具有附加动态参数的 Keras 损失函数 [英] Keras Loss Function with Additional Dynamic Parameter

问题描述

我正在为 deep-q 网络实现优先体验重放，规范的一部分是将梯度乘以所谓的重要性采样 (IS) 权重.以下论文的第 3.4 节讨论了梯度修改:https://arxiv.org/pdf/1511.05952.pdf 我正在努力创建一个自定义损失函数，除了 y_true 和 y_pred 之外，它还接受一系列 IS 权重.

I'm working on implementing prioritized experience replay for a deep-q network, and part of the specification is to multiply gradients by what's know as importance sampling (IS) weights. The gradient modification is discussed in section 3.4 of the following paper: https://arxiv.org/pdf/1511.05952.pdf I'm struggling with creating a custom loss function that takes in an array of IS weights in addition to y_true and y_pred.

这是我的模型的简化版本:

Here's a simplified version of my model:

import numpy as np
import tensorflow as tf

# Input is RAM, each byte in the range of [0, 255].
in_obs = tf.keras.layers.Input(shape=(4,))

# Normalize the observation to the range of [0, 1].
norm = tf.keras.layers.Lambda(lambda x: x / 255.0)(in_obs)

# Hidden layers.
dense1 = tf.keras.layers.Dense(128, activation="relu")(norm)
dense2 = tf.keras.layers.Dense(128, activation="relu")(dense1)
dense3 = tf.keras.layers.Dense(128, activation="relu")(dense2)
dense4 = tf.keras.layers.Dense(128, activation="relu")(dense3)

# Output prediction, which is an action to take.
out_pred = tf.keras.layers.Dense(2, activation="linear")(dense4)

opt     = tf.keras.optimizers.Adam(lr=5e-5)
network = tf.keras.models.Model(inputs=in_obs, outputs=out_pred)
network.compile(optimizer=opt, loss=huber_loss_mean_weighted)

这是我的自定义损失函数，它只是一个 Huber 损失乘以 IS 权重的实现:

Here's my custom loss function, which is just an implementation of Huber Loss multiplied by the IS weights:

'''
 ' Huber loss: https://en.wikipedia.org/wiki/Huber_loss
'''
def huber_loss(y_true, y_pred):
  error = y_true - y_pred
  cond  = tf.keras.backend.abs(error) < 1.0

  squared_loss = 0.5 * tf.keras.backend.square(error)
  linear_loss  = tf.keras.backend.abs(error) - 0.5

  return tf.where(cond, squared_loss, linear_loss)

'''
 ' Importance Sampling weighted huber loss.
'''
def huber_loss_mean_weighted(y_true, y_pred, is_weights):
  error = huber_loss(y_true, y_pred)

  return tf.keras.backend.mean(error * is_weights)

重要的一点是 is_weights 是动态的，即每次 fit() 被调用时它都是不同的.因此，我不能简单地关闭 is_weights 如下所述:在keras中制作自定义损失函数

The important bit is that is_weights is dynamic, i.e. it's different each time fit() is called. As such, I cannot simply close over is_weights as described here: Make a custom loss function in keras

我在网上找到了这段代码，它似乎使用了 Lambda 层来计算损失:https://github.com/keras-team/keras/blob/master/examples/image_ocr.py#L475 看起来很有希望，但我我正在努力理解它/使其适应我的特定问题.任何帮助表示赞赏.

I found this code online, which appears to use a Lambda layer to compute the loss: https://github.com/keras-team/keras/blob/master/examples/image_ocr.py#L475 It looks promising, but I'm struggling to understand it/adapt it to my particular problem. Any help is appreciated.

推荐答案

好的.这是一个例子.

from keras.layers import Input, Dense, Conv2D, MaxPool2D, Flatten
from keras.models import Model
from keras.losses import categorical_crossentropy

def sample_loss( y_true, y_pred, is_weight ) :
    return is_weight * categorical_crossentropy( y_true, y_pred ) 

x = Input(shape=(32,32,3), name='image_in')
y_true = Input( shape=(10,), name='y_true' )
is_weight = Input(shape=(1,), name='is_weight')
f = Conv2D(16,(3,3),padding='same')(x)
f = MaxPool2D((2,2),padding='same')(f)
f = Conv2D(32,(3,3),padding='same')(f)
f = MaxPool2D((2,2),padding='same')(f)
f = Conv2D(64,(3,3),padding='same')(f)
f = MaxPool2D((2,2),padding='same')(f)
f = Flatten()(f)
y_pred = Dense(10, activation='softmax', name='y_pred' )(f)
model = Model( inputs=[x, y_true, is_weight], outputs=y_pred, name='train_only' )
model.add_loss( sample_loss( y_true, y_pred, is_weight ) )
model.compile( loss=None, optimizer='sgd' )
print model.summary()

注意，由于您已经通过add_loss() 添加了损失，因此您不必通过compile( loss=xxx ) 来添加.

Note, since you've add loss through add_loss(), you don't have to do it through compile( loss=xxx ).

关于训练模型，除了将 y_true 移动到输入端之外，没有什么特别的.见下文

With regards to train a model, nothing is special except you move y_true to your input end. See below

import numpy as np 
a = np.random.randn(8,32,32,3)
a_true = np.random.randn(8,10)
a_is_weight = np.random.randint(0,2,size=(8,1))
model.fit( [a, a_true, a_is_weight] )

最后，您可以制作一个测试模型(共享 model 中的所有权重)以便于使用，即

Finally, you can make a testing model (which share all weights in model) for easier use, i.e.

test_model = Model( inputs=x, outputs=y_pred, name='test_only' )
a_pred = test_model.predict( a )

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