def fcc_wgan_discriminator(bnmode=0):
global usedbn, conv_init
d_input = Input(shape=dim_ordering_shape((1, 28, 28)))
L = Conv2D(filters=32, kernel_size=3, strides=2, padding='same', use_bias=False, kernel_initializer=conv_init)(d_input)
if(usedbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = LeakyReLU(alpha=0.2)(L)
L = Conv2D(filters=64, kernel_size=3, strides=2, padding='same', use_bias=False, kernel_initializer=conv_init)(L)
if (usedbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = LeakyReLU(alpha=0.2)(L)
L = Conv2D(filters=128, kernel_size=3, strides=2, use_bias=False, kernel_initializer=conv_init)(L)
if (usedbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = LeakyReLU(alpha=0.2)(L)
L = Flatten()(L)
L = Dense(512)(L)
L = LeakyReLU(alpha=0.2)(L)
L = Dense(64)(L)
L = LeakyReLU(alpha=0.2)(L)
L = Dense(16)(L)
L = LeakyReLU(alpha=0.2)(L)
d_output = Dense(1)(L)
return Model(d_input, d_output)
def fcc_wgan_generator(bnmode=0):
global usegbn, conv_init
d_input = Input(shape=(100,))
L = Reshape(target_shape=dim_ordering_shape((100, 1, 1)))(d_input)
L = Dense(64)(L)
L = Activation('relu')(L)
L = Dense(512)(L)
L = Activation('relu')(L)
L = Dense(128 * 3 * 3)(L)
if (usegbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = Reshape(dim_ordering_shape((128, 3, 3)))(L)
L = Conv2DTranspose(filters=64, kernel_size=3, strides=2, use_bias=False, kernel_initializer=conv_init)(L)
if (usegbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = Activation("relu")(L)
L = Conv2DTranspose(filters=32, kernel_size=3, strides=2, padding='same', use_bias=False, kernel_initializer=conv_init)(L)
if (usegbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = Activation("relu")(L)
L = Conv2DTranspose(filters=1, kernel_size=3, strides=2, padding='same', use_bias=False, kernel_initializer=conv_init)(L)
d_output = Activation('tanh')(L)
return Model(d_input, d_output)
def dcgan_discriminator(bnmode=0):
global usedbn, conv_init
d_input = Input(shape=dim_ordering_shape((3, 32, 32)))
L = ZeroPadding2D()(d_input)
L = Conv2D(filters=64, kernel_size=4, strides=2, use_bias=False, kernel_initializer=conv_init)(L)
if(usedbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = LeakyReLU(alpha=0.2)(L)
L = ZeroPadding2D()(L)
L = Conv2D(filters=128, kernel_size=4, strides=2, use_bias=False, kernel_initializer=conv_init)(L)
if (usedbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = LeakyReLU(alpha=0.2)(L)
L = ZeroPadding2D()(L)
L = Conv2D(filters=256, kernel_size=4, strides=2, use_bias=False, kernel_initializer=conv_init)(L)
if (usedbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = LeakyReLU(alpha=0.2)(L)
L = Conv2D(filters=1, kernel_size=4, strides=1, use_bias=False, kernel_initializer=conv_init)(L)
L = Flatten()(L)
d_output = Activation('sigmoid')(L)
return Model(d_input, d_output)
def dcgan_generator(bnmode=0):
global usegbn, conv_init
d_input = Input(shape=(100,))
L = Reshape(target_shape=dim_ordering_shape((100, 1, 1)))(d_input)
L = Conv2DTranspose(filters=256, kernel_size=4, strides=1, use_bias=False, kernel_initializer=conv_init)(L)
if (usegbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = Activation("relu")(L)
L = Conv2DTranspose(filters=128, kernel_size=4, strides=2, use_bias=False, kernel_initializer=conv_init)(L)
L = Cropping2D(cropping=1)(L)
if (usegbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = Activation("relu")(L)
L = Conv2DTranspose(filters=64, kernel_size=4, strides=2, use_bias=False, kernel_initializer=conv_init)(L)
L = Cropping2D(cropping=1)(L)
if (usegbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = Activation("relu")(L)
L = Conv2DTranspose(filters=3, kernel_size=4, strides=2, use_bias=False, kernel_initializer=conv_init)(L)
L = Cropping2D(cropping=1)(L)
d_output = Activation('tanh')(L)
return Model(d_input, d_output)
def fccgan_discriminator_pooling(bnmode=0):
global usedbn, conv_init
d_input = Input(shape=dim_ordering_shape((3, 32, 32)))
L = Conv2D(filters=64, kernel_size=4, strides=1, padding='same', use_bias=False, kernel_initializer=conv_init)(d_input)
if(usedbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = AveragePooling2D(pool_size=(2, 2))(L)
L = LeakyReLU(alpha=0.2)(L)
L = Conv2D(filters=128, kernel_size=4, strides=1, padding='same', use_bias=False, kernel_initializer=conv_init)(L)
if (usedbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = AveragePooling2D(pool_size=(2, 2))(L)
L = LeakyReLU(alpha=0.2)(L)
L = Conv2D(filters=256, kernel_size=4, strides=1, padding='same', use_bias=False, kernel_initializer=conv_init)(L)
if (usedbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = AveragePooling2D(pool_size=(2, 2))(L)
L = LeakyReLU(alpha=0.2)(L)
L = Flatten()(L)
L = Dense(512)(L)
L = LeakyReLU(alpha=0.2)(L)
L = Dense(64)(L)
L = LeakyReLU(alpha=0.2)(L)
L = Dense(16)(L)
L = LeakyReLU(alpha=0.2)(L)
L = Dense(1)(L)
d_output = Activation('sigmoid')(L)
return Model(d_input, d_output)
def fccgan_generator(bnmode=0):
global usegbn, conv_init
#LID improvement dense layers
d_input = Input(shape=(100,))
L = Dense(64)(d_input)
L = Activation('relu')(L)
L = Dense(512)(L)
L = Activation('relu')(L)
L = Dense(256 * 4 * 4)(L)
if (usegbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = Reshape(dim_ordering_shape((256, 4, 4)))(L)
L = Conv2DTranspose(filters=128, kernel_size=4, strides=2, use_bias=False, kernel_initializer=conv_init)(L)
L = Cropping2D(cropping=1)(L)
if (usegbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = Activation("relu")(L)
L = Conv2DTranspose(filters=64, kernel_size=4, strides=2, use_bias=False, kernel_initializer=conv_init)(L)
L = Cropping2D(cropping=1)(L)
if (usegbn): L = BatchNormalization(axis=get_bn_axis(), epsilon=1.01e-5)(L, training=bnmode)
L = Activation("relu")(L)
L = Conv2DTranspose(filters=3, kernel_size=4, strides=2, use_bias=False, kernel_initializer=conv_init)(L)
L = Cropping2D(cropping=1)(L)
d_output = Activation('tanh')(L)
return Model(d_input, d_output)
