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# Copyright 2017 Paul Balanca. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""TF Extended: additional math functions.
"""
import tensorflow as tf
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
def safe_divide(numerator, denominator, name):
"""Divides two values, returning 0 if the denominator is <= 0.
Args:
numerator: A real `Tensor`.
denominator: A real `Tensor`, with dtype matching `numerator`.
name: Name for the returned op.
Returns:
0 if `denominator` <= 0, else `numerator` / `denominator`
"""
return tf.where(
math_ops.greater(denominator, 0),
math_ops.divide(numerator, denominator),
tf.zeros_like(numerator),
name=name)
def cummax(x, reverse=False, name=None):
"""Compute the cumulative maximum of the tensor `x` along `axis`. This
operation is similar to the more classic `cumsum`. Only support 1D Tensor
for now.
Args:
x: A `Tensor`. Must be one of the following types: `float32`, `float64`,
`int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
`complex128`, `qint8`, `quint8`, `qint32`, `half`.
axis: A `Tensor` of type `int32` (default: 0).
reverse: A `bool` (default: False).
name: A name for the operation (optional).
Returns:
A `Tensor`. Has the same type as `x`.
"""
with ops.name_scope(name, "Cummax", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
# Not very optimal: should directly integrate reverse into tf.scan.
if reverse:
x = tf.reverse(x, axis=[0])
# 'Accumlating' maximum: ensure it is always increasing.
cmax = tf.scan(lambda a, y: tf.maximum(a, y), x,
initializer=None, parallel_iterations=1,
back_prop=False, swap_memory=False)
if reverse:
cmax = tf.reverse(cmax, axis=[0])
return cmax