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Added dense layer as latent space

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-04-18 12:13:21 +02:00
parent 532f4ac567
commit 71623db905
4 changed files with 45 additions and 17 deletions
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38
src/twomartens/masterthesis/aae/model.py
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@ -46,7 +46,7 @@ class Encoder(keras.Model):
def __init__(self, zsize: int) -> None:
super().__init__(name='encoder')
weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
self.conv1 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=3, strides=2, name='conv1',
self.conv1 = keras.layers.Conv2D(filters=zsize * 4, kernel_size=3, strides=2, name='conv1',
padding='same', kernel_initializer=weight_init)
self.conv1_a = keras.layers.ReLU()
self.conv2 = keras.layers.Conv2D(filters=zsize * 2, kernel_size=3, strides=2, name='conv2',
@ -55,6 +55,8 @@ class Encoder(keras.Model):
self.conv3 = keras.layers.Conv2D(filters=zsize, kernel_size=3, strides=2, name='conv3',
padding='same', kernel_initializer=weight_init)
self.conv3_a = keras.layers.ReLU()
self.flatten = keras.layers.Flatten(name='flatten')
self.latent = keras.layers.Dense(units=zsize * (2 ** 5), name='latent')
def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
"""See base class."""
@ -64,6 +66,8 @@ class Encoder(keras.Model):
result = self.conv2_a(result)
result = self.conv3(result)
result = self.conv3_a(result)
result = self.flatten(result)
result = self.latent(result)
return result
@ -71,31 +75,47 @@ class Encoder(keras.Model):
class Decoder(keras.Model):
"""
Generates input data from latent space values.
Args:
channels: number of channels in the input image
zsize: size of the latent space
"""
def __init__(self, channels: int, zsize: int) -> None:
def __init__(self, channels: int, zsize: int, image_size: int) -> None:
"""
Initializes the Decoder class.
Args:
channels: number of channels in the input image
zsize: size of the latent space
image_size: size of height/width of input image
"""
super().__init__(name='decoder')
weight_init = keras.initializers.RandomNormal(mean=0, stddev=0.02)
self.deconv1 = keras.layers.Conv2DTranspose(filters=zsize * 2, kernel_size=3, strides=2, name='deconv1',
# calculate dimension of last conv layer in encoder
conv_image_size = image_size / (2 ** 3)
dimensions = zsize * conv_image_size
self.conv_shape = (-1, conv_image_size, conv_image_size, zsize)
self.transform = keras.layers.Dense(units=dimensions, name='input_transform')
self.deconv1 = keras.layers.Conv2DTranspose(filters=zsize, kernel_size=3, strides=1, name='deconv1',
padding='same', kernel_initializer=weight_init)
self.deconv1_a = keras.layers.ReLU()
self.deconv2 = keras.layers.Conv2DTranspose(filters=zsize * 2, kernel_size=3, strides=2, name='deconv2',
padding='same', kernel_initializer=weight_init)
self.deconv2_a = keras.layers.ReLU()
self.deconv3 = keras.layers.Conv2DTranspose(filters=channels, kernel_size=3, strides=2, name='deconv3',
self.deconv3 = keras.layers.Conv2DTranspose(filters=zsize * 4, kernel_size=3, strides=2, name='deconv3',
padding='same', kernel_initializer=weight_init)
self.deconv3_a = keras.layers.ReLU()
self.deconv4 = keras.layers.Conv2DTranspose(filters=channels, kernel_size=3, strides=2, name='deconv4',
padding='same', kernel_initializer=weight_init)
def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor:
"""See base class."""
result = self.deconv1(inputs)
result = self.transform(inputs)
result = tf.reshape(result, self.conv_shape)
result = self.deconv1(result)
result = self.deconv1_a(result)
result = self.deconv2(result)
result = self.deconv2_a(result)
result = self.deconv3(result)
result = self.deconv3_a(result)
result = self.deconv4(result)
result = k.sigmoid(result)
return result

4
src/twomartens/masterthesis/aae/run.py
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@ -39,6 +39,7 @@ tfe = tf.contrib.eager
def run_simple(dataset: tf.data.Dataset,
iteration: int,
weights_prefix: str,
image_size: int,
channels: int = 3,
zsize: int = 64,
batch_size: int = 16,
@ -52,6 +53,7 @@ def run_simple(dataset: tf.data.Dataset,
dataset: run dataset
iteration: identifier for the used training run
weights_prefix: prefix for trained weights directory
image_size: height/width of input image
channels: number of channels in input image (default: 3)
zsize: size of the intermediary z (default: 64)
batch_size: size of each batch (default: 16)
@ -62,7 +64,7 @@ def run_simple(dataset: tf.data.Dataset,
checkpointables = {
# get models
'encoder': model.Encoder(zsize),
'decoder': model.Decoder(channels, zsize),
'decoder': model.Decoder(channels, zsize, image_size),
}
global_step = tf.train.get_or_create_global_step()

4
src/twomartens/masterthesis/aae/train.py
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@ -47,6 +47,7 @@ LOG_FREQUENCY: int = 10
def train_simple(dataset: tf.data.Dataset,
iteration: int,
weights_prefix: str,
image_size: int,
channels: int = 3,
zsize: int = 64,
lr: float = 0.0001,
@ -67,6 +68,7 @@ def train_simple(dataset: tf.data.Dataset,
dataset: train dataset
iteration: identifier for the current training run
weights_prefix: prefix for weights directory
image_size: height/width of input image
channels: number of channels in input image (default: 3)
zsize: size of the intermediary z (default: 64)
lr: initial learning rate (default: 0.0001)
@ -82,7 +84,7 @@ def train_simple(dataset: tf.data.Dataset,
checkpointables.update({
# get models
'encoder': model.Encoder(zsize),
'decoder': model.Decoder(channels, zsize),
'decoder': model.Decoder(channels, zsize, image_size),
# define optimizers
'enc_dec_optimizer': tf.train.AdamOptimizer(learning_rate=checkpointables['learning_rate_var']),
# global step counter

16
src/twomartens/masterthesis/main.py
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@ -123,8 +123,9 @@ def _val(args: argparse.Namespace) -> None:
category = args.category
category_trained = args.category_trained
batch_size = 16
image_size = 256
coco_data = data.load_coco_val(coco_path, category, num_epochs=1,
batch_size=batch_size, resized_shape=(256, 256))
batch_size=batch_size, resized_shape=(image_size, image_size))
use_summary_writer = summary_ops_v2.create_file_writer(
f"{args.summary_path}/val/category-{category}/{args.iteration}"
)
@ -132,11 +133,13 @@ def _val(args: argparse.Namespace) -> None:
with use_summary_writer.as_default():
run.run_simple(coco_data, iteration=args.iteration_trained,
weights_prefix=f"{args.weights_path}/category-{category_trained}",
zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size)
zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size,
image_size=image_size)
else:
run.run_simple(coco_data, iteration=args.iteration_trained,
weights_prefix=f"{args.weights_path}/category-{category_trained}",
zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size)
zsize=16, verbose=args.verbose, channels=3, batch_size=batch_size,
image_size=image_size)
def _auto_encoder_train(args: argparse.Namespace) -> None:
@ -149,8 +152,9 @@ def _auto_encoder_train(args: argparse.Namespace) -> None:
coco_path = args.coco_path
category = args.category
batch_size = 16
image_size = 256
coco_data = data.load_coco_train(coco_path, category, num_epochs=args.num_epochs, batch_size=batch_size,
resized_shape=(256, 256))
resized_shape=(image_size, image_size))
train_summary_writer = summary_ops_v2.create_file_writer(
f"{args.summary_path}/train/category-{category}/{args.iteration}"
)
@ -158,12 +162,12 @@ def _auto_encoder_train(args: argparse.Namespace) -> None:
with train_summary_writer.as_default():
train.train_simple(coco_data, iteration=args.iteration,
weights_prefix=f"{args.weights_path}/category-{category}",
zsize=16, lr=0.0001, verbose=args.verbose,
zsize=16, lr=0.0001, verbose=args.verbose, image_size=image_size,
channels=3, train_epoch=args.num_epochs, batch_size=batch_size)
else:
train.train_simple(coco_data, iteration=args.iteration,
weights_prefix=f"{args.weights_path}/category-{category}",
zsize=16, lr=0.0001, verbose=args.verbose,
zsize=16, lr=0.0001, verbose=args.verbose, image_size=image_size,
channels=3, train_epoch=args.num_epochs, batch_size=batch_size)