def _add_conv_2d(model: tf.keras.Sequential, filters: int):
model.add(tf.keras.layers.Conv2D(filters,
(4, 4),
strides=(2, 2),
padding='same',
use_bias=False,
kernel_initializer=tf.random_normal_initializer(0.0, 0.02)))
def _add_conv_2d(self,
model: tf.keras.Sequential,
filters: int,
strides: (int, int) = DISCRIMINATOR_STRIDE_SIZE) -> None:
model.add(
tf.keras.layers.Conv2D(
filters,
KERNEL_SIZE,
strides=strides,
padding=PADDING,
use_bias=USE_BIAS,
kernel_initializer=tf.random_normal_initializer(0.0, 0.02)))
def add_classifier(feature_extractor: tf.keras.Sequential, n_class: int, dropout: float=0.4) -> \
tf.keras.Sequential:
"""
Add classification layer to feature extraction model
:param feature_extractor: model to extract features
:param n_class: classes to differentiate
:return: classifier model
"""
feature_extractor.add(Flatten())
feature_extractor.add(Dense(500, activation='relu'))
feature_extractor.add(Dropout(dropout))
feature_extractor.add(Dense(n_class, activation='softmax'))
return feature_extractor
def _add_activation(self, model: tf.keras.Sequential) -> None:
model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
def _add_zeros_padding(self, model: tf.keras.Sequential) -> None:
model.add(tf.keras.layers.ZeroPadding2D())
def _add_dropout(self, model: tf.keras.Sequential) -> None:
if ADD_DROPOUT_D:
model.add(tf.keras.layers.Dropout(rate=DROPOUT_D_RATE))
def _add_batch_norm(self, model: tf.keras.Sequential) -> None:
model.add(tf.keras.layers.BatchNormalization(
momentum=0.8,
gamma_initializer=tf.random_normal_initializer(1.0, 0.02)))
def _add_last_activation(self, model: tf.keras.Sequential) -> None:
model.add(tf.keras.layers.Activation('tanh'))
def _add_upsampling(self, model: tf.keras.Sequential) -> None:
if UPSAMPLING:
model.add(tf.keras.layers.UpSampling2D(
interpolation=INTERPOLATION))
def _add_dropout(model: tf.keras.Sequential):
model.add(tf.keras.layers.Dropout(rate=0.5))
def _add_discriminator_activation(model: tf.keras.Sequential):
model.add(tf.keras.layers.LeakyReLU(alpha=0.2))
def _add_upsampling(model: tf.keras.Sequential):
model.add(tf.keras.layers.UpSampling2D())
def _add_generator_activation(model: tf.keras.Sequential):
model.add(tf.keras.layers.ReLU())
def add_dense(network: tf.keras.Sequential,
layer_dims: List[int],
input_shape=None,
activation="relu",
first_l1: float = 0.0,
first_l2: float = 0.0,
p_dropout: float = None,
*args,
**kwargs):
"""
Build a dense model with the given hidden state
:param network: sequential Keras network
:param layer_dims: list of hidden state dimensions
:param first_l1: L1 kernel regulariser on the first layer
:param first_l2: L2 kernel regulariser on the first layer
:param p_dropout: dropout percentage after the first layer
:param args: passed to Keras dense layer
:param kwargs: passed to Keras dense layer
:return: sequential Keras dense model
:return:
"""
# First layer
if input_shape:
network.add(
tf.keras.layers.Dense(
layer_dims[0],
input_shape=input_shape,
kernel_regularizer=tf.keras.regularizers.L1L2(l1=first_l1,
l2=first_l2),
bias_regularizer=tf.keras.regularizers.L1L2(l1=first_l1,
l2=first_l2),
))
else:
network.add(
tf.keras.layers.Dense(
layer_dims[0],
kernel_regularizer=tf.keras.regularizers.L1L2(l1=first_l1,
l2=first_l2),
bias_regularizer=tf.keras.regularizers.L1L2(l1=first_l1,
l2=first_l2),
))
network.add(tf.keras.layers.Activation(activation))
if p_dropout:
network.add(tf.keras.layers.Dropout(p_dropout))
# All the other feature_layers
for cur_dim in layer_dims[1:]:
network.add(tf.keras.layers.Dense(cur_dim, *args, **kwargs))
network.add(tf.keras.layers.Activation(activation))
def add_symmetric_autoencoder(network: tf.keras.Sequential,
layer_dims: List[int],
input_shape=None,
activation="relu",
*args,
**kwargs) -> tf.keras.Sequential:
"""
Build autoencoder where the hidden state dimensions of the en- and decoder are the same
:param network: sequential Keras network
:param layer_dims: list of hidden state dimensions
:param args: passed to Keras dense layer
:param kwargs: passed to Keras dense layer
:return: sequential Keras autoencoder model
"""
# First layer
network.add(
tf.keras.layers.Dense(layer_dims[0], input_shape=input_shape))
network.add(tf.keras.layers.Activation(activation))
# Encoder
for cur_dim in layer_dims[1:]:
network.add(tf.keras.layers.Dense(cur_dim, *args, **kwargs))
network.add(tf.keras.layers.Activation(activation))
# Decoder
for cur_dim in reversed(layer_dims[:-1]):
network.add(tf.keras.layers.Dense(cur_dim, *args, **kwargs))
network.add(tf.keras.layers.Activation(activation))