if __name__ == '__main__':
# Difference to test_cluster_nn_try00.py: No embedding is used and the network always returns that 10 clusters were
# found, but some of them may be empty
from sys import platform
from impl.data.simple_2d_point_data_provider import Simple2DPointDataProvider
from impl.nn.base.embedding_nn.simple_fc_embedding import SimpleFCEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
fixedc = 3
dp = Simple2DPointDataProvider(min_cluster_count=fixedc,
max_cluster_count=fixedc,
allow_less_clusters=False,
use_extended_data_gen=True)
# dp = Simple2DPointDataProvider(min_cluster_count=1, max_cluster_count=10, allow_less_clusters=False)
en = SimpleFCEmbedding(output_size=8,
hidden_layers=[16, 32, 64, 64],
hidden_activation=LeakyReLU(),
final_activation='tanh')
# en = None
c_nn = ClusterNNTry00_V11(dp,
102,
en,
lstm_layers=5,
lstm_units=96,
cluster_count_dense_layers=1,
cluster_count_dense_units=128,
from impl.nn.base.embedding_nn.cnn_embedding import CnnEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT_gpulab/" if is_linux else "G:/tmp/"
ds_dir = "./" if is_linux else "../"
p = Augmentor.Pipeline()
p.random_distortion(probability=1, grid_width=4, grid_height=4, magnitude=8)
p.flip_left_right(probability=0.5)
# p.flip_top_bottom(probability=0.5)
# p.rotate90(probability=0.5)
# p.rotate270(probability=0.5)
dp = Simple2DPointDataProvider(
min_cluster_count=1,
max_cluster_count=5,
use_extended_data_gen=True,
sigma=0.05
)
en = None
def get_cnn():
c_nn = ClusterNNTry00_V146(dp, 72, en, lstm_layers=14, internal_embedding_size=96*3, cluster_count_dense_layers=1, cluster_count_dense_units=256,
output_dense_layers=0, output_dense_units=256, cluster_count_lstm_layers=1, cluster_count_lstm_units=128,
kl_embedding_size=128, kl_divergence_factor=0., simplified_center_loss_factor=0.)
c_nn.include_self_comparison = False
c_nn.weighted_classes = True
c_nn.class_weights_approximation = 'stochastic'
c_nn.minibatch_size = 200
c_nn.class_weights_post_processing_f = lambda x: np.sqrt(x)
c_nn.set_loss_weight('similarities_output', 5.0)
c_nn.optimizer = Adadelta(lr=5.0)
max_cluster_count=10)
dp.target_min_cluster_count = 10
dp.target_max_cluster_count = 10
en = CnnEmbedding(output_size=100,
cnn_layers_per_block=1,
block_feature_counts=[32, 64],
fc_layer_feature_counts=[],
hidden_activation='relu',
final_activation='relu',
batch_norm_for_init_layer=True,
cnn_filter_size=5)
fixedc = 2
dp = Simple2DPointDataProvider(
min_cluster_count=fixedc,
max_cluster_count=fixedc,
)
en = None
dp = DummyDataProvider([[
[0.1, 0.9],
[0.2, 0.8],
], [
[0.3, 0.7],
[0.2, 0.7],
[0, 1],
], [[0, 1], [2, 3]]])
dp = Simple2DPointDataProvider(
min_cluster_count=1,
max_cluster_count=3,
if __name__ == '__main__':
# Difference to test_cluster_nn_try00.py: No embedding is used and the network always returns that 10 clusters were
# found, but some of them may be empty
from sys import platform
from impl.data.simple_2d_point_data_provider import Simple2DPointDataProvider
from impl.nn.base.embedding_nn.simple_fc_embedding import SimpleFCEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
fixedc = 7
dp = Simple2DPointDataProvider(min_cluster_count=fixedc,
max_cluster_count=fixedc,
allow_less_clusters=False)
# dp = Simple2DPointDataProvider(min_cluster_count=1, max_cluster_count=10, allow_less_clusters=False)
en = SimpleFCEmbedding(output_size=2,
hidden_layers=[16, 32, 64, 64],
hidden_activation=LeakyReLU(),
final_activation='tanh')
# en = None
c_nn = ClusterNNTry00_V08(dp,
50,
en,
lstm_layers=3,
pre_lstm_layers=3,
lstm_units=64,
cluster_count_dense_layers=1,
import matplotlib
matplotlib.use('Agg')
from impl.nn.try01.cluster_nn_try01 import ClusterNNTry01
if __name__ == '__main__':
from sys import platform
from impl.data.simple_2d_point_data_provider import Simple2DPointDataProvider
from impl.nn.base.embedding_nn.simple_fc_embedding import SimpleFCEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
dp = Simple2DPointDataProvider()
en = SimpleFCEmbedding()
#en = None # DO NOT USE ANY embedding (just use the twodimensional-points)
c_nn = ClusterNNTry01(dp, 50, en, iterations=2)
c_nn.minibatch_size = 96
c_nn.build_networks()
# Enable autosave and try to load the latest configuration
autosave_dir = top_dir + 'test/autosave_ClusterNNTry01'
c_nn.register_autosave(autosave_dir)
c_nn.try_load_from_autosave(autosave_dir)
# Train a loooong time
c_nn.train(1000000)
from impl.nn.try03_kmeans.cluster_nn_try03_kmeans_v04 import ClusterNNTry03KMeansV04
if __name__ == '__main__':
from sys import platform
from impl.data.simple_2d_point_data_provider import Simple2DPointDataProvider
from impl.nn.base.embedding_nn.simple_fc_embedding import SimpleFCEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
testing = False
if testing:
dp = Simple2DPointDataProvider(min_cluster_count=3,
max_cluster_count=3)
en = SimpleFCEmbedding(hidden_layers=0, output_size=2)
en = None
#en = None # DO NOT USE ANY embedding (just use the twodimensional-points)
c_nn = ClusterNNTry03KMeansV04(dp,
3,
en,
lstm_layers=0,
lstm_units=8,
kmeans_itrs=2,
cluster_count_dense_layers=0,
cluster_count_dense_units=1,
kmeans_input_dimension=2)
c_nn.weighted_classes = True
c_nn.minibatch_size = 2
if __name__ == '__main__':
# Difference to test_cluster_nn_try00.py: No embedding is used and the network uses weighted classes
from sys import platform
from impl.data.simple_2d_point_data_provider import Simple2DPointDataProvider
from impl.nn.base.embedding_nn.simple_fc_embedding import SimpleFCEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
#fixedc = 5
#dp = Simple2DPointDataProvider(min_cluster_count=fixedc, max_cluster_count=fixedc, allow_less_clusters=False)
dp = Simple2DPointDataProvider(min_cluster_count=1, max_cluster_count=10)
#en = SimpleFCEmbedding()
en = None
c_nn = ClusterNNTry00(dp, 50, en, weighted_classes=True)
# c_nn.f_cluster_count = lambda: 10
c_nn.minibatch_size = 200
c_nn.class_weights_approximation = 'stochastic'
# i = 0
# start = time()
# while True:
# try:
# print(i)
