Question

我正在尝试为分布式学习实现自定义正则函数，以实现等式中的惩罚函数

我将以上功能实现为逐层的正则化器，但是它给我带来了错误。期待社区的帮助

@tf.keras.utils.register_keras_serializable(package='Custom', name='esgd')
def esgd(w, wt, mu):
    delta = tf.math.square(tf.norm(w-wt))
    rl = (mu/2)*delta
    return rl

def model(w, wt, mu):
    model = tf.keras.models.Sequential([tf.keras.layers.Conv2D(32,(3,3), padding='same', activation='relu',input_shape=(28, 28, 1)),
                                tf.keras.layers.MaxPooling2D(2,2),
                                tf.keras.layers.Conv2D(64,(3,3), padding='same', activation='relu'),
                                tf.keras.layers.MaxPooling2D(2,2),
                                tf.keras.layers.Dropout(0.25),
                                tf.keras.layers.Flatten(),
                                tf.keras.layers.Dense(128,activation='relu',  kernel_initializer='ones',kernel_regularizer=esgd(w[0][7],wt[0][7],mu)
),
                                tf.keras.layers.Dropout(0.25),
                                tf.keras.layers.Dense(10, activation='softmax')
                               ])
    return model

-----错误-------

---> 59        model = init_model(w, wt, mu)
     60 
     61 #       model.set_weights(wei[0])

<ipython-input-5-e0796dd9fa55> in init_model(w, wt, mu)
     11                                 tf.keras.layers.Dropout(0.25),
     12                                 tf.keras.layers.Flatten(),
---> 13                                 tf.keras.layers.Dense(128,activation='relu',  kernel_initializer='ones',kernel_regularizer=esgd(w[0][7],wt[0][7],mu)
     14 ),
     15                                 tf.keras.layers.Dropout(0.25),

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py in __init__(self, units, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, **kwargs)
   1137     self.kernel_initializer = initializers.get(kernel_initializer)
   1138     self.bias_initializer = initializers.get(bias_initializer)
-> 1139     self.kernel_regularizer = regularizers.get(kernel_regularizer)
   1140     self.bias_regularizer = regularizers.get(bias_regularizer)
   1141     self.kernel_constraint = constraints.get(kernel_constraint)

/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/regularizers.py in get(identifier)
    313     return identifier
    314   else:
--> 315     raise ValueError('Could not interpret regularizer identifier:', identifier)

ValueError: ('Could not interpret regularizer identifier:', <tf.Tensor: shape=(), dtype=float32, numpy=0.00068962533>)

Answer 1

根据Layer weight regularizers，如果要让正则化器采用除图层权重张量之外的其他参数，则必须将tensorflow.keras.regularizers.Regularizer子类化。

看起来您正在尝试支持序列化，所以不要忘记添加get_config方法。

from tensorflow.keras import regularizers

@tf.keras.utils.register_keras_serializable(package='Custom', name='esgd')
class ESGD(regularizers.Regularizer):

    def __init__(self, mu):
        self.mu = mu

    def __call__(self, w):
        return (mu/2) * tf.math.square(tf.norm(w - tf.transpose(w)))

    def get_config(self):
        return {'mu': self.mu}

然后您可以将其与

一起使用

tf.keras.layers.Dense(128, activation='relu', kernel_initializer='ones', kernel_regularizer=ESGD(mu=mu))

如何在TF 2.X中实现Custom Keras Regularizer？

1 个答案: