def create_model_brick():
decoder = MLP(
dims=[NLAT, GEN_HIDDEN, GEN_HIDDEN, GEN_HIDDEN, GEN_HIDDEN, INPUT_DIM],
activations=[Sequence([BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply],
name='decoder_h1'),
Sequence([BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply],
name='decoder_h2'),
Sequence([BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply],
name='decoder_h3'),
Sequence([BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply],
name='decoder_h4'),
Identity(name='decoder_out')],
use_bias=False,
name='decoder')
discriminator = Sequence(
application_methods=[
LinearMaxout(
input_dim=INPUT_DIM,
output_dim=DISC_HIDDEN,
num_pieces=MAXOUT_PIECES,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_h1').apply,
LinearMaxout(
input_dim=DISC_HIDDEN,
output_dim=DISC_HIDDEN,
num_pieces=MAXOUT_PIECES,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_h2').apply,
LinearMaxout(
input_dim=DISC_HIDDEN,
output_dim=DISC_HIDDEN,
num_pieces=MAXOUT_PIECES,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_h3').apply,
Linear(
input_dim=DISC_HIDDEN,
output_dim=1,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_out').apply],
name='discriminator')
gan = GAN(decoder=decoder, discriminator=discriminator,
weights_init=GAUSSIAN_INIT, biases_init=ZERO_INIT, name='gan')
gan.push_allocation_config()
decoder.linear_transformations[-1].use_bias = True
gan.initialize()
return gan
def create_model_bricks(z_dim, image_size, depth):
g_image_size = image_size
g_image_size2 = g_image_size / 2
g_image_size3 = g_image_size / 4
g_image_size4 = g_image_size / 8
g_image_size5 = g_image_size / 16
encoder_layers = []
if depth > 0:
encoder_layers = encoder_layers + [
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=32,
name='conv1'),
SpatialBatchNormalization(name='batch_norm1'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=32,
name='conv2'),
SpatialBatchNormalization(name='batch_norm2'),
Rectifier(),
Convolutional(
filter_size=(2, 2), step=(2, 2), num_filters=32, name='conv3'),
SpatialBatchNormalization(name='batch_norm3'),
Rectifier()
]
if depth > 1:
encoder_layers = encoder_layers + [
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=64,
name='conv4'),
SpatialBatchNormalization(name='batch_norm4'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=64,
name='conv5'),
SpatialBatchNormalization(name='batch_norm5'),
Rectifier(),
Convolutional(
filter_size=(2, 2), step=(2, 2), num_filters=64, name='conv6'),
SpatialBatchNormalization(name='batch_norm6'),
Rectifier()
]
if depth > 2:
encoder_layers = encoder_layers + [
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=128,
name='conv7'),
SpatialBatchNormalization(name='batch_norm7'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=128,
name='conv8'),
SpatialBatchNormalization(name='batch_norm8'),
Rectifier(),
Convolutional(
filter_size=(2, 2), step=(2, 2), num_filters=128,
name='conv9'),
SpatialBatchNormalization(name='batch_norm9'),
Rectifier()
]
if depth > 3:
encoder_layers = encoder_layers + [
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=256,
name='conv10'),
SpatialBatchNormalization(name='batch_norm10'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=256,
name='conv11'),
SpatialBatchNormalization(name='batch_norm11'),
Rectifier(),
Convolutional(filter_size=(2, 2),
step=(2, 2),
num_filters=256,
name='conv12'),
SpatialBatchNormalization(name='batch_norm12'),
Rectifier(),
]
if depth > 4:
encoder_layers = encoder_layers + [
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=512,
name='conv13'),
SpatialBatchNormalization(name='batch_norm13'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=512,
name='conv14'),
SpatialBatchNormalization(name='batch_norm14'),
Rectifier(),
Convolutional(filter_size=(2, 2),
step=(2, 2),
num_filters=512,
name='conv15'),
SpatialBatchNormalization(name='batch_norm15'),
Rectifier()
]
decoder_layers = []
if depth > 4:
decoder_layers = decoder_layers + [
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=512,
name='conv_n3'),
SpatialBatchNormalization(name='batch_norm_n3'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=512,
name='conv_n2'),
SpatialBatchNormalization(name='batch_norm_n2'),
Rectifier(),
ConvolutionalTranspose(
filter_size=(2, 2),
step=(2, 2),
original_image_size=(g_image_size5, g_image_size5),
num_filters=512,
name='conv_n1'),
SpatialBatchNormalization(name='batch_norm_n1'),
Rectifier()
]
if depth > 3:
decoder_layers = decoder_layers + [
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=256,
name='conv1'),
SpatialBatchNormalization(name='batch_norm1'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=256,
name='conv2'),
SpatialBatchNormalization(name='batch_norm2'),
Rectifier(),
ConvolutionalTranspose(
filter_size=(2, 2),
step=(2, 2),
original_image_size=(g_image_size4, g_image_size4),
num_filters=256,
name='conv3'),
SpatialBatchNormalization(name='batch_norm3'),
Rectifier()
]
if depth > 2:
decoder_layers = decoder_layers + [
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=128,
name='conv4'),
SpatialBatchNormalization(name='batch_norm4'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=128,
name='conv5'),
SpatialBatchNormalization(name='batch_norm5'),
Rectifier(),
ConvolutionalTranspose(
filter_size=(2, 2),
step=(2, 2),
original_image_size=(g_image_size3, g_image_size3),
num_filters=128,
name='conv6'),
SpatialBatchNormalization(name='batch_norm6'),
Rectifier()
]
if depth > 1:
decoder_layers = decoder_layers + [
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=64,
name='conv7'),
SpatialBatchNormalization(name='batch_norm7'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=64,
name='conv8'),
SpatialBatchNormalization(name='batch_norm8'),
Rectifier(),
ConvolutionalTranspose(
filter_size=(2, 2),
step=(2, 2),
original_image_size=(g_image_size2, g_image_size2),
num_filters=64,
name='conv9'),
SpatialBatchNormalization(name='batch_norm9'),
Rectifier()
]
if depth > 0:
decoder_layers = decoder_layers + [
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=32,
name='conv10'),
SpatialBatchNormalization(name='batch_norm10'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=32,
name='conv11'),
SpatialBatchNormalization(name='batch_norm11'),
Rectifier(),
ConvolutionalTranspose(
filter_size=(2, 2),
step=(2, 2),
original_image_size=(g_image_size, g_image_size),
num_filters=32,
name='conv12'),
SpatialBatchNormalization(name='batch_norm12'),
Rectifier()
]
decoder_layers = decoder_layers + [
Convolutional(filter_size=(1, 1), num_filters=3, name='conv_out'),
Logistic()
]
print("creating model of depth {} with {} encoder and {} decoder layers".
format(depth, len(encoder_layers), len(decoder_layers)))
encoder_convnet = ConvolutionalSequence(
layers=encoder_layers,
num_channels=3,
image_size=(g_image_size, g_image_size),
use_bias=False,
weights_init=IsotropicGaussian(0.033),
biases_init=Constant(0),
name='encoder_convnet')
encoder_convnet.initialize()
encoder_filters = numpy.prod(encoder_convnet.get_dim('output'))
encoder_mlp = MLP(
dims=[encoder_filters, 1000, z_dim],
activations=[
Sequence([BatchNormalization(1000).apply,
Rectifier().apply],
name='activation1'),
Identity().apply
],
weights_init=IsotropicGaussian(0.033),
biases_init=Constant(0),
name='encoder_mlp')
encoder_mlp.initialize()
decoder_mlp = BatchNormalizedMLP(
activations=[Rectifier(), Rectifier()],
dims=[encoder_mlp.output_dim // 2, 1000, encoder_filters],
weights_init=IsotropicGaussian(0.033),
biases_init=Constant(0),
name='decoder_mlp')
decoder_mlp.initialize()
decoder_convnet = ConvolutionalSequence(
layers=decoder_layers,
num_channels=encoder_convnet.get_dim('output')[0],
image_size=encoder_convnet.get_dim('output')[1:],
use_bias=False,
weights_init=IsotropicGaussian(0.033),
biases_init=Constant(0),
name='decoder_convnet')
decoder_convnet.initialize()
return encoder_convnet, encoder_mlp, decoder_convnet, decoder_mlp
def create_model_bricks():
encoder_convnet = ConvolutionalSequence(
layers=[
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=32,
name='conv1'),
SpatialBatchNormalization(name='batch_norm1'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=32,
name='conv2'),
SpatialBatchNormalization(name='batch_norm2'),
Rectifier(),
Convolutional(filter_size=(2, 2),
step=(2, 2),
num_filters=32,
name='conv3'),
SpatialBatchNormalization(name='batch_norm3'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=64,
name='conv4'),
SpatialBatchNormalization(name='batch_norm4'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=64,
name='conv5'),
SpatialBatchNormalization(name='batch_norm5'),
Rectifier(),
Convolutional(filter_size=(2, 2),
step=(2, 2),
num_filters=64,
name='conv6'),
SpatialBatchNormalization(name='batch_norm6'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=128,
name='conv7'),
SpatialBatchNormalization(name='batch_norm7'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=128,
name='conv8'),
SpatialBatchNormalization(name='batch_norm8'),
Rectifier(),
Convolutional(filter_size=(2, 2),
step=(2, 2),
num_filters=128,
name='conv9'),
SpatialBatchNormalization(name='batch_norm9'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=256,
name='conv10'),
SpatialBatchNormalization(name='batch_norm10'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=256,
name='conv11'),
SpatialBatchNormalization(name='batch_norm11'),
Rectifier(),
Convolutional(filter_size=(2, 2),
step=(2, 2),
num_filters=256,
name='conv12'),
SpatialBatchNormalization(name='batch_norm12'),
Rectifier(),
],
num_channels=3,
image_size=(64, 64),
use_bias=False,
weights_init=IsotropicGaussian(0.033),
biases_init=Constant(0),
name='encoder_convnet')
encoder_convnet.initialize()
encoder_filters = numpy.prod(encoder_convnet.get_dim('output'))
encoder_mlp = MLP(
dims=[encoder_filters, 1000, 1000],
activations=[
Sequence([BatchNormalization(1000).apply,
Rectifier().apply],
name='activation1'),
Identity().apply
],
weights_init=IsotropicGaussian(0.033),
biases_init=Constant(0),
name='encoder_mlp')
encoder_mlp.initialize()
decoder_mlp = BatchNormalizedMLP(
activations=[Rectifier(), Rectifier()],
dims=[encoder_mlp.output_dim // 2, 1000, encoder_filters],
weights_init=IsotropicGaussian(0.033),
biases_init=Constant(0),
name='decoder_mlp')
decoder_mlp.initialize()
decoder_convnet = ConvolutionalSequence(
layers=[
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=256,
name='conv1'),
SpatialBatchNormalization(name='batch_norm1'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=256,
name='conv2'),
SpatialBatchNormalization(name='batch_norm2'),
Rectifier(),
ConvolutionalTranspose(filter_size=(2, 2),
step=(2, 2),
original_image_size=(8, 8),
num_filters=256,
name='conv3'),
SpatialBatchNormalization(name='batch_norm3'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=128,
name='conv4'),
SpatialBatchNormalization(name='batch_norm4'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=128,
name='conv5'),
SpatialBatchNormalization(name='batch_norm5'),
Rectifier(),
ConvolutionalTranspose(filter_size=(2, 2),
step=(2, 2),
original_image_size=(16, 16),
num_filters=128,
name='conv6'),
SpatialBatchNormalization(name='batch_norm6'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=64,
name='conv7'),
SpatialBatchNormalization(name='batch_norm7'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=64,
name='conv8'),
SpatialBatchNormalization(name='batch_norm8'),
Rectifier(),
ConvolutionalTranspose(filter_size=(2, 2),
step=(2, 2),
original_image_size=(32, 32),
num_filters=64,
name='conv9'),
SpatialBatchNormalization(name='batch_norm9'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=32,
name='conv10'),
SpatialBatchNormalization(name='batch_norm10'),
Rectifier(),
Convolutional(filter_size=(3, 3),
border_mode=(1, 1),
num_filters=32,
name='conv11'),
SpatialBatchNormalization(name='batch_norm11'),
Rectifier(),
ConvolutionalTranspose(filter_size=(2, 2),
step=(2, 2),
original_image_size=(64, 64),
num_filters=32,
name='conv12'),
SpatialBatchNormalization(name='batch_norm12'),
Rectifier(),
Convolutional(filter_size=(1, 1), num_filters=3, name='conv_out'),
Logistic(),
],
num_channels=encoder_convnet.get_dim('output')[0],
image_size=encoder_convnet.get_dim('output')[1:],
use_bias=False,
weights_init=IsotropicGaussian(0.033),
biases_init=Constant(0),
name='decoder_convnet')
decoder_convnet.initialize()
return encoder_convnet, encoder_mlp, decoder_convnet, decoder_mlp
def create_model_brick():
encoder_mapping = MLP(
dims=[2 * INPUT_DIM, GEN_HIDDEN, GEN_HIDDEN, NLAT],
activations=[
Sequence([
BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply
],
name='encoder_h1'),
Sequence([
BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply
],
name='encoder_h2'),
Identity(name='encoder_out')
],
use_bias=False,
name='encoder_mapping')
encoder = COVConditional(encoder_mapping, (INPUT_DIM, ),
name='encoder')
decoder_mapping = MLP(dims=[
NLAT, GEN_HIDDEN, GEN_HIDDEN, GEN_HIDDEN, GEN_HIDDEN, INPUT_DIM
],
activations=[
Sequence([
BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply
],
name='decoder_h1'),
Sequence([
BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply
],
name='decoder_h2'),
Sequence([
BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply
],
name='decoder_h3'),
Sequence([
BatchNormalization(GEN_HIDDEN).apply,
GEN_ACTIVATION().apply
],
name='decoder_h4'),
Identity(name='decoder_out')
],
use_bias=False,
name='decoder_mapping')
decoder = DeterministicConditional(decoder_mapping, name='decoder')
x_discriminator = Identity(name='x_discriminator')
z_discriminator = Identity(name='z_discriminator')
joint_discriminator = Sequence(application_methods=[
LinearMaxout(input_dim=INPUT_DIM + NLAT,
output_dim=DISC_HIDDEN,
num_pieces=MAXOUT_PIECES,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_h1').apply,
LinearMaxout(input_dim=DISC_HIDDEN,
output_dim=DISC_HIDDEN,
num_pieces=MAXOUT_PIECES,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_h2').apply,
LinearMaxout(input_dim=DISC_HIDDEN,
output_dim=DISC_HIDDEN,
num_pieces=MAXOUT_PIECES,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_h3').apply,
Linear(input_dim=DISC_HIDDEN,
output_dim=1,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='discriminator_out').apply
],
name='joint_discriminator')
discriminator = XZJointDiscriminator(x_discriminator,
z_discriminator,
joint_discriminator,
name='discriminator')
ali = ALI(encoder=encoder,
decoder=decoder,
discriminator=discriminator,
weights_init=GAUSSIAN_INIT,
biases_init=ZERO_INIT,
name='ali')
ali.push_allocation_config()
encoder_mapping.linear_transformations[-1].use_bias = True
decoder_mapping.linear_transformations[-1].use_bias = True
ali.initialize()
print("Number of parameters in discriminator: {}".format(
numpy.sum([
numpy.prod(v.shape.eval()) for v in Selector(
ali.discriminator).get_parameters().values()
])))
print("Number of parameters in encoder: {}".format(
numpy.sum([
numpy.prod(v.shape.eval())
for v in Selector(ali.encoder).get_parameters().values()
])))
print("Number of parameters in decoder: {}".format(
numpy.sum([
numpy.prod(v.shape.eval())
for v in Selector(ali.decoder).get_parameters().values()
])))
return ali
def create_model_brick(self):
decoder = MLP(
dims=[
self._config["num_zdim"], self._config["gen_hidden_size"],
self._config["gen_hidden_size"],
self._config["gen_hidden_size"],
self._config["gen_hidden_size"], self._config["num_xdim"]
],
activations=[
Sequence([
BatchNormalization(self._config["gen_hidden_size"]).apply,
self._config["gen_activation"]().apply
],
name='decoder_h1'),
Sequence([
BatchNormalization(self._config["gen_hidden_size"]).apply,
self._config["gen_activation"]().apply
],
name='decoder_h2'),
Sequence([
BatchNormalization(self._config["gen_hidden_size"]).apply,
self._config["gen_activation"]().apply
],
name='decoder_h3'),
Sequence([
BatchNormalization(self._config["gen_hidden_size"]).apply,
self._config["gen_activation"]().apply
],
name='decoder_h4'),
Identity(name='decoder_out')
],
use_bias=False,
name='decoder')
discriminator = Sequence(application_methods=[
LinearMaxout(input_dim=self._config["num_xdim"] *
self._config["num_packing"],
output_dim=self._config["disc_hidden_size"],
num_pieces=self._config["disc_maxout_pieces"],
weights_init=IsotropicGaussian(
self._config["weights_init_std"]),
biases_init=self._config["biases_init"],
name='discriminator_h1').apply,
LinearMaxout(input_dim=self._config["disc_hidden_size"],
output_dim=self._config["disc_hidden_size"],
num_pieces=self._config["disc_maxout_pieces"],
weights_init=IsotropicGaussian(
self._config["weights_init_std"]),
biases_init=self._config["biases_init"],
name='discriminator_h2').apply,
LinearMaxout(input_dim=self._config["disc_hidden_size"],
output_dim=self._config["disc_hidden_size"],
num_pieces=self._config["disc_maxout_pieces"],
weights_init=IsotropicGaussian(
self._config["weights_init_std"]),
biases_init=self._config["biases_init"],
name='discriminator_h3').apply,
Linear(input_dim=self._config["disc_hidden_size"],
output_dim=1,
weights_init=IsotropicGaussian(
self._config["weights_init_std"]),
biases_init=self._config["biases_init"],
name='discriminator_out').apply
],
name='discriminator')
gan = PacGAN(decoder=decoder,
discriminator=discriminator,
weights_init=IsotropicGaussian(
self._config["weights_init_std"]),
biases_init=self._config["biases_init"],
name='gan')
gan.push_allocation_config()
decoder.linear_transformations[-1].use_bias = True
gan.initialize()
print("Number of parameters in discriminator: {}".format(
numpy.sum([
numpy.prod(v.shape.eval())
for v in Selector(gan.discriminator).get_parameters().values()
])))
print("Number of parameters in decoder: {}".format(
numpy.sum([
numpy.prod(v.shape.eval())
for v in Selector(gan.decoder).get_parameters().values()
])))
return gan