def test_resnet():
n = 4
x = Input(shape=(1, 8, 8))
y = sequential([
conv2d_block(n),
resnet(n)
])(x)
model = Model(x, y)
assert model.get_output_shape_for((None, 1, 8, 8)) == (None, n, 8, 8)
def render_gan_discriminator_resnet(x,
n=32,
conv_repeat=1,
dense=[],
out_activation='sigmoid'):
def get_dense(nb):
return [
Dense(nb),
batch_norm(),
LeakyReLU(0.2),
]
return sequential([
Convolution2D(n, 3, 3, border_mode='same'),
resnet(n, activation=LeakyReLU(0.3)),
Convolution2D(n, 3, 3, subsample=(2, 2), border_mode='same'),
LeakyReLU(0.2),
batch_norm(),
resnet(2 * n, activation=LeakyReLU(0.3)),
resnet(2 * n, activation=LeakyReLU(0.3)),
resnet(2 * n, activation=LeakyReLU(0.3)),
resnet(2 * n, activation=LeakyReLU(0.3)),
resnet(2 * n, activation=LeakyReLU(0.3)),
Convolution2D(4 * n, 3, 3, subsample=(2, 2), border_mode='same'),
resnet(4 * n, activation=LeakyReLU(0.3)),
resnet(4 * n, activation=LeakyReLU(0.3)),
resnet(4 * n, activation=LeakyReLU(0.3)),
resnet(4 * n, activation=LeakyReLU(0.3)),
resnet(4 * n, activation=LeakyReLU(0.3)),
Convolution2D(4 * n, 3, 3, subsample=(2, 2), border_mode='same'),
resnet(4 * n, activation=LeakyReLU(0.3)),
resnet(4 * n, activation=LeakyReLU(0.3)),
resnet(4 * n, activation=LeakyReLU(0.3)),
resnet(4 * n, activation=LeakyReLU(0.3)),
resnet(4 * n, activation=LeakyReLU(0.3)),
Flatten(), [get_dense(nb) for nb in dense],
Dense(1, activation=out_activation)
],
ns='dis')(concat(x, axis=0, name='concat_fake_real'))
def render_gan_discriminator_resnet(x, n=32, conv_repeat=1, dense=[], out_activation='sigmoid'):
def get_dense(nb):
return [
Dense(nb),
batch_norm(),
LeakyReLU(0.2),
]
return sequential([
Convolution2D(n, 3, 3, border_mode='same'),
resnet(n, activation=LeakyReLU(0.3)),
Convolution2D(n, 3, 3, subsample=(2, 2), border_mode='same'),
LeakyReLU(0.2),
batch_norm(),
resnet(2*n, activation=LeakyReLU(0.3)),
resnet(2*n, activation=LeakyReLU(0.3)),
resnet(2*n, activation=LeakyReLU(0.3)),
resnet(2*n, activation=LeakyReLU(0.3)),
resnet(2*n, activation=LeakyReLU(0.3)),
Convolution2D(4*n, 3, 3, subsample=(2, 2), border_mode='same'),
resnet(4*n, activation=LeakyReLU(0.3)),
resnet(4*n, activation=LeakyReLU(0.3)),
resnet(4*n, activation=LeakyReLU(0.3)),
resnet(4*n, activation=LeakyReLU(0.3)),
resnet(4*n, activation=LeakyReLU(0.3)),
Convolution2D(4*n, 3, 3, subsample=(2, 2), border_mode='same'),
resnet(4*n, activation=LeakyReLU(0.3)),
resnet(4*n, activation=LeakyReLU(0.3)),
resnet(4*n, activation=LeakyReLU(0.3)),
resnet(4*n, activation=LeakyReLU(0.3)),
resnet(4*n, activation=LeakyReLU(0.3)),
Flatten(),
[get_dense(nb) for nb in dense],
Dense(1, activation=out_activation)
], ns='dis')(concat(x, axis=0, name='concat_fake_real'))
