File size: 1,727 Bytes
0275184 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 |
# coding: utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
from utils import dtype
def get_initializer(initializer, initializer_gain):
tfdtype = tf.as_dtype(dtype.floatx())
if initializer == "uniform":
max_val = initializer_gain
return tf.random_uniform_initializer(-max_val, max_val, dtype=tfdtype)
elif initializer == "normal":
return tf.random_normal_initializer(0.0, initializer_gain, dtype=tfdtype)
elif initializer == "normal_unit_scaling":
return tf.variance_scaling_initializer(initializer_gain,
mode="fan_avg",
distribution="normal",
dtype=tfdtype)
elif initializer == "uniform_unit_scaling":
return tf.variance_scaling_initializer(initializer_gain,
mode="fan_avg",
distribution="uniform",
dtype=tfdtype)
else:
tf.logging.warn("Unrecognized initializer: %s" % initializer)
tf.logging.warn("Return to default initializer: glorot_uniform_initializer")
return tf.glorot_uniform_initializer(dtype=tfdtype)
def scale_initializer(scale, initializer):
"""Rescale the value given by initializer"""
tfdtype = tf.as_dtype(dtype.floatx())
def _initializer(shape, dtype=tfdtype, partition_info=None):
value = initializer(shape, dtype=dtype, partition_info=partition_info)
value *= scale
return value
return _initializer
|