def get_cnn(): c_nn = ClusterNNTry00_V51(dp, 20, en, lstm_layers=7, internal_embedding_size=96, cluster_count_dense_layers=1, cluster_count_dense_units=256, output_dense_layers=1, output_dense_units=256, cluster_count_lstm_layers=1, cluster_count_lstm_units=128, kl_embedding_size=128, kl_divergence_factor=0.1) c_nn.include_self_comparison = False c_nn.weighted_classes = True c_nn.class_weights_approximation = 'stochastic' c_nn.minibatch_size = 15 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) validation_factor = 10 c_nn.early_stopping_iterations = 15001 c_nn.validate_every_nth_epoch = 10 * validation_factor c_nn.validation_data_count = c_nn.minibatch_size * validation_factor # c_nn.prepend_base_name_to_layer_name = False return c_nn
cnn_layers_per_block=1, block_feature_counts=[32, 64, 128], fc_layer_feature_counts=[256], hidden_activation=LeakyReLU(), final_activation=LeakyReLU(), batch_norm_for_init_layer=False, batch_norm_after_activation=True, batch_norm_for_final_layer=True) c_nn = ClusterNNTry00_V51(dp, 40, en, lstm_layers=21, internal_embedding_size=384, cluster_count_dense_layers=1, cluster_count_dense_units=256, output_dense_layers=1, output_dense_units=256, cluster_count_lstm_layers=1, cluster_count_lstm_units=512, kl_embedding_size=128, kl_divergence_factor=0.1) c_nn.include_self_comparison = False c_nn.weighted_classes = True c_nn.class_weights_approximation = 'stochastic' c_nn.minibatch_size = 15 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) validation_factor = 10
minimum_snippets_per_cluster=2, # Use this mode for the real avaluation snippet_merge_mode=[8, 2] if used_input_count == required_input_count else None # minimum_snippets_per_cluster=[(200, 200), (100, 100)], # window_width=[(100, 200)] ) en = CnnEmbedding( output_size=256, cnn_layers_per_block=1, block_feature_counts=[32, 64, 128], fc_layer_feature_counts=[256], hidden_activation=LeakyReLU(), final_activation=LeakyReLU(), batch_norm_for_init_layer=False, batch_norm_after_activation=True, batch_norm_for_final_layer=True ) c_nn = ClusterNNTry00_V51(dp, used_input_count, en, lstm_layers=7, internal_embedding_size=96, cluster_count_dense_layers=1, cluster_count_dense_units=256, output_dense_layers=1, output_dense_units=256, cluster_count_lstm_layers=1, cluster_count_lstm_units=128, kl_embedding_size=128, kl_divergence_factor=0.1) c_nn.include_self_comparison = False c_nn.weighted_classes = True c_nn.class_weights_approximation = 'stochastic' c_nn.minibatch_size = 35 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) validation_factor = 10 c_nn.early_stopping_iterations = 10001 c_nn.validate_every_nth_epoch = 10 * validation_factor c_nn.validation_data_count = c_nn.minibatch_size * validation_factor # c_nn.prepend_base_name_to_layer_name = False print_loss_plot_every_nth_itr = 100