Функция активации SeLU x-Parameter вызывает ошибку типа

Question

Я создаю CNN и определяю полностью связанный слой с SeLU в качестве его активации и AlphaDropout (0.5). Я пытаюсь инициализировать SeLU с распределением tf.random.normal следующим образом:

dist = tf.Variable(tf.random.normal([5, 5, 1, 32], stddev=np.sqrt(1/25)))

Вот код для моего полностью подключенного слоя:

def FullyConnectedLayer(denseUnits, seluDistribution, batchMomentum, alphaDropRate):
    model.add(Dense(denseUnits, activity_regularizer='l2'))
    model.add(Activation(selu(x=seluDistribution)))
    model.add(BatchNormalization(axis=-1, momentum=batchMomentum, epsilon=0.001))
    model.add(AlphaDropout(alphaDropRate, noise_shape=None, seed=None))
    return model

model = FullyConnectedLayer(512, dist, 0.99, 0.5) # 4 LAYERS

Я получаю ошибку :

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-121-f0000c6b1512> in <module>
     11 model = ConvAvgStack                  (256, (3, 3), (1, 1), 1, 0.99, 0.3, None, (2, 2), (2, 2)) # 5 LAYERS
     12 model = FlattenLayer                  (                                                       ) # 1 LAYER
---> 13 model = FullyConnectedLayer           (512,   dist,            0.99, 0.5                      ) # 4 LAYERS
     14 model = FullyConnectedLayer           (512,   dist,            0.99, 0.5                      ) # 4 LAYERS
     15 model = OutputLayer                   ( 28                                                    ) # 2 LAYERS

<ipython-input-119-58375bdf8845> in FullyConnectedLayer(denseUnits, seluDistribution, batchMomentum, alphaDropRate)
     56 def FullyConnectedLayer(denseUnits, seluDistribution, batchMomentum, alphaDropRate):
     57     model.add(Dense(denseUnits, activity_regularizer='l2'))
---> 58     model.add(Activation(gelu(x=seluDistribution)))
     59     model.add(BatchNormalization(axis=-1, momentum=batchMomentum, epsilon=0.001))
     60     model.add(AlphaDropout(alphaDropRate, noise_shape=None, seed=None))

~\Anaconda3\envs\py36\lib\site-packages\tensorflow_core\python\keras\layers\core.py in __init__(self, activation, **kwargs)
    376     super(Activation, self).__init__(**kwargs)
    377     self.supports_masking = True
--> 378     self.activation = activations.get(activation)
    379 
    380   def call(self, inputs):

~\Anaconda3\envs\py36\lib\site-packages\tensorflow_core\python\keras\activations.py in get(identifier)
    452     raise TypeError(
    453         'Could not interpret activation function identifier: {}'.format(
--> 454             repr(identifier)))

TypeError: Could not interpret activation function identifier: <tf.Tensor: shape=(5, 5, 1, 32), dtype=float32, numpy=
array([[[[-1.26586094e-01, -1.02963023e-01,  3.14652212e-02,
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           2.63120145e-01,  1.80331752e-01, -1.43979434e-02,
          -4.86476049e-02, -1.12639852e-01,  7.89660513e-02,
           1.24138966e-01,  5.12700714e-02, -1.20767031e-03,
          -1.09081008e-01, -3.03610712e-02]]],


       [[[-1.40361011e-01,  1.21919084e-02,  4.36685272e-02,
          -3.61564793e-02, -1.11773185e-01,  2.25092173e-02,
          -1.02469876e-01,  1.76996499e-01,  4.30173017e-02,
          -2.26258971e-02,  2.11037025e-01,  9.66922417e-02,
           5.76661676e-02,  9.65369982e-04, -1.35565817e-01,
          -4.83587980e-02,  4.68245940e-04, -1.47096828e-01,
           8.96992441e-03,  4.12831195e-02,  9.53651369e-02,
          -2.91392524e-02,  8.22411999e-02,  2.07852814e-02,
          -4.12134677e-02,  5.33621386e-02,  9.24792588e-02,
           8.16729572e-03,  4.25154343e-02,  6.19177930e-02,
           7.98290670e-02, -8.52704328e-03]],

        [[ 1.66879535e-01,  6.54919222e-02, -3.27483788e-02,
          -1.43241754e-03, -1.14416316e-01, -2.12962832e-02,
          -4.46583293e-02,  2.71647628e-02, -5.61558232e-02,
           1.09621109e-02,  1.67668343e-01,  3.30472551e-02,
           7.05115721e-02,  7.84466881e-03,  1.08160205e-01,
           2.66151220e-01,  1.52581872e-03,  7.19077215e-02,
          -1.24854170e-01,  1.25476092e-01, -7.09585026e-02,
          -4.40548174e-02,  7.21732453e-02,  7.45785460e-02,
           3.44901420e-02,  2.10928824e-02, -7.80880824e-02,
          -1.17296316e-01, -1.46051958e-01,  1.88378561e-02,
           6.55523613e-02,  3.32243517e-02]],

        [[ 2.60874778e-01, -1.45940065e-01, -9.79427770e-02,
          -8.68195742e-02,  2.04389215e-01, -2.24198923e-02,
           4.23102900e-02, -7.01505691e-02, -1.27080590e-01,
           6.70303479e-02,  1.60573255e-02, -7.93380756e-03,
          -8.38927086e-03, -4.99465019e-02,  4.69646640e-02,
          -7.15569034e-02, -1.78242605e-02, -8.51068646e-03,
           4.20920074e-01,  7.50197982e-03, -6.86415285e-02,
           7.11418912e-02,  1.07180420e-03, -9.36960131e-02,
           1.57825544e-01,  5.96512817e-02,  1.75660148e-01,
          -3.08227092e-02, -4.82530929e-02,  8.31630453e-02,
          -4.16018628e-02, -7.55471215e-02]],

        [[ 2.24076852e-01, -1.39667824e-01,  7.93220941e-03,
          -1.78845283e-02, -5.64770252e-02, -7.84719810e-02,
           5.26466146e-02,  6.62457757e-03,  2.76956528e-01,
           9.01412778e-03, -1.48465708e-01, -9.00324360e-02,
          -1.81565285e-02,  1.24106847e-01, -6.28474308e-03,
          -1.72791779e-02, -3.47166769e-02, -4.92920280e-02,
           1.33945951e-02, -1.16457433e-01, -1.28861982e-02,
           1.83324851e-02, -1.37674257e-01, -8.29964876e-02,
          -9.12440866e-02,  6.42236844e-02, -1.16013244e-01,
          -7.96606317e-02,  1.50838092e-01, -4.71229590e-02,
          -4.02066261e-02,  1.17019311e-01]],

        [[-3.95799540e-02, -4.35096361e-02, -9.93420109e-02,
           3.89132760e-02,  8.42780769e-02, -1.38364257e-02,
           2.48586033e-02, -8.65626428e-03,  1.72410719e-02,
          -6.20126911e-02,  1.93700612e-01,  5.02851121e-02,
          -9.00325775e-02,  1.32245719e-01,  2.68575907e-01,
          -8.08344856e-02, -4.56905663e-02,  1.26069590e-01,
           5.42675406e-02,  1.27283424e-01,  2.92954836e-02,
           2.07115993e-01, -1.58712193e-01, -2.03064550e-02,
          -6.64912462e-02,  9.61613879e-02, -1.48803489e-02,
           1.32543296e-01, -1.13899536e-01,  5.34827523e-02,

Я не могу инициализировать случайное распределение для функции активации SeLU. Будем благодарны за любую помощь!

Answer 1

Прежде всего, я думаю, что такого использования не может быть Activation(selu(x=dist)). Для selu используйте в Activation как function, а не как выход selu. Орудие selu можно найти ниже:

@keras_export('keras.activations.selu')
def selu(x):
  alpha = 1.6732632423543772848170429916717
  scale = 1.0507009873554804934193349852946
  return scale * K.elu(x, alpha)

В вашем случае, я думаю, article означает инициализацию весов слоев, а не selu. Согласно официальному API здесь , я думаю, что selu можно использовать, как показано ниже в вашем случае:

# official usage
model.add(Dense(16, kernel_initializer='lecun_normal', activation='selu')) 

# in your case, for the Dense layer refer to the standard layer in article
import numpy as np
import tensorflow as tf
from tensorflow.keras.activations import selu
from tensorflow.keras.layers import Dense, Activation, BatchNormalization, AlphaDropout
from tensorflow.keras import initializers

def FullyConnectedLayer(denseUnits, in_dim, batchMomentum, alphaDropRate):
    model = tf.keras.Sequential()
    model.add(Dense(denseUnits, activity_regularizer='l2', kernel_initializer=initializers.RandomNormal(stddev=np.sqrt(1/in_dim)), input_shape=(in_dim,)))
    model.add(Activation(selu))
    model.add(BatchNormalization(axis=-1, momentum=batchMomentum, epsilon=0.001))
    model.add(AlphaDropout(alphaDropRate, noise_shape=None, seed=None))
    return model

model = FullyConnectedLayer(512, 10, 0.99, 0.5) # 4 LAYERS

В целом, удачного кодирования.