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Microsoft/nni | examples/trials/mnist-batch-tune-keras/mnist-keras.py | create_mnist_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,... | python | 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,... | [
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Microsoft/nni | examples/trials/mnist-batch-tune-keras/mnist-keras.py | load_mnist_data | 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... | python | 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... | [
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Microsoft/nni | examples/trials/mnist-batch-tune-keras/mnist-keras.py | train | 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... | python | 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... | [
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Microsoft/nni | examples/trials/mnist-batch-tune-keras/mnist-keras.py | SendMetrics.on_epoch_end | def on_epoch_end(self, epoch, logs={}):
'''
Run on end of each epoch
'''
LOG.debug(logs)
nni.report_intermediate_result(logs["val_acc"]) | python | def on_epoch_end(self, epoch, logs={}):
'''
Run on end of each epoch
'''
LOG.debug(logs)
nni.report_intermediate_result(logs["val_acc"]) | [
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Microsoft/nni | tools/nni_cmd/config_utils.py | Config.get_all_config | def get_all_config(self):
'''get all of config values'''
return json.dumps(self.config, indent=4, sort_keys=True, separators=(',', ':')) | python | def get_all_config(self):
'''get all of config values'''
return json.dumps(self.config, indent=4, sort_keys=True, separators=(',', ':')) | [
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Microsoft/nni | tools/nni_cmd/config_utils.py | Config.set_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() | python | 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() | [
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Microsoft/nni | tools/nni_cmd/config_utils.py | Config.write_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 | python | 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 | [
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Microsoft/nni | tools/nni_cmd/config_utils.py | Experiments.add_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... | python | 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... | [
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Microsoft/nni | tools/nni_cmd/config_utils.py | Experiments.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 | python | 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 | [
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Microsoft/nni | tools/nni_cmd/config_utils.py | Experiments.remove_experiment | def remove_experiment(self, id):
'''remove an experiment by id'''
if id in self.experiments:
self.experiments.pop(id)
self.write_file() | python | def remove_experiment(self, id):
'''remove an experiment by id'''
if id in self.experiments:
self.experiments.pop(id)
self.write_file() | [
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Microsoft/nni | tools/nni_cmd/config_utils.py | Experiments.write_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 | python | 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 | [
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Microsoft/nni | tools/nni_cmd/config_utils.py | Experiments.read_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 {} | python | 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 {} | [
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/data.py | load_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... | python | 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... | [
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/data.py | tokenize | 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_... | python | 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_... | [
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/data.py | collect_vocab | 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 | python | 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 | [
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/data.py | shuffle_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 | python | 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 | [
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/data.py | get_batches | 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... | python | 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)]}
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/data.py | 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... | python | 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... | [
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/data.py | 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.... | python | 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.... | [
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/data.py | get_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 | python | 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 | [
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/data.py | get_answer_begin_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_... | python | 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_... | [
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/data.py | get_buckets | 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)]
... | python | 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)]
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Microsoft/nni | examples/trials/weight_sharing/ga_squad/data.py | WhitespaceTokenizer.tokenize | 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]
... | python | 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]
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Microsoft/nni | examples/tuners/weight_sharing/ga_customer_tuner/customer_tuner.py | CustomerTuner.generate_new_id | 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 | python | 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 | [
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Microsoft/nni | examples/tuners/weight_sharing/ga_customer_tuner/customer_tuner.py | CustomerTuner.init_population | 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, ... | python | 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, ... | [
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Microsoft/nni | examples/tuners/weight_sharing/ga_customer_tuner/customer_tuner.py | CustomerTuner.generate_parameters | 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",
... | python | 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",
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Microsoft/nni | examples/tuners/weight_sharing/ga_customer_tuner/customer_tuner.py | CustomerTuner.receive_trial_result | 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 {... | python | 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 {... | [
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Microsoft/nni | src/sdk/pynni/nni/medianstop_assessor/medianstop_assessor.py | MedianstopAssessor._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:
... | python | 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:
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trial_job_id: int
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Microsoft/nni | src/sdk/pynni/nni/medianstop_assessor/medianstop_assessor.py | MedianstopAssessor.trial_end | 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:
... | python | 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:
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Microsoft/nni | src/sdk/pynni/nni/medianstop_assessor/medianstop_assessor.py | MedianstopAssessor.assess_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... | python | 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
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Parameters
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trial_job_id: int
trial job id
trial_history: list
The history performance matrix of each trial
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Microsoft/nni | tools/nni_trial_tool/hdfsClientUtility.py | copyHdfsDirectoryToLocal | 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.... | python | 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.... | [
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Microsoft/nni | tools/nni_trial_tool/hdfsClientUtility.py | copyHdfsFileToLocal | 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)
... | python | 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)
... | [
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Microsoft/nni | tools/nni_trial_tool/hdfsClientUtility.py | copyDirectoryToHdfs | 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):
... | python | 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):
... | [
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Microsoft/nni | tools/nni_trial_tool/hdfsClientUtility.py | copyFileToHdfs | 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!')... | python | 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!')... | [
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Microsoft/nni | examples/trials/sklearn/regression/main.py | load_data | 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... | python | 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... | [
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Microsoft/nni | examples/trials/sklearn/regression/main.py | get_model | 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'])... | python | 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'])... | [
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Microsoft/nni | examples/trials/sklearn/regression/main.py | run | 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) | python | 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) | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | NetworkDescriptor.add_skip_connection | 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... | python | 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... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | NetworkDescriptor.to_json | 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} | python | 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} | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.add_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... | python | 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... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph._add_node | 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)
... | python | 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)
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph._add_edge | 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_... | python | 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_... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph._redirect_edge | 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... | python | 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`
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layer_id = None
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph._replace_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] =... | python | 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] =... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.topological_order | 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]:
... | python | 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]:
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph._get_pooling_layers | 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_... | python | 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_... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph._depth_first_search | 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
... | python | 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
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph._search | 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.
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... | python | def _search(self, u, start_dim, total_dim, n_add):
"""Search the graph for all the layers to be widened caused by an operation.
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.to_deeper_model | 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_... | python | 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_... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.to_wider_model | 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... | python | 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... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph._insert_new_layers | 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 =... | python | 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 =... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.to_add_skip_model | 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... | python | def to_add_skip_model(self, start_id, end_id):
"""Add a weighted add skip-connection from after start node to end node.
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start_id: The convolutional layer ID, after which to start the skip-connection.
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.to_concat_skip_model | 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-... | python | def to_concat_skip_model(self, start_id, end_id):
"""Add a weighted add concatenate connection from after start node to end node.
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start_id: The convolutional layer ID, after which to start the skip-connection.
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.extract_descriptor | 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... | python | 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... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.clear_weights | def clear_weights(self):
''' clear weights of the graph
'''
self.weighted = False
for layer in self.layer_list:
layer.weights = None | python | def clear_weights(self):
''' clear weights of the graph
'''
self.weighted = False
for layer in self.layer_list:
layer.weights = None | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.get_main_chain_layers | 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... | python | 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... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph.py | Graph.get_main_chain | 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... | python | 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):
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Microsoft/nni | src/sdk/pynni/nni/msg_dispatcher_base.py | MsgDispatcherBase.run | 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:
... | python | 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:
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Microsoft/nni | src/sdk/pynni/nni/msg_dispatcher_base.py | MsgDispatcherBase.command_queue_worker | 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:
... | python | 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:
... | [
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Microsoft/nni | src/sdk/pynni/nni/msg_dispatcher_base.py | MsgDispatcherBase.enqueue_command | 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... | python | 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... | [
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Microsoft/nni | src/sdk/pynni/nni/msg_dispatcher_base.py | MsgDispatcherBase.process_command_thread | 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))
... | python | 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))
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/lib_data.py | match_val_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... | python | 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
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/lib_data.py | rand | 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... | python | 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)
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py | to_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... | python | 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:
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py | to_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_... | python | 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_... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py | create_new_layer | 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"):
... | python | 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"):
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py | to_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]
... | python | 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]
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py | legal_graph | 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 | python | 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 | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/graph_transformer.py | transform | 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:
... | python | 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:
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | uniform | 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) | python | 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) | [
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | quniform | 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 | python | 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 | [
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | loguniform | 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... | python | 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... | [
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | qloguniform | 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 | python | 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 | [
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | qnormal | 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 | python | 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 | [
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | lognormal | def lognormal(mu, sigma, random_state):
'''
mu: float or array_like of floats
sigma: float or array_like of floats
random_state: an object of numpy.random.RandomState
'''
return np.exp(normal(mu, sigma, random_state)) | python | def lognormal(mu, sigma, random_state):
'''
mu: float or array_like of floats
sigma: float or array_like of floats
random_state: an object of numpy.random.RandomState
'''
return np.exp(normal(mu, sigma, random_state)) | [
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Microsoft/nni | src/sdk/pynni/nni/parameter_expressions.py | qlognormal | def qlognormal(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(lognormal(mu, sigma, random_state) / q) * q | python | def qlognormal(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(lognormal(mu, sigma, random_state) / q) * q | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GP/Prediction.py | predict | def predict(parameters_value, regressor_gp):
'''
Predict by Gaussian Process Model
'''
parameters_value = numpy.array(parameters_value).reshape(-1, len(parameters_value))
mu, sigma = regressor_gp.predict(parameters_value, return_std=True)
return mu[0], sigma[0] | python | def predict(parameters_value, regressor_gp):
'''
Predict by Gaussian Process Model
'''
parameters_value = numpy.array(parameters_value).reshape(-1, len(parameters_value))
mu, sigma = regressor_gp.predict(parameters_value, return_std=True)
return mu[0], sigma[0] | [
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Microsoft/nni | tools/nni_trial_tool/rest_utils.py | rest_get | def rest_get(url, timeout):
'''Call rest get method'''
try:
response = requests.get(url, timeout=timeout)
return response
except Exception as e:
print('Get exception {0} when sending http get to url {1}'.format(str(e), url))
return None | python | def rest_get(url, timeout):
'''Call rest get method'''
try:
response = requests.get(url, timeout=timeout)
return response
except Exception as e:
print('Get exception {0} when sending http get to url {1}'.format(str(e), url))
return None | [
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Microsoft/nni | tools/nni_trial_tool/rest_utils.py | rest_post | def rest_post(url, data, timeout, rethrow_exception=False):
'''Call rest post method'''
try:
response = requests.post(url, headers={'Accept': 'application/json', 'Content-Type': 'application/json'},\
data=data, timeout=timeout)
return response
except Exceptio... | python | def rest_post(url, data, timeout, rethrow_exception=False):
'''Call rest post method'''
try:
response = requests.post(url, headers={'Accept': 'application/json', 'Content-Type': 'application/json'},\
data=data, timeout=timeout)
return response
except Exceptio... | [
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Microsoft/nni | tools/nni_trial_tool/rest_utils.py | rest_put | def rest_put(url, data, timeout):
'''Call rest put method'''
try:
response = requests.put(url, headers={'Accept': 'application/json', 'Content-Type': 'application/json'},\
data=data, timeout=timeout)
return response
except Exception as e:
print('Get ex... | python | def rest_put(url, data, timeout):
'''Call rest put method'''
try:
response = requests.put(url, headers={'Accept': 'application/json', 'Content-Type': 'application/json'},\
data=data, timeout=timeout)
return response
except Exception as e:
print('Get ex... | [
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Microsoft/nni | tools/nni_trial_tool/rest_utils.py | rest_delete | def rest_delete(url, timeout):
'''Call rest delete method'''
try:
response = requests.delete(url, timeout=timeout)
return response
except Exception as e:
print('Get exception {0} when sending http delete to url {1}'.format(str(e), url))
return None | python | def rest_delete(url, timeout):
'''Call rest delete method'''
try:
response = requests.delete(url, timeout=timeout)
return response
except Exception as e:
print('Get exception {0} when sending http delete to url {1}'.format(str(e), url))
return None | [
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py | CurvefittingAssessor.trial_end | def trial_end(self, trial_job_id, success):
"""update the best performance of completed trial job
Parameters
----------
trial_job_id: int
trial job id
success: bool
True if succssfully finish the experiment, False otherwise
"""
if ... | python | def trial_end(self, trial_job_id, success):
"""update the best performance of completed trial job
Parameters
----------
trial_job_id: int
trial job id
success: bool
True if succssfully finish the experiment, False otherwise
"""
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Microsoft/nni | src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py | CurvefittingAssessor.assess_trial | def assess_trial(self, trial_job_id, trial_history):
"""assess whether a trial should be early stop by curve fitting algorithm
Parameters
----------
trial_job_id: int
trial job id
trial_history: list
The history performance matrix of each trial
R... | python | def assess_trial(self, trial_job_id, trial_history):
"""assess whether a trial should be early stop by curve fitting algorithm
Parameters
----------
trial_job_id: int
trial job id
trial_history: list
The history performance matrix of each trial
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Microsoft/nni | src/sdk/pynni/nni/multi_phase/multi_phase_dispatcher.py | MultiPhaseMsgDispatcher.handle_initialize | def handle_initialize(self, data):
'''
data is search space
'''
self.tuner.update_search_space(data)
send(CommandType.Initialized, '')
return True | python | def handle_initialize(self, data):
'''
data is search space
'''
self.tuner.update_search_space(data)
send(CommandType.Initialized, '')
return True | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.generate_parameters | def generate_parameters(self, parameter_id):
"""
Returns a set of trial neural architecture, as a serializable object.
Parameters
----------
parameter_id : int
"""
if not self.history:
self.init_search()
new_father_id = None
generated... | python | def generate_parameters(self, parameter_id):
"""
Returns a set of trial neural architecture, as a serializable object.
Parameters
----------
parameter_id : int
"""
if not self.history:
self.init_search()
new_father_id = None
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.receive_trial_result | def receive_trial_result(self, parameter_id, parameters, value):
""" Record an observation of the objective function.
Parameters
----------
parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
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""" Record an observation of the objective function.
Parameters
----------
parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.init_search | def init_search(self):
"""Call the generators to generate the initial architectures for the search."""
if self.verbose:
logger.info("Initializing search.")
for generator in self.generators:
graph = generator(self.n_classes, self.input_shape).generate(
self... | python | def init_search(self):
"""Call the generators to generate the initial architectures for the search."""
if self.verbose:
logger.info("Initializing search.")
for generator in self.generators:
graph = generator(self.n_classes, self.input_shape).generate(
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.generate | def generate(self):
"""Generate the next neural architecture.
Returns
-------
other_info: any object
Anything to be saved in the training queue together with the architecture.
generated_graph: Graph
An instance of Graph.
"""
generated_grap... | python | def generate(self):
"""Generate the next neural architecture.
Returns
-------
other_info: any object
Anything to be saved in the training queue together with the architecture.
generated_graph: Graph
An instance of Graph.
"""
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.update | def update(self, other_info, graph, metric_value, model_id):
""" Update the controller with evaluation result of a neural architecture.
Parameters
----------
other_info: any object
In our case it is the father ID in the search tree.
graph: Graph
An instan... | python | def update(self, other_info, graph, metric_value, model_id):
""" Update the controller with evaluation result of a neural architecture.
Parameters
----------
other_info: any object
In our case it is the father ID in the search tree.
graph: Graph
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.add_model | def add_model(self, metric_value, model_id):
""" Add model to the history, x_queue and y_queue
Parameters
----------
metric_value : float
graph : dict
model_id : int
Returns
-------
model : dict
"""
if self.verbose:
lo... | python | def add_model(self, metric_value, model_id):
""" Add model to the history, x_queue and y_queue
Parameters
----------
metric_value : float
graph : dict
model_id : int
Returns
-------
model : dict
"""
if self.verbose:
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.get_best_model_id | def get_best_model_id(self):
""" Get the best model_id from history using the metric value
"""
if self.optimize_mode is OptimizeMode.Maximize:
return max(self.history, key=lambda x: x["metric_value"])["model_id"]
return min(self.history, key=lambda x: x["metric_value"])["mod... | python | def get_best_model_id(self):
""" Get the best model_id from history using the metric value
"""
if self.optimize_mode is OptimizeMode.Maximize:
return max(self.history, key=lambda x: x["metric_value"])["model_id"]
return min(self.history, key=lambda x: x["metric_value"])["mod... | [
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Microsoft/nni | src/sdk/pynni/nni/networkmorphism_tuner/networkmorphism_tuner.py | NetworkMorphismTuner.load_model_by_id | def load_model_by_id(self, model_id):
"""Get the model by model_id
Parameters
----------
model_id : int
model index
Returns
-------
load_model : Graph
the model graph representation
"""
with open(os.path.join(self... | python | def load_model_by_id(self, model_id):
"""Get the model by model_id
Parameters
----------
model_id : int
model index
Returns
-------
load_model : Graph
the model graph representation
"""
with open(os.path.join(self... | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | _rand_init | def _rand_init(x_bounds, x_types, selection_num_starting_points):
'''
Random sample some init seed within bounds.
'''
return [lib_data.rand(x_bounds, x_types) for i \
in range(0, selection_num_starting_points)] | python | def _rand_init(x_bounds, x_types, selection_num_starting_points):
'''
Random sample some init seed within bounds.
'''
return [lib_data.rand(x_bounds, x_types) for i \
in range(0, selection_num_starting_points)] | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | get_median | def get_median(temp_list):
"""Return median
"""
num = len(temp_list)
temp_list.sort()
print(temp_list)
if num % 2 == 0:
median = (temp_list[int(num/2)] + temp_list[int(num/2) - 1]) / 2
else:
median = temp_list[int(num/2)]
return median | python | def get_median(temp_list):
"""Return median
"""
num = len(temp_list)
temp_list.sort()
print(temp_list)
if num % 2 == 0:
median = (temp_list[int(num/2)] + temp_list[int(num/2) - 1]) / 2
else:
median = temp_list[int(num/2)]
return median | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | MetisTuner.update_search_space | def update_search_space(self, search_space):
"""Update the self.x_bounds and self.x_types by the search_space.json
Parameters
----------
search_space : dict
"""
self.x_bounds = [[] for i in range(len(search_space))]
self.x_types = [NONE_TYPE for i in range(len(se... | python | def update_search_space(self, search_space):
"""Update the self.x_bounds and self.x_types by the search_space.json
Parameters
----------
search_space : dict
"""
self.x_bounds = [[] for i in range(len(search_space))]
self.x_types = [NONE_TYPE for i in range(len(se... | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | MetisTuner._pack_output | def _pack_output(self, init_parameter):
"""Pack the output
Parameters
----------
init_parameter : dict
Returns
-------
output : dict
"""
output = {}
for i, param in enumerate(init_parameter):
output[self.key_order[i]] = param
... | python | def _pack_output(self, init_parameter):
"""Pack the output
Parameters
----------
init_parameter : dict
Returns
-------
output : dict
"""
output = {}
for i, param in enumerate(init_parameter):
output[self.key_order[i]] = param
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | MetisTuner.generate_parameters | def generate_parameters(self, parameter_id):
"""Generate next parameter for trial
If the number of trial result is lower than cold start number,
metis will first random generate some parameters.
Otherwise, metis will choose the parameters by the Gussian Process Model and the Gussian Mixt... | python | def generate_parameters(self, parameter_id):
"""Generate next parameter for trial
If the number of trial result is lower than cold start number,
metis will first random generate some parameters.
Otherwise, metis will choose the parameters by the Gussian Process Model and the Gussian Mixt... | [
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | MetisTuner.receive_trial_result | def receive_trial_result(self, parameter_id, parameters, value):
"""Tuner receive result from trial.
Parameters
----------
parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
"""
value = extr... | python | def receive_trial_result(self, parameter_id, parameters, value):
"""Tuner receive result from trial.
Parameters
----------
parameter_id : int
parameters : dict
value : dict/float
if value is dict, it should have "default" key.
"""
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/metis_tuner.py | MetisTuner.import_data | def import_data(self, data):
"""Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
"""
_completed_num = 0
for trial_info in data:
logger.info("Im... | python | def import_data(self, data):
"""Import additional data for tuning
Parameters
----------
data:
a list of dictionarys, each of which has at least two keys, 'parameter' and 'value'
"""
_completed_num = 0
for trial_info in data:
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Microsoft/nni | src/sdk/pynni/nni/metis_tuner/Regression_GP/CreateModel.py | create_model | def create_model(samples_x, samples_y_aggregation,
n_restarts_optimizer=250, is_white_kernel=False):
'''
Trains GP regression model
'''
kernel = gp.kernels.ConstantKernel(constant_value=1,
constant_value_bounds=(1e-12, 1e12)) * \
... | python | def create_model(samples_x, samples_y_aggregation,
n_restarts_optimizer=250, is_white_kernel=False):
'''
Trains GP regression model
'''
kernel = gp.kernels.ConstantKernel(constant_value=1,
constant_value_bounds=(1e-12, 1e12)) * \
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"c... | Trains GP regression model | [
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"GP",
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] | c7cc8db32da8d2ec77a382a55089f4e17247ce41 | https://github.com/Microsoft/nni/blob/c7cc8db32da8d2ec77a382a55089f4e17247ce41/src/sdk/pynni/nni/metis_tuner/Regression_GP/CreateModel.py#L30-L52 | train | Create a GP regression model for the given samples. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
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