def initialize_parameters():
print('Initializing parameters...')
import os
# Obtain the path of the directory of this script
file_path = os.path.dirname(os.path.realpath(__file__))
# Import the CANDLE library
import sys
sys.path.append(candle_lib)
import candle_keras as candle
# Instantiate the candle.Benchmark class
mymodel_common = candle.Benchmark(file_path,
os.getenv("DEFAULT_PARAMS_FILE"),
'keras',
prog='myprog',
desc='My model')
# Get a dictionary of the model hyperparamters
hyperparams = candle.initialize_parameters(mymodel_common)
# Return the dictionary of the hyperparameters
return (hyperparams)
def initialize_parameters():
gae_common = candle.Benchmark('./',
'gae_params.txt',
'keras',
prog='gae_baseline_keras2',
desc='GAE Network')
# Initialize parameters
gParameters = default_utils.initialize_parameters(gae_common)
return gParameters
def initialize_parameters():
t29_common = candle_keras.Benchmark(file_path,
't29_default_model.txt',
'keras',
prog='t29res.py',
desc='resnet')
# Need a pointer to the docs showing what is provided
# by default
additional_definitions = [{
'name': 'connections',
'default': 1,
'type': int,
'help': 'The number of residual connections.'
}, {
'name':
'distance',
'default':
1,
'type':
int,
'help':
'Residual connection distance between dense layers.'
}, {
'name': 'model',
'default': 'model.json',
'type': str,
'help': 'Name of json model description file.'
}, {
'name': 'weights',
'default': 'model.h5',
'type': str,
'help': 'Name of h5 weights file.'
}, {
'name':
'n_pred',
'default':
1,
'type':
int,
'help':
'Number of predictions to do on each sample.'
}]
t29_common.additional_definitions = additional_definitions
gParameters = candle_keras.initialize_parameters(t29_common)
return gParameters
def initialize_parameters():
t29_common = candle_keras.Benchmark(file_path, 't29_default_model.txt','keras',
prog='/t29res.py',desc='resnet')
#A list common parsed parameters are available here: https://ecp-candle.github.io/Candle/html/_modules/default_utils.html#get_common_parser
additional_definitions = [
{'name':'connections',
'default':1,
'type':int,
'help':'The number of residual connections.'},
{'name':'distance',
'default':1,
'type':int,
'help':'Residual connection distance between dense layers.'}
]
t29_common.additional_definitions = additional_definitions
gParameters = candle_keras.initialize_parameters(t29_common)
return gParameters
def initialize_parameters():
t29_common = candle_keras.Benchmark(file_path, 't29_default_model.txt','keras',
prog='t29res.py',desc='resnet')
# Need a pointer to the docs showing what is provided
# by default
additional_definitions = [
{'name':'connections',
'default':1,
'type':int,
'help':'The number of residual connections.'},
{'name':'distance',
'default':1,
'type':int,
'help':'Residual connection distance between dense layers.'}
]
t29_common.additional_definitions = additional_definitions
gParameters = candle_keras.initialize_parameters(t29_common)
return gParameters
def initialize_parameters():
# Add the candle_keras library to the Python path
import sys, os
sys.path.append(os.getenv("CANDLE") + '/Candle/common')
# Instantiate the Benchmark class (the values of the prog and desc parameters don't really matter)
import candle_keras as candle
mymodel_common = candle.Benchmark(os.path.dirname(
os.path.realpath(__file__)),
os.getenv("DEFAULT_PARAMS_FILE"),
'keras',
prog='myprogram',
desc='My CANDLE example')
# Read the parameters (in a dictionary format) pointed to by the environment variable DEFAULT_PARAMS_FILE
gParameters = candle.initialize_parameters(mymodel_common)
# Return this dictionary of parameters
return (gParameters)