[英]SeLU Activation Function x-Parameter causes a typeError
I am building a CNN and am defining a fully connected layer with SeLU as its activation and AlphaDropout(0.5).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)。 I am trying to initialize SeLU with a
tf.random.normal
distribution as follows: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)))
Here is the code for my fully connected layer: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
I get the error: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=
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-4.46583293e-02, 2.71647628e-02, -5.61558232e-02,
1.09621109e-02, 1.67668343e-01, 3.30472551e-02,
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-1.17296316e-01, -1.46051958e-01, 1.88378561e-02,
6.55523613e-02, 3.32243517e-02]],
[[ 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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-1.81565285e-02, 1.24106847e-01, -6.28474308e-03,
-1.72791779e-02, -3.47166769e-02, -4.92920280e-02,
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-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,
I am unable to initialize the random distribution for the SeLU activation function.我无法初始化 SeLU 激活函数的随机分布。 All help would be appreciated!
所有帮助将不胜感激!
First of all, I think there may no exist Activation(selu(x=dist))
such usage.首先,我认为可能不存在
Activation(selu(x=dist))
这样的用法。 For selu
use in the Activation
as a function
not the output of the selu
.对于
selu
在Activation
用作function
而不是selu
的输出。 The implement of the selu
can be found below: 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)
In your case, I think the article means to initialize the weights of the layers rather than selu
.在你的情况下,我认为这篇文章的意思是初始化层的权重而不是
selu
。 According to the official api here , I think selu can be used as below in your case: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
All in all, happy coding.总而言之,快乐编码。
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