def plot_cluster_head():
parser = argparse.ArgumentParser()
parser.add_argument("--model", "-m", type=str, default="model.hdf5")
args = parser.parse_args()
model = Model()
assert model.load(args.model)
all_y = np.identity(10, dtype=np.float32)
head = model.cluster_head(all_y).data
labels = [i for i in range(10)]
plot.scatter_labeled_z(head, labels, "cluster_head.png")
def plot_mapped_cluster_head():
parser = argparse.ArgumentParser()
parser.add_argument("--model", "-m", type=str, default="model.hdf5")
args = parser.parse_args()
model = Model()
assert model.load(args.model)
identity = np.identity(model.ndim_y, dtype=np.float32)
mapped_head = model.linear_transformation(identity)
labels = [i for i in range(10)]
plot.scatter_labeled_z(mapped_head.data, labels, "cluster_head.png")
def plot_scatter():
parser = argparse.ArgumentParser()
parser.add_argument("--model", "-m", type=str, default="model.hdf5")
args = parser.parse_args()
dataset_train, dataset_test = chainer.datasets.get_mnist()
images_train, labels_train = dataset_train._datasets
images_test, labels_test = dataset_test._datasets
model = Model()
assert model.load(args.model)
# normalize
images_train = (images_train - 0.5) * 2
images_test = (images_test - 0.5) * 2
with chainer.no_backprop_mode() and chainer.using_config("train", False):
z = model.encode_x_z(images_test).data
plot.scatter_labeled_z(z, labels_test, "scatter_gen.png")
def plot_scatter():
parser = argparse.ArgumentParser()
parser.add_argument("--model", "-m", type=str, default="model.hdf5")
args = parser.parse_args()
dataset_train, dataset_test = chainer.datasets.get_mnist()
images_train, labels_train = dataset_train._datasets
images_test, labels_test = dataset_test._datasets
model = Model()
assert model.load(args.model)
# normalize
images_train = (images_train - 0.5) * 2
images_test = (images_test - 0.5) * 2
with chainer.no_backprop_mode() and chainer.using_config("train", False):
z = model.encode_x_yz(images_test)[1].data
plot.scatter_labeled_z(z, labels_test, "scatter_gen.png")
def plot_representation():
parser = argparse.ArgumentParser()
parser.add_argument("--model", "-m", type=str, default="model.hdf5")
args = parser.parse_args()
dataset_train, dataset_test = chainer.datasets.get_mnist()
images_train, labels_train = dataset_train._datasets
images_test, labels_test = dataset_test._datasets
model = Model()
assert model.load(args.model)
# normalize
images_train = (images_train - 0.5) * 2
images_test = (images_test - 0.5) * 2
with chainer.no_backprop_mode() and chainer.using_config("train", False):
y_onehot, z = model.encode_x_yz(images_test, apply_softmax_y=True)
representation = model.encode_yz_representation(y_onehot, z).data
plot.scatter_labeled_z(representation, labels_test, "scatter_r.png")
def plot_representation():
parser = argparse.ArgumentParser()
parser.add_argument("--model", "-m", type=str, default="model.hdf5")
args = parser.parse_args()
dataset_train, dataset_test = chainer.datasets.get_mnist()
images_train, labels_train = dataset_train._datasets
images_test, labels_test = dataset_test._datasets
model = Model()
assert model.load(args.model)
# normalize
images_train = (images_train - 0.5) * 2
images_test = (images_test - 0.5) * 2
with chainer.no_backprop_mode() and chainer.using_config("train", False):
y_onehot, z = model.encode_x_yz(images_test, apply_softmax_y=True)
representation = model.encode_yz_representation(y_onehot, z).data
plot.scatter_labeled_z(representation, labels_test, "scatter_r.png")