SeLU 激活函数 x 参数导致类型错误
SeLU Activation Function x-Parameter causes a typeError
我正在构建一个 CNN 并定义一个完全连接的层，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=
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我无法初始化 SeLU 激活函数的随机分布。我们将不胜感激！
首先，我认为可能不存在Activation(selu(x=dist))这样的用法。对于 selu 在 Activation 中用作 function 而不是 selu 的输出。 selu的实现如下：
@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 means to initialize the weights of the layers rather than selu. According to the official api here，我认为 selu 在你的情况下可以如下使用：
# 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，编码愉快。
我正在构建一个 CNN 并定义一个完全连接的层，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=
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我无法初始化 SeLU 激活函数的随机分布。我们将不胜感激！
首先，我认为可能不存在Activation(selu(x=dist))这样的用法。对于 selu 在 Activation 中用作 function 而不是 selu 的输出。 selu的实现如下：
@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 means to initialize the weights of the layers rather than selu. According to the official api here，我认为 selu 在你的情况下可以如下使用：
# 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，编码愉快。