with tf.Graph().as_default():
train_model = GAG(cfg, embed, p_graph)
train_model.build_net(is_training=True)
tf.get_variable_scope().reuse_variables()
dev... |
Returns multiple sets of trial ( hyper - ) parameters as iterable of serializable objects. Call generate_parameters () by count times by default. User code must override either this function or generate_parameters (). If there s no more trial user should raise nni. NoMoreTrialError exception in generate_parameters (). ... | def generate_multiple_parameters(self, parameter_id_list):
"""Returns multiple sets of trial (hyper-)parameters, as iterable of serializable objects.
Call 'generate_parameters()' by 'count' times by default.
User code must override either this function or 'generate_parameters()'.
If ther... |
Load graph | def graph_loads(graph_json):
'''
Load graph
'''
layers = []
for layer in graph_json['layers']:
layer_info = Layer(layer['graph_type'], layer['input'], layer['output'], layer['size'], layer['hash_id'])
layer_info.is_delete = layer['is_delete']
_logger.debug('append layer {}'.f... |
Calculation of hash_id of Layer. Which is determined by the properties of itself and the hash_id s of input layers | def update_hash(self, layers: Iterable):
"""
Calculation of `hash_id` of Layer. Which is determined by the properties of itself, and the `hash_id`s of input layers
"""
if self.graph_type == LayerType.input.value:
return
hasher = hashlib.md5()
hasher.update(Lay... |
update hash id of each layer in topological order/ recursively hash id will be used in weight sharing | def update_hash(self):
"""
update hash id of each layer, in topological order/recursively
hash id will be used in weight sharing
"""
_logger.debug('update hash')
layer_in_cnt = [len(layer.input) for layer in self.layers]
topo_queue = deque([i for i, layer in enume... |
Initialize root logger. This will redirect anything from logging. getLogger () as well as stdout to specified file. logger_file_path: path of logger file ( path - like object ). | def init_logger(logger_file_path, log_level_name='info'):
"""Initialize root logger.
This will redirect anything from logging.getLogger() as well as stdout to specified file.
logger_file_path: path of logger file (path-like object).
"""
log_level = log_level_map.get(log_level_name, logging.INFO)
... |
Create simple convolutional model | def create_mnist_model(hyper_params, input_shape=(H, W, 1), num_classes=NUM_CLASSES):
'''
Create simple convolutional model
'''
layers = [
Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D(pool_size=(2,... |
Load MNIST dataset | def load_mnist_data(args):
'''
Load MNIST dataset
'''
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = (np.expand_dims(x_train, -1).astype(np.float) / 255.)[:args.num_train]
x_test = (np.expand_dims(x_test, -1).astype(np.float) / 255.)[:args.num_test]
y_train = keras.utils... |
Train model | def train(args, params):
'''
Train model
'''
x_train, y_train, x_test, y_test = load_mnist_data(args)
model = create_mnist_model(params)
# nni
model.fit(x_train, y_train, batch_size=args.batch_size, epochs=args.epochs, verbose=1,
validation_data=(x_test, y_test), callbacks=[SendMet... |
Run on end of each epoch | def on_epoch_end(self, epoch, logs={}):
'''
Run on end of each epoch
'''
LOG.debug(logs)
nni.report_intermediate_result(logs["val_acc"]) |
get all of config values | def get_all_config(self):
'''get all of config values'''
return json.dumps(self.config, indent=4, sort_keys=True, separators=(',', ':')) |
set { key: value } paris to self. config | def set_config(self, key, value):
'''set {key:value} paris to self.config'''
self.config = self.read_file()
self.config[key] = value
self.write_file() |
save config to local file | def write_file(self):
'''save config to local file'''
if self.config:
try:
with open(self.config_file, 'w') as file:
json.dump(self.config, file)
except IOError as error:
print('Error:', error)
return |
set { key: value } paris to self. experiment | def add_experiment(self, id, port, time, file_name, platform):
'''set {key:value} paris to self.experiment'''
self.experiments[id] = {}
self.experiments[id]['port'] = port
self.experiments[id]['startTime'] = time
self.experiments[id]['endTime'] = 'N/A'
self.experiments[id... |
Update experiment | def update_experiment(self, id, key, value):
'''Update experiment'''
if id not in self.experiments:
return False
self.experiments[id][key] = value
self.write_file()
return True |
remove an experiment by id | def remove_experiment(self, id):
'''remove an experiment by id'''
if id in self.experiments:
self.experiments.pop(id)
self.write_file() |
save config to local file | def write_file(self):
'''save config to local file'''
try:
with open(self.experiment_file, 'w') as file:
json.dump(self.experiments, file)
except IOError as error:
print('Error:', error)
return |
load config from local file | def read_file(self):
'''load config from local file'''
if os.path.exists(self.experiment_file):
try:
with open(self.experiment_file, 'r') as file:
return json.load(file)
except ValueError:
return {}
return {} |
load data from file | def load_from_file(path, fmt=None, is_training=True):
'''
load data from file
'''
if fmt is None:
fmt = 'squad'
assert fmt in ['squad', 'csv'], 'input format must be squad or csv'
qp_pairs = []
if fmt == 'squad':
with open(path) as data_file:
data = json.load(data... |
tokenize function. | def tokenize(qp_pair, tokenizer=None, is_training=False):
'''
tokenize function.
'''
question_tokens = tokenizer.tokenize(qp_pair['question'])
passage_tokens = tokenizer.tokenize(qp_pair['passage'])
if is_training:
question_tokens = question_tokens[:300]
passage_tokens = passage_... |
Build the vocab from corpus. | def collect_vocab(qp_pairs):
'''
Build the vocab from corpus.
'''
vocab = set()
for qp_pair in qp_pairs:
for word in qp_pair['question_tokens']:
vocab.add(word['word'])
for word in qp_pair['passage_tokens']:
vocab.add(word['word'])
return vocab |
Shuffle the step | def shuffle_step(entries, step):
'''
Shuffle the step
'''
answer = []
for i in range(0, len(entries), step):
sub = entries[i:i+step]
shuffle(sub)
answer += sub
return answer |
Get batches data and shuffle. | def get_batches(qp_pairs, batch_size, need_sort=True):
'''
Get batches data and shuffle.
'''
if need_sort:
qp_pairs = sorted(qp_pairs, key=lambda qp: (
len(qp['passage_tokens']), qp['id']), reverse=True)
batches = [{'qp_pairs': qp_pairs[i:(i + batch_size)]}
for i i... |
Get char input. | def get_char_input(data, char_dict, max_char_length):
'''
Get char input.
'''
batch_size = len(data)
sequence_length = max(len(d) for d in data)
char_id = np.zeros((max_char_length, sequence_length,
batch_size), dtype=np.int32)
char_lengths = np.zeros((sequence_length... |
Get word input. | def get_word_input(data, word_dict, embed, embed_dim):
'''
Get word input.
'''
batch_size = len(data)
max_sequence_length = max(len(d) for d in data)
sequence_length = max_sequence_length
word_input = np.zeros((max_sequence_length, batch_size,
embed_dim), dtype=np.... |
Given word return word index. | def get_word_index(tokens, char_index):
'''
Given word return word index.
'''
for (i, token) in enumerate(tokens):
if token['char_end'] == 0:
continue
if token['char_begin'] <= char_index and char_index <= token['char_end']:
return i
return 0 |
Get answer s index of begin and end. | def get_answer_begin_end(data):
'''
Get answer's index of begin and end.
'''
begin = []
end = []
for qa_pair in data:
tokens = qa_pair['passage_tokens']
char_begin = qa_pair['answer_begin']
char_end = qa_pair['answer_end']
word_begin = get_word_index(tokens, char_... |
Get bucket by length. | def get_buckets(min_length, max_length, bucket_count):
'''
Get bucket by length.
'''
if bucket_count <= 0:
return [max_length]
unit_length = int((max_length - min_length) // (bucket_count))
buckets = [min_length + unit_length *
(i + 1) for i in range(0, bucket_count)]
... |
tokenize function in Tokenizer. | def tokenize(self, text):
'''
tokenize function in Tokenizer.
'''
start = -1
tokens = []
for i, character in enumerate(text):
if character == ' ' or character == '\t':
if start >= 0:
word = text[start:i]
... |
generate new id and event hook for new Individual | def generate_new_id(self):
"""
generate new id and event hook for new Individual
"""
self.events.append(Event())
indiv_id = self.indiv_counter
self.indiv_counter += 1
return indiv_id |
initialize populations for evolution tuner | def init_population(self, population_size, graph_max_layer, graph_min_layer):
"""
initialize populations for evolution tuner
"""
population = []
graph = Graph(max_layer_num=graph_max_layer, min_layer_num=graph_min_layer,
inputs=[Layer(LayerType.input.value, ... |
Returns a set of trial graph config as a serializable object. An example configuration: json { shared_id: [ 4a11b2ef9cb7211590dfe81039b27670 370af04de24985e5ea5b3d72b12644c9 11f646e9f650f5f3fedc12b6349ec60f 0604e5350b9c734dd2d770ee877cfb26 6dbeb8b022083396acb721267335f228 ba55380d6c84f5caeb87155d1c5fa654 ] graph: { lay... | def generate_parameters(self, parameter_id):
"""Returns a set of trial graph config, as a serializable object.
An example configuration:
```json
{
"shared_id": [
"4a11b2ef9cb7211590dfe81039b27670",
"370af04de24985e5ea5b3d72b12644c9",
... |
Record an observation of the objective function parameter_id: int parameters: dict of parameters value: final metrics of the trial including reward | def receive_trial_result(self, parameter_id, parameters, value):
'''
Record an observation of the objective function
parameter_id : int
parameters : dict of parameters
value: final metrics of the trial, including reward
'''
logger.debug('acquiring lock for param {... |
update data | def _update_data(self, trial_job_id, trial_history):
"""update data
Parameters
----------
trial_job_id: int
trial job id
trial_history: list
The history performance matrix of each trial
"""
if trial_job_id not in self.running_history:
... |
trial_end Parameters ---------- trial_job_id: int trial job id success: bool True if succssfully finish the experiment False otherwise | def trial_end(self, trial_job_id, success):
"""trial_end
Parameters
----------
trial_job_id: int
trial job id
success: bool
True if succssfully finish the experiment, False otherwise
"""
if trial_job_id in self.running_history:
... |
assess_trial Parameters ---------- trial_job_id: int trial job id trial_history: list The history performance matrix of each trial | def assess_trial(self, trial_job_id, trial_history):
"""assess_trial
Parameters
----------
trial_job_id: int
trial job id
trial_history: list
The history performance matrix of each trial
Returns
-------
bool
As... |
Copy directory from HDFS to local | def copyHdfsDirectoryToLocal(hdfsDirectory, localDirectory, hdfsClient):
'''Copy directory from HDFS to local'''
if not os.path.exists(localDirectory):
os.makedirs(localDirectory)
try:
listing = hdfsClient.list_status(hdfsDirectory)
except Exception as exception:
nni_log(LogType.... |
Copy file from HDFS to local | def copyHdfsFileToLocal(hdfsFilePath, localFilePath, hdfsClient, override=True):
'''Copy file from HDFS to local'''
if not hdfsClient.exists(hdfsFilePath):
raise Exception('HDFS file {} does not exist!'.format(hdfsFilePath))
try:
file_status = hdfsClient.get_file_status(hdfsFilePath)
... |
Copy directory from local to HDFS | def copyDirectoryToHdfs(localDirectory, hdfsDirectory, hdfsClient):
'''Copy directory from local to HDFS'''
if not os.path.exists(localDirectory):
raise Exception('Local Directory does not exist!')
hdfsClient.mkdirs(hdfsDirectory)
result = True
for file in os.listdir(localDirectory):
... |
Copy a local file to HDFS directory | def copyFileToHdfs(localFilePath, hdfsFilePath, hdfsClient, override=True):
'''Copy a local file to HDFS directory'''
if not os.path.exists(localFilePath):
raise Exception('Local file Path does not exist!')
if os.path.isdir(localFilePath):
raise Exception('localFile should not a directory!')... |
Load dataset use boston dataset | def load_data():
'''Load dataset, use boston dataset'''
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=99, test_size=0.25)
#normalize data
ss_X = StandardScaler()
ss_y = StandardScaler()
X_train = ss_X.fit_transform(X_trai... |
Get model according to parameters | def get_model(PARAMS):
'''Get model according to parameters'''
model_dict = {
'LinearRegression': LinearRegression(),
'SVR': SVR(),
'KNeighborsRegressor': KNeighborsRegressor(),
'DecisionTreeRegressor': DecisionTreeRegressor()
}
if not model_dict.get(PARAMS['model_name'])... |
Train model and predict result | def run(X_train, X_test, y_train, y_test, PARAMS):
'''Train model and predict result'''
model.fit(X_train, y_train)
predict_y = model.predict(X_test)
score = r2_score(y_test, predict_y)
LOG.debug('r2 score: %s' % score)
nni.report_final_result(score) |
Add a skip - connection to the descriptor. Args: u: Number of convolutional layers before the starting point. v: Number of convolutional layers before the ending point. connection_type: Must be either CONCAT_CONNECT or ADD_CONNECT. | def add_skip_connection(self, u, v, connection_type):
""" Add a skip-connection to the descriptor.
Args:
u: Number of convolutional layers before the starting point.
v: Number of convolutional layers before the ending point.
connection_type: Must be either CONCAT_CONN... |
NetworkDescriptor to json representation | def to_json(self):
''' NetworkDescriptor to json representation
'''
skip_list = []
for u, v, connection_type in self.skip_connections:
skip_list.append({"from": u, "to": v, "type": connection_type})
return {"node_list": self.layers, "skip_list": skip_list} |
Add a layer to the Graph. Args: layer: An instance of the subclasses of StubLayer in layers. py. input_node_id: An integer. The ID of the input node of the layer. Returns: output_node_id: An integer. The ID of the output node of the layer. | def add_layer(self, layer, input_node_id):
"""Add a layer to the Graph.
Args:
layer: An instance of the subclasses of StubLayer in layers.py.
input_node_id: An integer. The ID of the input node of the layer.
Returns:
output_node_id: An integer. The ID of the o... |
Add a new node to node_list and give the node an ID. Args: node: An instance of Node. Returns: node_id: An integer. | def _add_node(self, node):
"""Add a new node to node_list and give the node an ID.
Args:
node: An instance of Node.
Returns:
node_id: An integer.
"""
node_id = len(self.node_list)
self.node_to_id[node] = node_id
self.node_list.append(node)
... |
Add a new layer to the graph. The nodes should be created in advance. | def _add_edge(self, layer, input_id, output_id):
"""Add a new layer to the graph. The nodes should be created in advance."""
if layer in self.layer_to_id:
layer_id = self.layer_to_id[layer]
if input_id not in self.layer_id_to_input_node_ids[layer_id]:
self.layer_... |
Redirect the layer to a new node. Change the edge originally from u_id to v_id into an edge from u_id to new_v_id while keeping all other property of the edge the same. | def _redirect_edge(self, u_id, v_id, new_v_id):
"""Redirect the layer to a new node.
Change the edge originally from `u_id` to `v_id` into an edge from `u_id` to `new_v_id`
while keeping all other property of the edge the same.
"""
layer_id = None
for index, edge_tuple in... |
Replace the layer with a new layer. | def _replace_layer(self, layer_id, new_layer):
"""Replace the layer with a new layer."""
old_layer = self.layer_list[layer_id]
new_layer.input = old_layer.input
new_layer.output = old_layer.output
new_layer.output.shape = new_layer.output_shape
self.layer_list[layer_id] =... |
Return the topological order of the node IDs from the input node to the output node. | def topological_order(self):
"""Return the topological order of the node IDs from the input node to the output node."""
q = Queue()
in_degree = {}
for i in range(self.n_nodes):
in_degree[i] = 0
for u in range(self.n_nodes):
for v, _ in self.adj_list[u]:
... |
Given two node IDs return all the pooling layers between them. | def _get_pooling_layers(self, start_node_id, end_node_id):
"""Given two node IDs, return all the pooling layers between them."""
layer_list = []
node_list = [start_node_id]
assert self._depth_first_search(end_node_id, layer_list, node_list)
ret = []
for layer_id in layer_... |
Search for all the layers and nodes down the path. A recursive function to search all the layers and nodes between the node in the node_list and the node with target_id. | def _depth_first_search(self, target_id, layer_id_list, node_list):
"""Search for all the layers and nodes down the path.
A recursive function to search all the layers and nodes between the node in the node_list
and the node with target_id."""
assert len(node_list) <= self.n_nodes
... |
Search the graph for all the layers to be widened caused by an operation. It is an recursive function with duplication check to avoid deadlock. It searches from a starting node u until the corresponding layers has been widened. Args: u: The starting node ID. start_dim: The position to insert the additional dimensions. ... | def _search(self, u, start_dim, total_dim, n_add):
"""Search the graph for all the layers to be widened caused by an operation.
It is an recursive function with duplication check to avoid deadlock.
It searches from a starting node u until the corresponding layers has been widened.
Args:
... |
Insert a relu - conv - bn block after the target block. Args: target_id: A convolutional layer ID. The new block should be inserted after the block. new_layer: An instance of StubLayer subclasses. | def to_deeper_model(self, target_id, new_layer):
"""Insert a relu-conv-bn block after the target block.
Args:
target_id: A convolutional layer ID. The new block should be inserted after the block.
new_layer: An instance of StubLayer subclasses.
"""
self.operation_... |
Widen the last dimension of the output of the pre_layer. Args: pre_layer_id: The ID of a convolutional layer or dense layer. n_add: The number of dimensions to add. | def to_wider_model(self, pre_layer_id, n_add):
"""Widen the last dimension of the output of the pre_layer.
Args:
pre_layer_id: The ID of a convolutional layer or dense layer.
n_add: The number of dimensions to add.
"""
self.operation_history.append(("to_wider_mode... |
Insert the new_layers after the node with start_node_id. | def _insert_new_layers(self, new_layers, start_node_id, end_node_id):
"""Insert the new_layers after the node with start_node_id."""
new_node_id = self._add_node(deepcopy(self.node_list[end_node_id]))
temp_output_id = new_node_id
for layer in new_layers[:-1]:
temp_output_id =... |
Add a weighted add skip - connection from after start node to end node. Args: start_id: The convolutional layer ID after which to start the skip - connection. end_id: The convolutional layer ID after which to end the skip - connection. | def to_add_skip_model(self, start_id, end_id):
"""Add a weighted add skip-connection from after start node to end node.
Args:
start_id: The convolutional layer ID, after which to start the skip-connection.
end_id: The convolutional layer ID, after which to end the skip-connection... |
Add a weighted add concatenate connection from after start node to end node. Args: start_id: The convolutional layer ID after which to start the skip - connection. end_id: The convolutional layer ID after which to end the skip - connection. | def to_concat_skip_model(self, start_id, end_id):
"""Add a weighted add concatenate connection from after start node to end node.
Args:
start_id: The convolutional layer ID, after which to start the skip-connection.
end_id: The convolutional layer ID, after which to end the skip-... |
Extract the the description of the Graph as an instance of NetworkDescriptor. | def extract_descriptor(self):
"""Extract the the description of the Graph as an instance of NetworkDescriptor."""
main_chain = self.get_main_chain()
index_in_main_chain = {}
for index, u in enumerate(main_chain):
index_in_main_chain[u] = index
ret = NetworkDescriptor... |
clear weights of the graph | def clear_weights(self):
''' clear weights of the graph
'''
self.weighted = False
for layer in self.layer_list:
layer.weights = None |
Return a list of layer IDs in the main chain. | def get_main_chain_layers(self):
"""Return a list of layer IDs in the main chain."""
main_chain = self.get_main_chain()
ret = []
for u in main_chain:
for v, layer_id in self.adj_list[u]:
if v in main_chain and u in main_chain:
ret.append(la... |
Returns the main chain node ID list. | def get_main_chain(self):
"""Returns the main chain node ID list."""
pre_node = {}
distance = {}
for i in range(self.n_nodes):
distance[i] = 0
pre_node[i] = i
for i in range(self.n_nodes - 1):
for u in range(self.n_nodes):
for v... |
Run the tuner. This function will never return unless raise. | def run(self):
"""Run the tuner.
This function will never return unless raise.
"""
_logger.info('Start dispatcher')
if dispatcher_env_vars.NNI_MODE == 'resume':
self.load_checkpoint()
while True:
command, data = receive()
if data:
... |
Process commands in command queues. | def command_queue_worker(self, command_queue):
"""Process commands in command queues.
"""
while True:
try:
# set timeout to ensure self.stopping is checked periodically
command, data = command_queue.get(timeout=3)
try:
... |
Enqueue command into command queues | def enqueue_command(self, command, data):
"""Enqueue command into command queues
"""
if command == CommandType.TrialEnd or (command == CommandType.ReportMetricData and data['type'] == 'PERIODICAL'):
self.assessor_command_queue.put((command, data))
else:
self.defau... |
Worker thread to process a command. | def process_command_thread(self, request):
"""Worker thread to process a command.
"""
command, data = request
if multi_thread_enabled():
try:
self.process_command(command, data)
except Exception as e:
_logger.exception(str(e))
... |
Update values in the array to match their corresponding type | def match_val_type(vals, vals_bounds, vals_types):
'''
Update values in the array, to match their corresponding type
'''
vals_new = []
for i, _ in enumerate(vals_types):
if vals_types[i] == "discrete_int":
# Find the closest integer in the array, vals_bounds
vals_new... |
Random generate variable value within their bounds | def rand(x_bounds, x_types):
'''
Random generate variable value within their bounds
'''
outputs = []
for i, _ in enumerate(x_bounds):
if x_types[i] == "discrete_int":
temp = x_bounds[i][random.randint(0, len(x_bounds[i]) - 1)]
outputs.append(temp)
elif x_type... |
wider graph | def to_wider_graph(graph):
''' wider graph
'''
weighted_layer_ids = graph.wide_layer_ids()
weighted_layer_ids = list(
filter(lambda x: graph.layer_list[x].output.shape[-1], weighted_layer_ids)
)
wider_layers = sample(weighted_layer_ids, 1)
for layer_id in wider_layers:
layer... |
skip connection graph | def to_skip_connection_graph(graph):
''' skip connection graph
'''
# The last conv layer cannot be widen since wider operator cannot be done over the two sides of flatten.
weighted_layer_ids = graph.skip_connection_layer_ids()
valid_connection = []
for skip_type in sorted([NetworkDescriptor.ADD_... |
create new layer for the graph | def create_new_layer(layer, n_dim):
''' create new layer for the graph
'''
input_shape = layer.output.shape
dense_deeper_classes = [StubDense, get_dropout_class(n_dim), StubReLU]
conv_deeper_classes = [get_conv_class(n_dim), get_batch_norm_class(n_dim), StubReLU]
if is_layer(layer, "ReLU"):
... |
deeper graph | def to_deeper_graph(graph):
''' deeper graph
'''
weighted_layer_ids = graph.deep_layer_ids()
if len(weighted_layer_ids) >= Constant.MAX_LAYERS:
return None
deeper_layer_ids = sample(weighted_layer_ids, 1)
for layer_id in deeper_layer_ids:
layer = graph.layer_list[layer_id]
... |
judge if a graph is legal or not. | def legal_graph(graph):
'''judge if a graph is legal or not.
'''
descriptor = graph.extract_descriptor()
skips = descriptor.skip_connections
if len(skips) != len(set(skips)):
return False
return True |
core transform function for graph. | def transform(graph):
'''core transform function for graph.
'''
graphs = []
for _ in range(Constant.N_NEIGHBOURS * 2):
random_num = randrange(3)
temp_graph = None
if random_num == 0:
temp_graph = to_deeper_graph(deepcopy(graph))
elif random_num == 1:
... |
low: an float that represent an lower bound high: an float that represent an upper bound random_state: an object of numpy. random. RandomState | def uniform(low, high, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
random_state: an object of numpy.random.RandomState
'''
assert high > low, 'Upper bound must be larger than lower bound'
return random_state.uniform(low, high) |
low: an float that represent an lower bound high: an float that represent an upper bound q: sample step random_state: an object of numpy. random. RandomState | def quniform(low, high, q, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(uniform(low, high, random_state) / q) * q |
low: an float that represent an lower bound high: an float that represent an upper bound random_state: an object of numpy. random. RandomState | def loguniform(low, high, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
random_state: an object of numpy.random.RandomState
'''
assert low > 0, 'Lower bound must be positive'
return np.exp(uniform(np.log(low), np.log(high), random... |
low: an float that represent an lower bound high: an float that represent an upper bound q: sample step random_state: an object of numpy. random. RandomState | def qloguniform(low, high, q, random_state):
'''
low: an float that represent an lower bound
high: an float that represent an upper bound
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(loguniform(low, high, random_state) / q) * q |
mu: float or array_like of floats sigma: float or array_like of floats q: sample step random_state: an object of numpy. random. RandomState | def qnormal(mu, sigma, q, random_state):
'''
mu: float or array_like of floats
sigma: float or array_like of floats
q: sample step
random_state: an object of numpy.random.RandomState
'''
return np.round(normal(mu, sigma, random_state) / q) * q |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.