我正在构建一个 CNN 并定义一个全连接层,SeLU 作为其激活和 AlphaDropout(0.5)。我正在尝试使用 tf.random.normal 分布初始化 SeLU,如下所示:

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

这是我的全连接层的代码:
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

我收到错误:
---------------------------------------------------------------------------
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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        [[ 1.66879535e-01,  6.54919222e-02, -3.27483788e-02,
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          -4.40548174e-02,  7.21732453e-02,  7.45785460e-02,
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        [[ 2.60874778e-01, -1.45940065e-01, -9.79427770e-02,
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        [[ 2.24076852e-01, -1.39667824e-01,  7.93220941e-03,
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          -7.96606317e-02,  1.50838092e-01, -4.71229590e-02,
          -4.02066261e-02,  1.17019311e-01]],

        [[-3.95799540e-02, -4.35096361e-02, -9.93420109e-02,
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          -8.08344856e-02, -4.56905663e-02,  1.26069590e-01,
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           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,

我无法初始化 SeLU 激活函数的随机分布。所有帮助将不胜感激!

最佳答案

首先,我认为可能不存在 Activation(selu(x=dist)) 这样的用法。对于 seluActivation 中用作 function 而不是 selu 的输出。 selu 的实现可以在下面找到:

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

在你的情况下,我认为 article 意味着初始化层的权重而不是 selu 。根据官方 api here ,我认为 selu 可以在您的情况下使用如下:

# 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

总而言之,快乐编码。

关于python - SeLU 激活函数 x 参数导致类型错误,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/60675024/

10-12 16:41