def initialize_parameters():
t29_common = candle.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.initialize_parameters(t29_common)
return gParameters
def initialize_parameters():
unet_common = unet.UNET(unet.file_path,
'unet_params.txt',
'keras',
prog='unet_example',
desc='UNET example')
# Initialize parameters
gParameters = candle.initialize_parameters(unet_common)
return gParameters
def initialize_parameters():
# Build benchmark object
p3b1Bmk = bmk.BenchmarkP3B1(bmk.file_path, 'p3b1_default_model.txt', 'keras',
prog='p3b1_baseline', desc='Multi-task (DNN) for data extraction from clinical reports - Pilot 3 Benchmark 1')
# Initialize parameters
gParameters = candle.initialize_parameters(p3b1Bmk)
#bmk.logger.info('Params: {}'.format(gParameters))
return gParameters
def initialize_parameters():
# Build benchmark object
p3b3Bmk = bmk.BenchmarkP3B3(bmk.file_path, 'p3b4_default_model.txt', 'keras',
prog='p3b4_baseline', desc='Hierarchical Convolutional Attention Networks for data extraction from clinical reports - Pilot 3 Benchmark 4')
# Initialize parameters
gParameters = candle.initialize_parameters(p3b3Bmk)
#bmk.logger.info('Params: {}'.format(gParameters))
return gParameters
def initialize_parameters():
mnist_common = mnist.MNIST(mnist.file_path,
'mnist_params.txt',
'keras',
prog='mnist_mlp',
desc='MNIST example')
# Initialize parameters
gParameters = candle.initialize_parameters(mnist_common)
csv_logger = CSVLogger('{}/params.log'.format(gParameters))
return gParameters
def initialize_parameters():
# Build benchmark object
p1b2Bmk = p1b2.BenchmarkP1B2(p1b2.file_path,
'p1b2_default_model.txt',
'keras',
prog='p1b2_baseline',
desc='Train Classifier - Pilot 1 Benchmark 2')
# Initialize parameters
gParameters = candle.initialize_parameters(p1b2Bmk)
#p1b2.logger.info('Params: {}'.format(gParameters))
return gParameters
def initialize_parameters():
# Build benchmark object
unoBmk = benchmark.BenchmarkUno(
benchmark.file_path,
'uno_default_model.txt',
'keras',
prog='uno_baseline',
desc=
'Build neural network based models to predict tumor response to single and paired drugs.'
)
# Initialize parameters
gParameters = candle.initialize_parameters(unoBmk)
# benchmark.logger.info('Params: {}'.format(gParameters))
return gParameters
def initialize_parameters():
t29_common = candle.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.initialize_parameters(t29_common)
return gParameters
def initialize_parameters():
# Build benchmark object
comboBmk = combo.BenchmarkCombo(
combo.file_path,
'combo_default_model.txt',
'keras',
prog='combo_baseline',
desc=
'Build neural network based models to predict tumor response to drug pairs.'
)
# Initialize parameters
gParameters = candle.initialize_parameters(comboBmk)
#combo.logger.info('Params: {}'.format(gParameters))
return gParameters
def initialize_parameters():
# Build benchmark object
unoMTb = unoMT.unoMTBk(
unoMT.file_path,
'unoMT_default_model.txt',
'pytorch',
prog='unoMT_baseline',
desc=
'Multi-task combined single and combo drug prediction for cross-study data - Pilot 1'
)
print("Created unoMT benchmark")
# Initialize parameters
gParameters = candle.initialize_parameters(unoMTb)
print("Parameters initialized")
return gParameters
def initialize_parameters():
# Build benchmark object
p2b1Bmk = p2b1.BenchmarkP2B1(
p2b1.file_path,
'p2b1_default_model.txt',
'keras',
prog='p2b1_baseline',
desc='Train Molecular Frame Autoencoder - Pilot 2 Benchmark 1')
# Initialize parameters
GP = candle.initialize_parameters(p2b1Bmk)
#p2b1.logger.info('Params: {}'.format(gParameters))
print('\nTraining parameters:')
for key in sorted(GP):
print("\t%s: %s" % (key, GP[key]))
# print json.dumps(GP, indent=4, skipkeys=True, sort_keys=True)
if GP['backend'] != 'theano' and GP['backend'] != 'tensorflow':
sys.exit('Invalid backend selected: %s' % GP['backend'])
os.environ['KERAS_BACKEND'] = GP['backend']
reload(K)
'''
if GP['backend'] == 'theano':
K.set_image_dim_ordering('th')
elif GP['backend'] == 'tensorflow':
K.set_image_dim_ordering('tf')
'''
K.set_image_data_format('channels_last')
#"th" format means that the convolutional kernels will have the shape (depth, input_depth, rows, cols)
#"tf" format means that the convolutional kernels will have the shape (rows, cols, input_depth, depth)
print("Image data format: ", K.image_data_format())
# print "Image ordering: ", K.image_dim_ordering()
return GP