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