def build_Gx(self):
z_input = Input(shape=(self.z_dims,))
orig_channels = self.input_shape[2]
x = Reshape((1, 1, -1))(z_input)
x = BasicDeconvLayer(256, (4, 4), strides=(1, 1), bnorm=True, activation='leaky_relu', leaky_relu_slope=0.01)(x)
x = BasicDeconvLayer(128, (4, 4), strides=(2, 2), bnorm=True, activation='leaky_relu', leaky_relu_slope=0.01)(x)
x = BasicDeconvLayer(64, (4, 4), strides=(1, 1), bnorm=True, activation='leaky_relu', leaky_relu_slope=0.01)(x)
x = BasicDeconvLayer(32, (4, 4), strides=(2, 2), bnorm=True, activation='leaky_relu', leaky_relu_slope=0.01)(x)
x = BasicDeconvLayer(32, (5, 5), strides=(1, 1), bnorm=True, activation='leaky_relu', leaky_relu_slope=0.01)(x)
x = BasicConvLayer(32, (1, 1), strides=(1, 1), bnorm=True, activation='leaky_relu', leaky_relu_slope=0.01)(x)
x = BasicConvLayer(orig_channels, (1, 1), activation='sigmoid', bnorm=False)(x)
return Model(z_input, x, name="Gx")
def build_Gx(self):
z_input = Input(shape=(self.z_dims,))
orig_channels = self.input_shape[2]
x = Dense(512)(z_input)
x = LeakyReLU(0.1)(x)
x = Dense(512)(x)
x = LeakyReLU(0.1)(x)
x = Reshape((4, 4, 32))(x)
x = BasicDeconvLayer(64, (3, 3), strides=(1, 1), bnorm=False, activation='leaky_relu', leaky_relu_slope=0.1, padding='same')(x)
x = BasicDeconvLayer(64, (3, 3), strides=(1, 1), bnorm=True, activation='leaky_relu', leaky_relu_slope=0.1, padding='same')(x)
x = ResDeconvLayer(64, (4, 4), strides=(2, 2), bnorm=True, activation='leaky_relu', leaky_relu_slope=0.1, padding='same')(x)
x = ResDeconvLayer(64, (4, 4), strides=(2, 2), bnorm=True, activation='leaky_relu', leaky_relu_slope=0.1, padding='same')(x)
x = ResDeconvLayer(64, (4, 4), strides=(2, 2), bnorm=True, activation='leaky_relu', leaky_relu_slope=0.1, padding='same')(x)
x = BasicDeconvLayer(64, (3, 3), strides=(1, 1), bnorm=True, activation='leaky_relu', leaky_relu_slope=0.1, padding='same')(x)
x = BasicDeconvLayer(32, (5, 5), strides=(1, 1), bnorm=True, activation='leaky_relu', leaky_relu_slope=0.1, padding='same')(x)
x = BasicConvLayer(32, (1, 1), strides=(1, 1), bnorm=False, activation='leaky_relu', leaky_relu_slope=0.1)(x)
x = BasicConvLayer(orig_channels, (1, 1), activation='sigmoid', bnorm=False)(x)
return Model(z_input, x)
def build_Gx(self):
z_input = Input(shape=(self.z_dims, ))
orig_channels = self.input_shape[2]
x = Dense(64, activation='relu')(z_input)
x = Dense(128, activation='relu')(x)
x = Reshape((4, 4, 8))(x)
res_x = x = BasicDeconvLayer(64, (3, 3),
strides=(1, 1),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same')(x)
x = BasicDeconvLayer(64, (3, 3),
strides=(1, 1),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same',
residual=res_x)(x)
res_x = x = BasicDeconvLayer(64, (3, 3),
strides=(2, 2),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same')(x)
x = BasicDeconvLayer(64, (3, 3),
strides=(1, 1),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same',
residual=res_x)(x)
res_x = x = BasicDeconvLayer(64, (3, 3),
strides=(1, 1),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same')(x)
x = BasicDeconvLayer(64, (3, 3),
strides=(1, 1),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same',
residual=res_x)(x)
res_x = x = BasicDeconvLayer(64, (3, 3),
strides=(2, 2),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same')(x)
x = BasicDeconvLayer(64, (3, 3),
strides=(1, 1),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same',
residual=res_x)(x)
res_x = x = BasicDeconvLayer(64, (3, 3),
strides=(1, 1),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same')(x)
x = BasicDeconvLayer(64, (3, 3),
strides=(1, 1),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same',
residual=res_x)(x)
res_x = x = BasicDeconvLayer(64, (3, 3),
strides=(2, 2),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same')(x)
x = BasicDeconvLayer(64, (3, 3),
strides=(1, 1),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same',
residual=res_x)(x)
res_x = x = BasicDeconvLayer(32, (5, 5),
strides=(1, 1),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same')(x)
x = BasicDeconvLayer(32, (5, 5),
strides=(1, 1),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01,
padding='same',
residual=res_x)(x)
x = BasicConvLayer(32, (1, 1),
strides=(1, 1),
bnorm=True,
activation='leaky_relu',
leaky_relu_slope=0.01)(x)
x = BasicConvLayer(orig_channels, (1, 1),
activation='sigmoid',
bnorm=False)(x)
return Model(z_input, x)