tensorflow - 在'tensorflow 中无法获取'日志'

问题描述

我正在研究 CapsNet 并从这里获取代码帮助。在 tensorflow = 2.4.0 的 google colab 上进行模拟。我收到以下错误：

 AttributeError: in user code:
    
        /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
            return step_function(self, iterator)
        /content/drive/My Drive/Cervical GAN/Segmentation/Cheng-Lin-Li/SegCaps-master-aashish/utils/custom_losses.py:102 dice_loss  *
            return 1-dice_soft(y_true, y_pred, from_logits=False)
        /content/drive/My Drive/Cervical GAN/Segmentation/Cheng-Lin-Li/SegCaps-master-aashish/utils/custom_losses.py:41 dice_soft  *
            y_pred = tf.log(y_pred / (1 - y_pred))
    
        AttributeError: module 'tensorflow' has no attribute 'log'

以下是 custom_losses.py

'''
Capsules for Object Segmentation (SegCaps)
Original Paper: https://arxiv.org/abs/1804.04241
Code written by: Rodney LaLonde
If you use significant portions of this code or the ideas from our paper, please cite it :)
If you have any questions, please email me at lalonde@knights.ucf.edu.
This file contains the definitions of custom loss functions not present in the default Keras.
=====
This program includes all custom loss functions UNet, tiramisu, Capsule Nets (capsbasic) or SegCaps(segcapsr1 or segcapsr3).
@author: Cheng-Lin Li a.k.a. Clark
@copyright:  2018 Cheng-Lin Li@Insight AI. All rights reserved.
@license:    Licensed under the Apache License v2.0. http://www.apache.org/licenses/
@contact:    clark.cl.li@gmail.com     
    
Enhancement:
    1. Revise default loss_type to jaccard on dice_soft function. 
    2. add bce_dice_loss for future usage.
'''

import tensorflow as tf
from keras import backend as K
from keras.losses import binary_crossentropy

def dice_soft(y_true, y_pred, loss_type='jaccard', axis=[1,2,3], smooth=1e-5, from_logits=False):
    """Soft dice (Sørensen or Jaccard) coefficient for comparing the similarity
    of two batch of data, usually be used for binary image segmentation
    i.e. labels are binary. The coefficient between 0 to 1, 1 means totally match.
    Parameters
    -----------
    y_pred : tensor
        A distribution with shape: [batch_size, ....], (any dimensions).
    y_true : tensor
        A distribution with shape: [batch_size, ....], (any dimensions).
    loss_type : string
        ``jaccard`` or ``sorensen``, default is ``jaccard``.
    axis : list of integer
        All dimensions are reduced, default ``[1,2,3]``.
    smooth : float
        This small value will be added to the numerator and denominator.
        If both y_pred and y_true are empty, it makes sure dice is 1.
        If either y_pred or y_true are empty (all pixels are background), dice = ```smooth/(small_value + smooth)``,
        then if smooth is very small, dice close to 0 (even the image values lower than the threshold),
        so in this case, higher smooth can have a higher dice.
    Examples
    ---------
    >>> outputs = tl.act.pixel_wise_softmax(network.outputs)
    >>> dice_loss = 1 - tl.cost.dice_coe(outputs, y_)
    References
    -----------
    - `Wiki-Dice <https://en.wikipedia.org/wiki/Sørensen–Dice_coefficient>`_
    """

    if not from_logits:
        # transform back to logits
        _epsilon = tf.convert_to_tensor(1e-7, y_pred.dtype.base_dtype)
        y_pred = tf.clip_by_value(y_pred, _epsilon, 1 - _epsilon)
        y_pred = tf.log(y_pred / (1 - y_pred))

    inse = tf.reduce_sum(y_pred * y_true, axis=axis)
    if loss_type == 'jaccard':
        l = tf.reduce_sum(y_pred * y_pred, axis=axis)
        r = tf.reduce_sum(y_true * y_true, axis=axis)
    elif loss_type == 'sorensen':
        l = tf.reduce_sum(y_pred, axis=axis)
        r = tf.reduce_sum(y_true, axis=axis)
    else:
        raise Exception("Unknow loss_type")
    ## old axis=[0,1,2,3]
    # dice = 2 * (inse) / (l + r)
    # epsilon = 1e-5
    # dice = tf.clip_by_value(dice, 0, 1.0-epsilon) # if all empty, dice = 1
    ## new haodong
    dice = (2. * inse + smooth) / (l + r + smooth)
    ##
    dice = tf.reduce_mean(dice)
    return dice


def dice_hard(y_true, y_pred, threshold=0.5, axis=[1,2,3], smooth=1e-5):
    """Non-differentiable Sørensen–Dice coefficient for comparing the similarity
    of two batch of data, usually be used for binary image segmentation i.e. labels are binary.
    The coefficient between 0 to 1, 1 if totally match.
    Parameters
    -----------
    y_pred : tensor
        A distribution with shape: [batch_size, ....], (any dimensions).
    y_true : tensor
        A distribution with shape: [batch_size, ....], (any dimensions).
    threshold : float
        The threshold value to be true.
    axis : list of integer
        All dimensions are reduced, default ``[1,2,3]``.
    smooth : float
        This small value will be added to the numerator and denominator, see ``dice_coe``.
    References
    -----------
    - `Wiki-Dice <https://en.wikipedia.org/wiki/Sørensen–Dice_coefficient>`_
    """
    y_pred = tf.cast(y_pred > threshold, dtype=tf.float32)
    y_true = tf.cast(y_true > threshold, dtype=tf.float32)
    inse = tf.reduce_sum(tf.multiply(y_pred, y_true), axis=axis)
    l = tf.reduce_sum(y_pred, axis=axis)
    r = tf.reduce_sum(y_true, axis=axis)
    ## old axis=[0,1,2,3]
    # hard_dice = 2 * (inse) / (l + r)
    # epsilon = 1e-5
    # hard_dice = tf.clip_by_value(hard_dice, 0, 1.0-epsilon)
    ## new haodong
    hard_dice = (2. * inse + smooth) / (l + r + smooth)
    ##
    hard_dice = tf.reduce_mean(hard_dice)
    return hard_dice


def dice_loss(y_true, y_pred, from_logits=False):
    return 1-dice_soft(y_true, y_pred, from_logits=False)


def bce_dice_loss(y_true, y_pred):
    return binary_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred)



def weighted_binary_crossentropy_loss(pos_weight):
    # pos_weight: A coefficient to use on the positive examples.
    def weighted_binary_crossentropy(target, output, from_logits=False):
        """Binary crossentropy between an output tensor and a target tensor.
        # Arguments
            target: A tensor with the same shape as `output`.
            output: A tensor.
            from_logits: Whether `output` is expected to be a logits tensor.
                By default, we consider that `output`
                encodes a probability distribution.
        # Returns
            A tensor.
        """
        # Note: tf.nn.sigmoid_cross_entropy_with_logits
        # expects logits, Keras expects probabilities.
        if not from_logits:
            # transform back to logits
            _epsilon = tf.convert_to_tensor(1e-7, output.dtype.base_dtype)
            output = tf.clip_by_value(output, _epsilon, 1 - _epsilon)
            output = tf.log(output / (1 - output))

        return tf.nn.weighted_cross_entropy_with_logits(targets=target,
                                                       logits=output,
                                                        pos_weight=pos_weight)
    return weighted_binary_crossentropy


def margin_loss(margin=0.4, downweight=0.5, pos_weight=1.0):
    '''
    Args:
        margin: scalar, the margin after subtracting 0.5 from raw_logits.
        downweight: scalar, the factor for negative cost.
    '''

    def _margin_loss(labels, raw_logits):
        """Penalizes deviations from margin for each logit.
        Each wrong logit costs its distance to margin. For negative logits margin is
        0.1 and for positives it is 0.9. First subtract 0.5 from all logits. Now
        margin is 0.4 from each side.
        Args:
        labels: tensor, one hot encoding of ground truth.
        raw_logits: tensor, model predictions in range [0, 1]
        Returns:
        A tensor with cost for each data point of shape [batch_size].
        """
        logits = raw_logits - 0.5
        positive_cost = pos_weight * labels * tf.cast(tf.less(logits, margin),
                                       tf.float32) * tf.pow(logits - margin, 2)
        negative_cost = (1 - labels) * tf.cast(
          tf.greater(logits, -margin), tf.float32) * tf.pow(logits + margin, 2)
        return 0.5 * positive_cost + downweight * 0.5 * negative_cost

    return _margin_loss

以上是在使用骰子损失时出现的。使用 bce loss 时没有错误。我已经尝试过tf.math.log，tf.log但仍然出现以下错误：

TypeError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function  *
        return step_function(self, iterator)
    /content/drive/MyDrive/Cervical GAN/Segmentation/Cheng-Lin-Li/SegCaps-master-aashish/utils/custom_losses.py:102 dice_loss  *
        return 1-dice_soft(y_true, y_pred, from_logits=False)
    /content/drive/MyDrive/Cervical GAN/Segmentation/Cheng-Lin-Li/SegCaps-master-aashish/utils/custom_losses.py:43 dice_soft  *
        inse = tf.reduce_sum(y_pred * y_true, axis=axis)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1180 binary_op_wrapper
        raise e
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1164 binary_op_wrapper
        return func(x, y, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:1496 _mul_dispatch
        return multiply(x, y, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:518 multiply
        return gen_math_ops.mul(x, y, name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py:6078 mul
        "Mul", x=x, y=y, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:558 _apply_op_helper
        inferred_from[input_arg.type_attr]))

    TypeError: Input 'y' of 'Mul' Op has type uint8 that does not match type float32 of argument 'x'.

标签： tensorflowkerasdeep-learning

tensorflow - 在'tensorflow 中无法获取'日志'

问题描述

解决方案

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