File size: 1,576 Bytes
cbc5e69 | 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 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | # coding: utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
# the type of float to use throughout the session.
_FLOATX = 'float32'
_EPSILON = 1e-8
_INF = 1e8
def epsilon():
return _EPSILON
def set_epsilon(e):
global _EPSILON
_EPSILON = e
def inf():
return _INF
def set_inf(e):
global _INF
_INF = e
def floatx():
return _FLOATX
def set_floatx(floatx):
global _FLOATX
if floatx not in {'float16', 'float32', 'float64'}:
raise ValueError('Unknown floatx type: ' + str(floatx))
_FLOATX = str(floatx)
def np_to_float(x):
return np.asarray(x, dtype=_FLOATX)
def tf_to_float(x):
return tf.cast(x, tf.as_dtype(floatx()))
def float32_variable_storage_getter(getter, name, shape=None, dtype=None,
initializer=None, regularizer=None,
trainable=True,
*args, **kwargs):
"""Custom variable getter that forces trainable variables to be stored in
float32 precision and then casts them to the training precision.
"""
storage_dtype = tf.float32 if trainable else dtype
variable = getter(name, shape, dtype=storage_dtype,
initializer=initializer, regularizer=regularizer,
trainable=trainable,
*args, **kwargs)
if trainable and dtype != tf.float32:
variable = tf.cast(variable, dtype)
return variable
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