Commit 1ebd5f15 authored by lucas_miranda's avatar lucas_miranda
Browse files

Increased default dimensionality of latent space

parent 69196db8
...@@ -133,9 +133,9 @@ class GMVAE: ...@@ -133,9 +133,9 @@ class GMVAE:
defaults = { defaults = {
"bidirectional_merge": "concat", "bidirectional_merge": "concat",
"clipvalue": 1.0, "clipvalue": 0.75,
"dense_activation": "relu", "dense_activation": "relu",
"dense_layers_per_branch": 3, "dense_layers_per_branch": 1,
"dropout_rate": 0.1, "dropout_rate": 0.1,
"learning_rate": 1e-4, "learning_rate": 1e-4,
"units_conv": 64, "units_conv": 64,
......
...@@ -19,11 +19,11 @@ warmup_epochs = [15] ...@@ -19,11 +19,11 @@ warmup_epochs = [15]
warmup_mode = ["sigmoid"] warmup_mode = ["sigmoid"]
losses = ["ELBO"] # , "MMD", "ELBO+MMD"] losses = ["ELBO"] # , "MMD", "ELBO+MMD"]
overlap_loss = [0.1, 0.2, 0.5, 0.75, 1.] overlap_loss = [0.1, 0.2, 0.5, 0.75, 1.]
encodings = [32] # [2, 4, 6, 8, 10, 12, 14, 16] encodings = [16] # [2, 4, 6, 8, 10, 12, 14, 16]
cluster_numbers = [15] # [1, 5, 10, 15, 20, 25] cluster_numbers = [15] # [1, 5, 10, 15, 20, 25]
latent_reg = ["variance"] # ["none", "categorical", "variance", "categorical+variance"] latent_reg = ["variance"] # ["none", "categorical", "variance", "categorical+variance"]
entropy_knn = [10] entropy_knn = [10]
next_sequence_pred_weights = [0.15] next_sequence_pred_weights = [0.0]
phenotype_pred_weights = [0.0] phenotype_pred_weights = [0.0]
rule_based_pred_weights = [0.0] rule_based_pred_weights = [0.0]
window_lengths = [22] # range(11,56,11) window_lengths = [22] # range(11,56,11)
......
...@@ -553,11 +553,11 @@ ...@@ -553,11 +553,11 @@
loss=loss, loss=loss,
number_of_components=k, number_of_components=k,
compile_model=True, compile_model=True,
batch_size=batch_size, batch_size=batch_size,
encoding=encoding, encoding=encoding,
next_sequence_prediction=NextSeqPred, next_sequence_prediction=0.1,
phenotype_prediction=PhenoPred, phenotype_prediction=PhenoPred,
rule_based_prediction=RuleBasedPred, rule_based_prediction=RuleBasedPred,
).build( ).build(
data_prep.shape data_prep.shape
) )
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