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Fix model to actually introduce a bottleneck

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
2019-04-16 14:20:23 +02:00
parent 7edf5879e8
commit 18fc46b70c
1 changed files with 9 additions and 9 deletions
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18
src/twomartens/masterthesis/aae/model.py
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@ -46,13 +46,13 @@ class Encoder(keras.Model):
def __init__(self, zsize: int) -> None: def __init__(self, zsize: int) -> None:
super().__init__(name='encoder') super().__init__(name='encoder')
weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02) weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
self.conv1 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=1, name='conv1', self.conv1 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=2, name='conv1',
padding='same', kernel_initializer=weight_init) padding='same', kernel_initializer=weight_init)
self.conv1_a = keras.layers.ReLU() self.conv1_a = keras.layers.ReLU()
self.conv2 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=1, name='conv2', self.conv2 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=2, name='conv2',
padding='same', kernel_initializer=weight_init) padding='same', kernel_initializer=weight_init)
self.conv2_a = keras.layers.ReLU() self.conv2_a = keras.layers.ReLU()
self.conv3 = keras.layers.Conv2D(filters=zsize, kernel_size=7, strides=1, name='conv3', self.conv3 = keras.layers.Conv2D(filters=zsize, kernel_size=7, strides=2, name='conv3',
padding='same', kernel_initializer=weight_init) padding='same', kernel_initializer=weight_init)
self.conv3_a = keras.layers.ReLU() self.conv3_a = keras.layers.ReLU()
@ -80,14 +80,14 @@ class Decoder(keras.Model):
def __init__(self, channels: int, zsize: int) -> None: def __init__(self, channels: int, zsize: int) -> None:
super().__init__(name='decoder') super().__init__(name='decoder')
weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02) weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
self.deconv1 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=1, name='deconv1', self.deconv1 = keras.layers.Conv2DTranspose(filters=zsize * 2, kernel_size=7, strides=2, name='deconv1',
padding='same', kernel_initializer=weight_init) padding='same', kernel_initializer=weight_init)
self.deconv1_a = keras.layers.ReLU() self.deconv1_a = keras.layers.ReLU()
self.deconv2 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=7, strides=1, name='deconv2', self.deconv2 = keras.layers.Conv2DTranspose(filters=zsize * 2, kernel_size=7, strides=2, name='deconv2',
padding='same', kernel_initializer=weight_init) padding='same', kernel_initializer=weight_init)
self.deconv2_a = keras.layers.ReLU() self.deconv2_a = keras.layers.ReLU()
self.deconv3 = keras.layers.Conv2D(filters=channels, kernel_size=7, strides=1, name='deconv3', self.deconv3 = keras.layers.Conv2DTranspose(filters=channels, kernel_size=7, strides=2, name='deconv3',
padding='same', kernel_initializer=weight_init) padding='same', kernel_initializer=weight_init)
def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor: def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
"""See base class.""" """See base class."""