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Removed unnecessary layers

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
2019-04-12 14:16:48 +02:00
parent 568adeab08
commit d749678d20
1 changed files with 0 additions and 14 deletions
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src/twomartens/masterthesis/aae/model.py
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@ -51,10 +51,6 @@ class Encoder(keras.Model):
self.conv1_a = keras.layers.ReLU()
self.dropout = keras.layers.Dropout(rate=0.25)
self.pool1 = keras.layers.MaxPool2D(pool_size=(2, 2), padding='same', name='pool1')
self.conv2 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=1, name='conv2',
padding='same', kernel_initializer=weight_init)
self.conv2_a = keras.layers.ReLU()
self.pool2 = keras.layers.MaxPool2D(pool_size=(2, 2), padding='same', name='pool2')
self.conv3 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=1, name='conv3',
padding='same', kernel_initializer=weight_init)
self.conv3_a = keras.layers.ReLU()
@ -70,9 +66,6 @@ class Encoder(keras.Model):
result = self.conv1_a(result)
result = self.dropout(result)
result = self.pool1(result)
# result = self.conv2(result)
# result = self.conv2_a(result)
# result = self.pool2(result)
result = self.conv3(result)
result = self.conv3_a(result)
result = self.pool3(result)
@ -107,10 +100,6 @@ class Decoder(keras.Model):
padding='same', kernel_initializer=weight_init)
self.deconv3_a = keras.layers.ReLU()
self.upsample3 = keras.layers.UpSampling2D(size=(2, 2), name='upsample3')
self.deconv4 = keras.layers.Conv2D(filters=zsize * 3, kernel_size=7, strides=1, name='deconv4',
padding='same', kernel_initializer=weight_init)
self.deconv4_a = keras.layers.ReLU()
self.upsample4 = keras.layers.UpSampling2D(size=(2, 2), name='upsample4')
self.deconv5 = keras.layers.Conv2D(filters=channels, kernel_size=7, strides=1, name='deconv5',
padding='same', kernel_initializer=weight_init)
@ -125,9 +114,6 @@ class Decoder(keras.Model):
result = self.deconv3(result)
result = self.deconv3_a(result)
result = self.upsample3(result)
# result = self.deconv4(result)
# result = self.deconv4_a(result)
# result = self.upsample4(result)
result = self.deconv5(result)
result = k.sigmoid(result)