def model_encoder(latent_dim,
input_shape,
units=512,
reg=lambda: l1l2(l1=1e-7, l2=1e-7),
dropout=0.5):
k = 5
x = Input(input_shape)
h = Convolution2D(units / 4, k, k, border_mode='same',
W_regularizer=reg())(x)
# h = SpatialDropout2D(dropout)(h)
h = MaxPooling2D(pool_size=(2, 2))(h)
h = LeakyReLU(0.2)(h)
h = Convolution2D(units / 2, k, k, border_mode='same',
W_regularizer=reg())(h)
# h = SpatialDropout2D(dropout)(h)
h = MaxPooling2D(pool_size=(2, 2))(h)
h = LeakyReLU(0.2)(h)
h = Convolution2D(units / 2, k, k, border_mode='same',
W_regularizer=reg())(h)
# h = SpatialDropout2D(dropout)(h)
h = MaxPooling2D(pool_size=(2, 2))(h)
h = LeakyReLU(0.2)(h)
h = Convolution2D(units, k, k, border_mode='same', W_regularizer=reg())(h)
# h = SpatialDropout2D(dropout)(h)
h = LeakyReLU(0.2)(h)
h = Flatten()(h)
mu = Dense(latent_dim, name="encoder_mu", W_regularizer=reg())(h)
log_sigma_sq = Dense(latent_dim,
name="encoder_log_sigma_sq",
W_regularizer=reg())(h)
z = Lambda(
lambda
(_mu, _lss): _mu + K.random_normal(K.shape(_mu)) * K.exp(_lss / 2),
output_shape=lambda (_mu, _lss): _mu)([mu, log_sigma_sq])
return Model(x, z, name="encoder")
def model_discriminator(input_shape=(1, 92, 92), dropout_rate=0.5):
d_input = dim_ordering_input(input_shape, name="input_x")
nch = 128
# nch = 128
H = Convolution2D(int(nch / 2),
5,
5,
subsample=(2, 2),
border_mode='same',
activation='relu')(d_input)
H = LeakyReLU(0.2)(H)
H = Dropout(dropout_rate)(H)
H = Convolution2D(nch,
5,
5,
subsample=(2, 2),
border_mode='same',
activation='relu')(H)
H = LeakyReLU(0.2)(H)
H = Dropout(dropout_rate)(H)
H = Flatten()(H)
H = Dense(int(nch / 2))(H)
H = LeakyReLU(0.2)(H)
H = Dropout(dropout_rate)(H)
d_V = Dense(1, activation='sigmoid')(H)
return Model(d_input, d_V)
def model_generator(latent_dim, input_dim, hidden_dim=256):
return Sequential([
Dense(hidden_dim, name="generator_h1", input_dim=latent_dim),
LeakyReLU(0.2),
Dense(hidden_dim, name="generator_h2"),
LeakyReLU(0.2),
Dense(hidden_dim, name="generator_h3"),
LeakyReLU(0.2),
Dense(input_dim, name="generator_x_flat")],
name="generator")
def model_discriminator(input_dim, hidden_dim=256):
return Sequential([
Dense(hidden_dim, name="discriminator_h1", input_dim=input_dim),
LeakyReLU(0.2),
Dense(hidden_dim, name="discriminator_h2"),
LeakyReLU(0.2),
Dense(hidden_dim, name="discriminator_h3"),
LeakyReLU(0.2),
Dense(1, name="discriminator_y"),
Activation('sigmoid')],
name="discriminator")
def model_discriminator(input_shape, hidden_dim=1024, reg=lambda: l1l2(1e-5, 1e-5), output_activation="sigmoid"):
return Sequential([
Flatten(name="discriminator_flatten", input_shape=input_shape),
Dense(hidden_dim, name="discriminator_h1", W_regularizer=reg()),
LeakyReLU(0.2),
Dense(hidden_dim / 2, name="discriminator_h2", W_regularizer=reg()),
LeakyReLU(0.2),
Dense(hidden_dim / 4, name="discriminator_h3", W_regularizer=reg()),
LeakyReLU(0.2),
Dense(1, name="discriminator_y", W_regularizer=reg()),
Activation(output_activation)],
name="discriminator")
def model_generator(latent_dim, input_shape, hidden_dim=1024, reg=lambda: l1l2(1e-5, 1e-5)):
return Sequential([
Dense(hidden_dim / 4, name="generator_h1", input_dim=latent_dim, W_regularizer=reg()),
LeakyReLU(0.2),
Dense(hidden_dim / 2, name="generator_h2", W_regularizer=reg()),
LeakyReLU(0.2),
Dense(hidden_dim, name="generator_h3", W_regularizer=reg()),
LeakyReLU(0.2),
Dense(np.prod(input_shape), name="generator_x_flat", W_regularizer=reg()),
Activation('sigmoid'),
Reshape(input_shape, name="generator_x")],
name="generator")
def model_discriminator(input_shape,
hidden_dim=1024,
reg=lambda: l1l2(1e-5, 1e-5)):
return Sequential([
Flatten(input_shape=input_shape),
Dense(hidden_dim, W_regularizer=reg()),
LeakyReLU(0.2),
Dense(int(hidden_dim / 2), W_regularizer=reg()),
LeakyReLU(0.2),
Dense(int(hidden_dim / 4), W_regularizer=reg()),
LeakyReLU(0.2),
Dense(1, W_regularizer=reg()),
Activation('sigmoid')
],
name="discriminator")
def model_generator(latent_dim,
units=512,
dropout=0.5,
reg=lambda: l1l2(l1=1e-7, l2=1e-7)):
model = Sequential(name="decoder")
h = 5
model.add(Dense(units * 4 * 4, input_dim=latent_dim, W_regularizer=reg()))
model.add(Reshape(dim_ordering_shape((units, 4, 4))))
# model.add(SpatialDropout2D(dropout))
model.add(LeakyReLU(0.2))
model.add(
Convolution2D(units / 2, h, h, border_mode='same',
W_regularizer=reg()))
# model.add(SpatialDropout2D(dropout))
model.add(LeakyReLU(0.2))
model.add(UpSampling2D(size=(2, 2)))
model.add(
Convolution2D(units / 2, h, h, border_mode='same',
W_regularizer=reg()))
# model.add(SpatialDropout2D(dropout))
model.add(LeakyReLU(0.2))
model.add(UpSampling2D(size=(2, 2)))
model.add(
Convolution2D(units / 4, h, h, border_mode='same',
W_regularizer=reg()))
# model.add(SpatialDropout2D(dropout))
model.add(LeakyReLU(0.2))
model.add(UpSampling2D(size=(2, 2)))
model.add(Convolution2D(3, h, h, border_mode='same', W_regularizer=reg()))
model.add(Activation('sigmoid'))
return model
def model_generator():
model = Sequential()
nch = 256
reg = lambda: l1l2(l1=1e-7, l2=1e-7)
h = 5
model.add(Dense(nch * 4 * 4, input_dim=100, W_regularizer=reg()))
model.add(BatchNormalization(mode=0))
model.add(Reshape(dim_ordering_shape((nch, 4, 4))))
model.add(
Convolution2D(nch / 2, h, h, border_mode='same', W_regularizer=reg()))
model.add(BatchNormalization(mode=0, axis=1))
model.add(LeakyReLU(0.2))
model.add(UpSampling2D(size=(2, 2)))
model.add(
Convolution2D(nch / 2, h, h, border_mode='same', W_regularizer=reg()))
model.add(BatchNormalization(mode=0, axis=1))
model.add(LeakyReLU(0.2))
model.add(UpSampling2D(size=(2, 2)))
model.add(
Convolution2D(nch / 4, h, h, border_mode='same', W_regularizer=reg()))
model.add(BatchNormalization(mode=0, axis=1))
model.add(LeakyReLU(0.2))
model.add(UpSampling2D(size=(2, 2)))
model.add(Convolution2D(3, h, h, border_mode='same', W_regularizer=reg()))
model.add(Activation('sigmoid'))
return model
def model_discriminator():
nch = 512
h = 5
reg = lambda: l1l2(l1=1e-7, l2=1e-7)
input_d = Input(shape=(64, 64, 3))
c1 = Convolution2D(int(nch / 4),
h,
h,
border_mode='same',
W_regularizer=reg(),
input_shape=(64, 64, 3))
c2 = Convolution2D(int(nch / 4),
h,
h,
border_mode='same',
W_regularizer=reg())
c3 = Convolution2D(int(nch / 2),
h,
h,
border_mode='same',
W_regularizer=reg())
c4 = Convolution2D(int(nch), h, h, border_mode='same', W_regularizer=reg())
H = c1(input_d)
H = MaxPooling2D(pool_size=(2, 2))(H)
H = LeakyReLU(0.2)(H)
H = c2(H)
H = MaxPooling2D(pool_size=(2, 2))(H)
H = LeakyReLU(0.2)(H)
H = c3(H)
H = MaxPooling2D(pool_size=(2, 2))(H)
H = LeakyReLU(0.2)(H)
H = c4(H)
H = AveragePooling2D(pool_size=(4, 4), border_mode='valid')(H)
H = Flatten()(H)
H = Dense(256)(H)
H = LeakyReLU(0.2)(H)
H = Dense(64)(H)
H = LeakyReLU(0.2)(H)
fake = Dense(1)(H)
fake = Activation('sigmoid')(fake)
category = Dense(nb_labels)(H)
category = Activation('softmax')(category)
return Model(inputs=[input_d], outputs=[fake, category])
def get_discriminator():
"""
Returns the discriminator model
"""
reg = lambda: l1l2(1e-5, 1e-5)
model = Sequential()
model.add(
Flatten(name="discriminator_flatten",
input_shape=(IMAGE_DIM, IMAGE_DIM)))
model.add(Dense(1024, W_regularizer=reg()))
model.add(LeakyReLU(0.2))
model.add(Dense(512, W_regularizer=reg()))
model.add(LeakyReLU(0.2))
model.add(Dense(256, W_regularizer=reg()))
model.add(Dense(1, W_regularizer=reg()))
model.add(Activation('sigmoid'))
return model
def get_generator():
"""
Returns the generator model
"""
reg = lambda: l1l2(1e-5, 1e-5)
model = Sequential()
model.add(Dense(256, input_dim=IMAGE_DIM, W_regularizer=reg()))
model.add(LeakyReLU(0.2))
model.add(Dense(512, W_regularizer=reg()))
model.add(LeakyReLU(0.2))
model.add(Dense(1024, W_regularizer=reg()))
model.add(LeakyReLU(0.2))
# I think this has to be done to make the discriminator work
model.add(Dense(np.prod((IMAGE_DIM, IMAGE_DIM)), W_regularizer=reg()))
model.add(Activation('sigmoid'))
model.add(Reshape((IMAGE_DIM, IMAGE_DIM)))
return model
def model_discriminator(latent_dim,
output_dim=1,
units=256,
reg=lambda: l1l2(1e-7, 1e-7)):
z = Input((latent_dim, ))
h = z
mode = 1
h = Dense(units, name="discriminator_h1", W_regularizer=reg())(h)
# h = BatchNormalization(mode=mode)(h)
h = LeakyReLU(0.2)(h)
h = Dense(units / 2, name="discriminator_h2", W_regularizer=reg())(h)
# h = BatchNormalization(mode=mode)(h)
h = LeakyReLU(0.2)(h)
h = Dense(units / 2, name="discriminator_h3", W_regularizer=reg())(h)
# h = BatchNormalization(mode=mode)(h)
h = LeakyReLU(0.2)(h)
y = Dense(output_dim,
name="discriminator_y",
activation="sigmoid",
W_regularizer=reg())(h)
return Model(z, y)
def model_generator():
nch = 256
reg = lambda: l1l2(l1=1e-7, l2=1e-7)
h = 5
input_z = Input(shape=(latent_dim, ))
input_label = Input(shape=(1, ))
input_label_embedding = Flatten()(
Embedding(nb_labels,
latent_dim,
embeddings_initializer='glorot_normal')(input_label))
H = layers.multiply([input_z, input_label_embedding])
H = Dense(nch * 4 * 4, W_regularizer=reg())(H)
H = BatchNormalization(mode=0)(H)
H = Reshape(dim_ordering_shape((nch, 4, 4)))(H)
H = Convolution2D(int(nch / 2),
h,
h,
border_mode='same',
W_regularizer=reg())(H)
H = BatchNormalization(mode=0, axis=1)(H)
H = LeakyReLU(0.2)(H)
H = (UpSampling2D(size=(2, 2)))(H)
H = (Convolution2D(int(nch / 2),
h,
h,
border_mode='same',
W_regularizer=reg()))(H)
H = (BatchNormalization(mode=0, axis=1))(H)
H = (LeakyReLU(0.2))(H)
H = (UpSampling2D(size=(2, 2)))(H)
H = (Convolution2D(int(nch / 2),
h,
h,
border_mode='same',
W_regularizer=reg()))(H)
H = (BatchNormalization(mode=0, axis=1))(H)
H = (LeakyReLU(0.2))(H)
H = (UpSampling2D(size=(2, 2)))(H)
H = (Convolution2D(int(nch / 4),
h,
h,
border_mode='same',
W_regularizer=reg()))(H)
H = (BatchNormalization(mode=0, axis=1))(H)
H = (LeakyReLU(0.2))(H)
H = (UpSampling2D(size=(2, 2)))(H)
H = (Convolution2D(3, h, h, border_mode='same', W_regularizer=reg()))(H)
H = (Activation('sigmoid'))(H)
return Model(inputs=[input_z, input_label], outputs=H)
def model_generator():
nch = 256
g_input = Input(shape=[100])
H = Dense(nch * 14 * 14)(g_input)
H = BatchNormalization(mode=2)(H)
H = Activation('relu')(H)
H = dim_ordering_reshape(nch, 14)(H)
H = UpSampling2D(size=(2, 2))(H)
H = Convolution2D(int(nch / 2), 3, 3, border_mode='same')(H)
H = BatchNormalization(mode=2, axis=1)(H)
H = Activation('relu')(H)
H = Convolution2D(int(nch / 4), 3, 3, border_mode='same')(H)
H = BatchNormalization(mode=2, axis=1)(H)
H = Activation('relu')(H)
H = Convolution2D(1, 1, 1, border_mode='same')(H)
g_V = Activation('sigmoid')(H)
return Model(g_input, g_V)