product_matching/experiments/euclidean_product_matching.py [28:41]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
preprocessor.fit(train_raw)
train_processed = preprocessor.transform(train_raw)
test_processed = preprocessor.transform(test_raw)

from tensorflow.python.keras import backend as K
import tensorflow as tf
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True  # dynamically grow the memory used on the GPU
config.log_device_placement = True  # to log device placement (on which device the operation ran)
sess = tf.compat.v1.Session(config=config)
K.set_session(sess)
strategy = tf.distribute.MirroredStrategy()
print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
with strategy.scope():
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



product_matching/experiments/hyperboloid_product_matching.py [28:41]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
preprocessor.fit(train_raw)
train_processed = preprocessor.transform(train_raw)
test_processed = preprocessor.transform(test_raw)

from tensorflow.python.keras import backend as K
import tensorflow as tf
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True  # dynamically grow the memory used on the GPU
config.log_device_placement = True  # to log device placement (on which device the operation ran)
sess = tf.compat.v1.Session(config=config)
K.set_session(sess)
strategy = tf.distribute.MirroredStrategy()
print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
with strategy.scope():
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -