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,
output_dense_layers=1,
output_dense_units=128)
c_nn.include_self_comparison = False
c_nn.weighted_classes = True
c_nn.class_weights_approximation = 'stochastic'
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)
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,
allow_less_clusters=False)
en = None
# dp = Simple2DPointDataProvider(min_cluster_count=1, max_cluster_count=10, allow_less_clusters=False)
en = SimpleFCEmbedding(output_size=3,
hidden_layers=[2],
final_activation='tanh')
c_nn = ClusterNNMergedInputs(dp, 3, en)
c_nn.minibatch_size = 2
c_nn.validate_every_nth_epoch = 1
c_nn.debug_mode = True
# c_nn.use_cluster_count_loss = False
# c_nn.use_similarities_loss = False
# c_nn.fixed_cluster_count_output = dp.get_max_cluster_count()
# c_nn.f_cluster_count = lambda: 10
# c_nn.minibatch_size = 200
# c_nn._get_keras_loss()
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
c_nn.class_weights_post_processing_f = lambda x: np.sqrt(x)
# 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], final_activation='tanh')
# en = None
c_nn = ClusterNNTry00_V02(dp, 50, en, lstm_layers=15, lstm_units=128, cluster_count_dense_layers=1, cluster_count_dense_units=128,
output_dense_layers=1, output_dense_units=256)
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.validate_every_nth_epoch = 10
# c_nn.f_cluster_count = lambda: 10
c_nn.minibatch_size = 200
# i = 0
# start = time()
from impl.data.image.cifar100_data_provider import Cifar100DataProvider
from impl.data.image.birds200_data_provider import Birds200DataProvider
from impl.data.audio.timit_data_provider import TIMITDataProvider
from impl.nn.base.embedding_nn.cnn_embedding import CnnEmbedding
from impl.nn.base.embedding_nn.cnn_bdlstm_embedding import CnnBDLSTMEmbedding
from impl.nn.base.embedding_nn.simple_fc_embedding import SimpleFCEmbedding
from impl.nn.base.embedding_nn.bdlstm_embedding import BDLSTMEmbedding
is_linux = platform == "linux" or platform == "linux2"
top_dir = "/cluster/home/meierbe8/data/MT/" if is_linux else "G:/tmp/"
ds_dir = "./" if is_linux else "../"
dp = Simple2DPointDataProvider(min_cluster_count=1,
max_cluster_count=3,
allow_less_clusters=False)
en = SimpleFCEmbedding(hidden_layers=[2])
autosave_dir = top_dir + 'test/autosave_ClusterNN_playground'
# Define the possible input counts
input_counts = [3, 4, 5]
# Create an array with all possible models
models = []
master_network = None
for input_count in reversed(sorted(input_counts)):
print("Initialize model with {} inputs...".format(input_count))
# cnn = ClusterNNMergedInputs(dp, input_count, en, weighted_classes=True)
cnn = MinimalClusterNN(dp, input_count, en, weighted_classes=True)