2martens/masterthesis
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Set first hidden layer of encoder to sigmoid

Browse Source 
Sigmoid activation function hinders Dying ReLU effect.

Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-05-08 15:37:58 +02:00
parent 62ee6094f1
commit fa7a88191b
1 changed files with 2 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
src/twomartens/masterthesis/aae/model.py
View File

@ -47,8 +47,8 @@ class Encoder(keras.Model):
super().__init__(name='encoder')
weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
self.conv1 = keras.layers.Conv2D(filters=zsize * 4, kernel_size=3, strides=2, name='conv1',
padding='same', kernel_initializer=weight_init)
self.conv1_a = keras.layers.ReLU()
padding='same', kernel_initializer=weight_init,
activation=keras.activations.sigmoid)
self.conv2 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=3, strides=2, name='conv2',
padding='same', kernel_initializer=weight_init)
self.conv2_a = keras.layers.ReLU()
@ -61,7 +61,6 @@ class Encoder(keras.Model):
def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
"""See base class."""
result = self.conv1(inputs)
result = self.conv1_a(result)
result = self.conv2(result)
result = self.conv2_a(result)
result = self.conv3(result)