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Updated docstring for model module

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Signed-off-by: Jim Martens <github@2martens.de>
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Jim Martens
2019-02-08 21:57:57 +01:00
parent 81f245be99
commit 85f3100b5d
1 changed files with 123 additions and 96 deletions
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src/twomartens/masterthesis/aae/model.py
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@ -14,56 +14,65 @@
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
"""aae.model.py: contains model definitions""" """
Provides the models of my AAE implementation.
Classes:
``Encoder``: encodes an image input to a latent space
``Decoder``: decodes data from a latent space to resemble input data
``XDiscriminator``: differentiates between real input data and decoded input data
``ZDiscriminator``: differentiates between z values drawn from a normal distribution (real) and the encoded input
(fake)
"""
import tensorflow as tf import tensorflow as tf
# shortcuts for tensorflow - quasi imports # shortcuts for tensorflow - quasi imports
keras = tf.keras keras = tf.keras
k = tf.keras.backend k = tf.keras.backend
Model = keras.Model
sigmoid = keras.activations.sigmoid
RandomNormal = keras.initializers.RandomNormal
BatchNormalization = keras.layers.BatchNormalization
Conv2D = keras.layers.Conv2D
Conv2DTranspose = keras.layers.Conv2DTranspose
Dense = keras.layers.Dense
Cropping2D = keras.layers.Cropping2D
ZeroPadding2D = keras.layers.ZeroPadding2D
ReLU = keras.layers.ReLU
LeakyReLU = keras.layers.LeakyReLU
class Encoder(Model): class Encoder(keras.Model):
""" """
Encoder model. Encodes input to a latent space.
Args:
zsize: size of the latent space
""" """
def __init__(self, zsize: int) -> None: def __init__(self, zsize: int) -> None:
super().__init__(name='encoder') super().__init__(name='encoder')
weight_init = RandomNormal(mean=0, stddev=0.02) weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
self.x_padded = ZeroPadding2D(padding=1) self.x_padded = keras.layers.ZeroPadding2D(padding=1)
self.conv1 = Conv2D(filters=64, kernel_size=4, strides=2, name='conv1', self.conv1 = keras.layers.Conv2D(filters=64, kernel_size=4, strides=2, name='conv1',
padding='valid', kernel_initializer=weight_init) padding='valid', kernel_initializer=weight_init)
self.conv1_a = LeakyReLU(alpha=0.2) self.conv1_a = keras.layers.LeakyReLU(alpha=0.2)
self.conv1_a_padded = ZeroPadding2D(padding=1) self.conv1_a_padded = keras.layers.ZeroPadding2D(padding=1)
self.conv2 = Conv2D(filters=256, kernel_size=4, strides=2, name='conv2', self.conv2 = keras.layers.Conv2D(filters=256, kernel_size=4, strides=2, name='conv2',
padding='valid', kernel_initializer=weight_init) padding='valid', kernel_initializer=weight_init)
self.conv2_bn = BatchNormalization() self.conv2_bn = keras.layers.BatchNormalization()
self.conv2_a = LeakyReLU(alpha=0.2) self.conv2_a = keras.layers.LeakyReLU(alpha=0.2)
self.conv2_a_padded = ZeroPadding2D(padding=1) self.conv2_a_padded = keras.layers.ZeroPadding2D(padding=1)
self.conv3 = Conv2D(filters=512, kernel_size=4, strides=2, name='conv3', self.conv3 = keras.layers.Conv2D(filters=512, kernel_size=4, strides=2, name='conv3',
padding='valid', kernel_initializer=weight_init) padding='valid', kernel_initializer=weight_init)
self.conv3_bn = BatchNormalization() self.conv3_bn = keras.layers.BatchNormalization()
self.conv3_a = LeakyReLU(alpha=0.2) self.conv3_a = keras.layers.LeakyReLU(alpha=0.2)
self.conv4 = Conv2D(filters=zsize, kernel_size=4, strides=1, name='conv4', self.conv4 = keras.layers.Conv2D(filters=zsize, kernel_size=4, strides=1, name='conv4',
padding='valid', kernel_initializer=weight_init) padding='valid', kernel_initializer=weight_init)
def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor: def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
""" """
Performs the forward pass. Overwrites the ``call`` method and is called by ``__call__``.
:param inputs: input values
:param kwargs: additional keyword arguments - none are used Args:
:return: result values inputs: input values
``**kwargs``: additional keyword arguments - none are used
Returns:
result values
""" """
result = self.x_padded(inputs) result = self.x_padded(inputs)
result = self.conv1(result) result = self.conv1(result)
@ -81,38 +90,45 @@ class Encoder(Model):
return result return result
class Decoder(Model): class Decoder(keras.Model):
""" """
Decoder model. Generates input data from latent space values.
Args:
channels: number of channels in the input image
""" """
def __init__(self, channels: int) -> None: def __init__(self, channels: int) -> None:
super().__init__(name='decoder') super().__init__(name='decoder')
weight_init = RandomNormal(mean=0, stddev=0.02) weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
self.deconv1 = Conv2DTranspose(filters=256, kernel_size=4, strides=1, name='deconv1', self.deconv1 = keras.layers.Conv2DTranspose(filters=256, kernel_size=4, strides=1, name='deconv1',
padding='valid', kernel_initializer=weight_init) padding='valid', kernel_initializer=weight_init)
self.deconv1_bn = BatchNormalization() self.deconv1_bn = keras.layers.BatchNormalization()
self.deconv1_a = ReLU() self.deconv1_a = keras.layers.ReLU()
self.deconv2 = Conv2DTranspose(filters=256, kernel_size=4, strides=2, name='deconv2', self.deconv2 = keras.layers.Conv2DTranspose(filters=256, kernel_size=4, strides=2, name='deconv2',
padding='valid', kernel_initializer=weight_init) padding='valid', kernel_initializer=weight_init)
self.deconv2_cropped = Cropping2D(cropping=1) self.deconv2_cropped = keras.layers.Cropping2D(cropping=1)
self.deconv2_bn = BatchNormalization() self.deconv2_bn = keras.layers.BatchNormalization()
self.deconv2_a = ReLU() self.deconv2_a = keras.layers.ReLU()
self.deconv3 = Conv2DTranspose(filters=128, kernel_size=4, strides=2, name='deconv3', self.deconv3 = keras.layers.Conv2DTranspose(filters=128, kernel_size=4, strides=2, name='deconv3',
padding='valid', kernel_initializer=weight_init) padding='valid', kernel_initializer=weight_init)
self.deconv3_cropped = Cropping2D(cropping=1) self.deconv3_cropped = keras.layers.Cropping2D(cropping=1)
self.deconv3_bn = BatchNormalization() self.deconv3_bn = keras.layers.BatchNormalization()
self.deconv3_a = ReLU() self.deconv3_a = keras.layers.ReLU()
self.deconv4 = Conv2DTranspose(filters=channels, kernel_size=4, strides=2, name='deconv4', self.deconv4 = keras.layers.Conv2DTranspose(filters=channels, kernel_size=4, strides=2, name='deconv4',
padding='valid', kernel_initializer=weight_init) padding='valid', kernel_initializer=weight_init)
self.deconv4_cropped = Cropping2D(cropping=1) self.deconv4_cropped = keras.layers.Cropping2D(cropping=1)
def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor: def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
""" """
Performs the forward pass. Overwrites the ``call`` method and is called by ``__call__``.
:param inputs: input values
:param kwargs: additional keyword arguments - none are used Args:
:return: result values inputs: input values
``**kwargs``: additional keyword arguments - none are used
Returns:
result values
""" """
result = self.deconv1(inputs) result = self.deconv1(inputs)
result = self.deconv1_bn(result) result = self.deconv1_bn(result)
@ -132,27 +148,34 @@ class Decoder(Model):
return result return result
class ZDiscriminator(Model): class ZDiscriminator(keras.Model):
""" """
ZDiscriminator model Discriminates between encoded inputs and latent space distribution.
The latent space value is drawn from a normal distribution with ``0`` mean
and a variance of ``1``.
""" """
def __init__(self) -> None: def __init__(self) -> None:
super().__init__(name='zdiscriminator') super().__init__(name='zdiscriminator')
weight_init = RandomNormal(mean=0, stddev=0.02) weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
self.zd1 = Dense(units=128, name='zd1', kernel_initializer=weight_init) self.zd1 = keras.layers.Dense(units=128, name='zd1', kernel_initializer=weight_init)
self.zd1_a = LeakyReLU(alpha=0.2) self.zd1_a = keras.layers.LeakyReLU(alpha=0.2)
self.zd2 = Dense(units=128, name='zd2', kernel_initializer=weight_init) self.zd2 = keras.layers.Dense(units=128, name='zd2', kernel_initializer=weight_init)
self.zd2_a = LeakyReLU(alpha=0.2) self.zd2_a = keras.layers.LeakyReLU(alpha=0.2)
self.zd3 = Dense(units=1, name='zd3', activation='sigmoid', self.zd3 = keras.layers.Dense(units=1, name='zd3', activation='sigmoid',
kernel_initializer=weight_init) kernel_initializer=weight_init)
def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor: def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
""" """
Performs the forward pass. Overwrites the ``call`` method and is called by ``__call__``.
:param inputs: input values
:param kwargs: additional keyword arguments - none are used Args:
:return: result values inputs: input values
``**kwargs``: additional keyword arguments - none are used
Returns:
result values
""" """
result = self.zd1(inputs) result = self.zd1(inputs)
result = self.zd1_a(result) result = self.zd1_a(result)
@ -163,38 +186,42 @@ class ZDiscriminator(Model):
return result return result
class XDiscriminator(Model): class XDiscriminator(keras.Model):
""" """
XDiscriminator model Discriminates between generated inputs and the actual inputs.
""" """
def __init__(self) -> None: def __init__(self) -> None:
super().__init__(name='xdiscriminator') super().__init__(name='xdiscriminator')
weight_init = RandomNormal(mean=0, stddev=0.02) weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
self.x_padded = ZeroPadding2D(padding=1) self.x_padded = keras.layers.ZeroPadding2D(padding=1)
self.xd1 = Conv2D(filters=64, kernel_size=4, strides=2, name='xd1', self.xd1 = keras.layers.Conv2D(filters=64, kernel_size=4, strides=2, name='xd1',
padding='valid', kernel_initializer=weight_init) padding='valid', kernel_initializer=weight_init)
self.xd1_a = LeakyReLU(alpha=0.2) self.xd1_a = keras.layers.LeakyReLU(alpha=0.2)
self.xd1_a_padded = ZeroPadding2D(padding=1) self.xd1_a_padded = keras.layers.ZeroPadding2D(padding=1)
self.xd2 = Conv2D(filters=256, kernel_size=4, strides=2, name='xd2', self.xd2 = keras.layers.Conv2D(filters=256, kernel_size=4, strides=2, name='xd2',
padding='valid', kernel_initializer=weight_init) padding='valid', kernel_initializer=weight_init)
self.xd2_bn = BatchNormalization() self.xd2_bn = keras.layers.BatchNormalization()
self.xd2_a = LeakyReLU(alpha=0.2) self.xd2_a = keras.layers.LeakyReLU(alpha=0.2)
self.xd2_a_padded = ZeroPadding2D(padding=1) self.xd2_a_padded = keras.layers.ZeroPadding2D(padding=1)
self.xd3 = Conv2D(filters=512, kernel_size=4, strides=2, name='xd3', self.xd3 = keras.layers.Conv2D(filters=512, kernel_size=4, strides=2, name='xd3',
padding='valid', kernel_initializer=weight_init) padding='valid', kernel_initializer=weight_init)
self.xd3_bn = BatchNormalization() self.xd3_bn = keras.layers.BatchNormalization()
self.xd3_a = LeakyReLU(alpha=0.2) self.xd3_a = keras.layers.LeakyReLU(alpha=0.2)
self.xd4 = Conv2D(filters=1, kernel_size=4, strides=1, name='xd4', self.xd4 = keras.layers.Conv2D(filters=1, kernel_size=4, strides=1, name='xd4',
padding='valid', kernel_initializer=weight_init, padding='valid', kernel_initializer=weight_init,
activation='sigmoid') activation='sigmoid')
def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor: def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
""" """
Performs the forward pass. Overwrites the ``call`` method and is called by ``__call__``.
:param inputs: input values
:param kwargs: additional keyword arguments - none are used Args:
:return: result values inputs: input values
``**kwargs``: additional keyword arguments - none are used
Returns:
result values
""" """
result = self.x_padded(inputs) result = self.x_padded(inputs)
result = self.xd1(result) result = self.xd1(result)