def cyclegan_discriminator(h):
with C.layers.default_options(init=C.normal(0.02), pad=True, bias=False):
h = C.leaky_relu(Convolution2D((3, 3), 64, strides=2, bias=True)(h), alpha=0.2)
h = C.leaky_relu(InstanceNormalization((128, 1, 1)))(Convolution2D((3, 3), 128, strides=2)(h)), alpha=0.2)
h = C.leaky_relu(InstanceNormalization((256, 1, 1)))(Convolution2D((3, 3), 256, strides=2)(h)), alpha=0.2)
h = C.leaky_relu(InstanceNormalization((512, 1, 1)))(Convolution2D((3, 3), 512, strides=2)(h)), alpha=0.2)
h = Convolution2D((1, 1), 1, activation=None, bias=True)(h)
return h
def cgan_discriminator(x, y):
with C.layers.default_options(init=C.normal(scale=0.02), map_rank=1, use_cntk_engine=True):
hx = C.reshape(x, (1, 28, 28))
hy = C.ones_like(hx) * C.reshape(y, (label_dim, 1, 1))
h = C.splice(hx, hy, axis=0)
h = C.leaky_relu((Convolution2D((5, 5), 1, strides=(2, 2))(h)), alpha=0.2)
h = C.leaky_relu(BatchNormalization()(Convolution2D((5, 5), 64, strides=(2, 2))(h)), alpha=0.2)
h = C.leaky_relu(BatchNormalization()(Dense(1024)(h)), alpha=0.2)
h = Dense(1, activation=C.sigmoid)(h)
return h
def pix2pix_discriminator(y, x):
with C.layers.default_options(init=C.normal(0.02), pad=True, bias=False, map_rank=1, use_cntk_engine=True):
x = C.leaky_relu(Convolution2D((3, 3), 32, strides=2, bias=True)(x), alpha=0.2)
y = C.leaky_relu(Convolution2D((3, 3), 32, strides=2, bias=True)(y), alpha=0.2)
h = C.splice(x, y, axis=0)
h = C.leaky_relu(BatchNormalization()(Convolution2D((3, 3), 128, strides=2)(h)), alpha=0.2)
h = C.leaky_relu(BatchNormalization()(Convolution2D((3, 3), 256, strides=2)(h)), alpha=0.2)
h = C.leaky_relu(BatchNormalization()(Convolution2D((3, 3), 512, strides=2)(h)), alpha=0.2)
h = Convolution2D((1, 1), 1, activation=None, bias=True)(h)
return h
def pix2pix_generator(h):
with C.layers.default_options(init=C.normal(0.02), pad=True, bias=False, map_rank=1, use_cntk_engine=True):
h_enc1 = C.leaky_relu(Convolution2D((4, 4), 64, strides=2, bias=True)(h), alpha=0.2)
h_enc2 = C.leaky_relu(BatchNormalization()(Convolution2D((4, 4), 128, strides=2)(h_enc1)), alpha=0.2)
h_enc3 = C.leaky_relu(BatchNormalization()(Convolution2D((4, 4), 256, strides=2)(h_enc2)), alpha=0.2)
h_enc4 = C.leaky_relu(BatchNormalization()(Convolution2D((4, 4), 512, strides=2)(h_enc3)), alpha=0.2)
h_enc5 = C.leaky_relu(BatchNormalization()(Convolution2D((4, 4), 512, strides=2)(h_enc4)), alpha=0.2)
h_enc6 = C.leaky_relu(BatchNormalization()(Convolution2D((4, 4), 512, strides=2)(h_enc5)), alpha=0.2)
h_enc7 = C.leaky_relu(BatchNormalization()(Convolution2D((4, 4), 512, strides=1)(h_enc6)), alpha=0.2)
h_enc8 = C.leaky_relu(BatchNormalization()(Convolution2D((4, 4), 512, strides=1)(h_enc7)), alpha=0.2)
h_dec8 = Dropout(0.5)(BatchNormalization()(ConvolutionTranspose2D(
(4, 4), 512, strides=1, pad=True, output_shape=(img_height // 64, img_width // 64))(h_enc8)))
h_dec8 = C.splice(h_dec8, h_enc8, axis=0)
h_dec8 = C.relu(h_dec8)
h_dec7 = Dropout(0.5)(BatchNormalization()(ConvolutionTranspose2D(
(4, 4), 512, strides=1, pad=True, output_shape=(img_height // 64, img_width // 64))(h_dec8)))
h_dec7 = C.splice(h_dec7, h_enc7, axis=0)
h_dec7 = C.relu(h_dec7)
h_dec6 = Dropout(0.5)(BatchNormalization()(ConvolutionTranspose2D(
(4, 4), 512, strides=1, pad=True, output_shape=(img_height // 64, img_width // 64))(h_dec7)))
h_dec6 = C.splice(h_dec6, h_enc6, axis=0)
h_dec6 = C.relu(h_dec6)
h_dec5 = Dropout(0.5)(BatchNormalization()(ConvolutionTranspose2D(
(4, 4), 512, strides=2, pad=True, output_shape=(img_height // 32, img_width // 32))(h_dec6)))
h_dec5 = C.splice(h_dec5, h_enc5, axis=0)
h_dec5 = C.relu(h_dec5)
h_dec4 = Dropout(0.5)(BatchNormalization()(ConvolutionTranspose2D(
(4, 4), 512, strides=2, pad=True, output_shape=(img_height // 16, img_width // 16))(h_dec5)))
h_dec4 = C.splice(h_dec4, h_enc4, axis=0)
h_dec4 = C.relu(h_dec4)
h_dec3 = Dropout(0.5)(BatchNormalization()(ConvolutionTranspose2D(
(4, 4), 256, strides=2, pad=True, output_shape=(img_height // 8, img_width // 8))(h_dec4)))
h_dec3 = C.splice(h_dec3, h_enc3, axis=0)
h_dec3 = C.relu(h_dec3)
h_dec2 = Dropout(0.5)(BatchNormalization()(ConvolutionTranspose2D(
(4, 4), 128, strides=2, pad=True, output_shape=(img_height // 4, img_width // 4))(h_dec3)))
h_dec2 = C.splice(h_dec2, h_enc2, axis=0)
h_dec2 = C.relu(h_dec2)
h_dec1 = Dropout(0.5)(BatchNormalization()(ConvolutionTranspose2D(
(4, 4), 64, strides=2, pad=True, output_shape=(img_height // 2, img_width // 2))(h_dec2)))
h_dec1 = C.splice(h_dec1, h_enc1, axis=0)
h_dec1 = C.relu(h_dec1)
h = ConvolutionTranspose2D((4, 4), 3, activation=C.tanh, strides=2, pad=True, bias=True,
output_shape=(img_height, img_width))(h_dec1)
return h
def wgan_critic(h):
with C.layers.default_options(init=C.normal(0.02), pad=True, bias=False):
h = C.leaky_relu(Convolution2D((3, 3), 32, strides=2, bias=True)(h),
alpha=0.2)
h = C.leaky_relu(LayerNormalization()(Convolution2D((3, 3),
64,
strides=2)(h)),
alpha=0.2)
h = C.leaky_relu(LayerNormalization()(Convolution2D((3, 3),
128,
strides=2)(h)),
alpha=0.2)
h = C.leaky_relu(LayerNormalization()(Convolution2D((3, 3),
256,
strides=2)(h)),
alpha=0.2)
h = C.leaky_relu(LayerNormalization()(Convolution2D((3, 3),
512,
strides=2)(h)),
alpha=0.2)
h = C.leaky_relu(LayerNormalization()(Convolution2D((3, 3),
1024,
strides=2)(h)),
alpha=0.2)
h = Convolution2D((4, 4), 1, pad=False, strides=1, bias=True)(h)
return h
def dcgan_discriminator(h):
with C.layers.default_options(init=C.normal(0.02),
pad=True,
bias=False,
map_rank=1,
use_cntk_engine=True):
h = C.leaky_relu(Convolution2D((3, 3), 32, strides=2, bias=True)(h),
alpha=0.2)
h = C.leaky_relu(BatchNormalization()(Convolution2D((3, 3),
64,
strides=2)(h)),
alpha=0.2)
h = C.leaky_relu(BatchNormalization()(Convolution2D((3, 3),
128,
strides=2)(h)),
alpha=0.2)
h = C.leaky_relu(BatchNormalization()(Convolution2D((3, 3),
256,
strides=2)(h)),
alpha=0.2)
h = C.leaky_relu(BatchNormalization()(Convolution2D((3, 3),
512,
strides=2)(h)),
alpha=0.2)
h = C.leaky_relu(BatchNormalization()(Convolution2D((3, 3),
1024,
strides=2)(h)),
alpha=0.2)
h = Convolution2D((4, 4),
1,
activation=C.sigmoid,
pad=False,
bias=True,
strides=1)(h)
return h
def test_LeakyRelu(tmpdir):
data = np.asarray([[-1, -0.5, 0, 1, 2]], dtype=np.float32)
model = C.leaky_relu(data)
verify_no_input(model, tmpdir, 'LeakyRelu_0')
def test_LeakyRelu(tmpdir, dtype):
with C.default_options(dtype=dtype):
data = np.asarray([[-1, -0.5, 0, 1, 2]], dtype=dtype)
model = C.leaky_relu(data)
verify_no_input(model, tmpdir, 'LeakyRelu_0')
def test_LeakyRelu(tmpdir, dtype):
with C.default_options(dtype = dtype):
data = np.asarray([[-1, -0.5, 0, 1, 2]], dtype=dtype)
model = C.leaky_relu(data)
verify_no_input(model, tmpdir, 'LeakyRelu_0')
def test_LeakyRelu(tmpdir):
data = np.asarray([[-1, -0.5, 0, 1, 2]], dtype=np.float32)
model = C.leaky_relu(data)
verify_no_input(model, tmpdir, 'LeakyRelu_0')
