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add deeper domain cnn; refactor hyperband using load_data function

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René Knaebel
2017-11-10 12:52:18 +01:00
parent 3ce385eca6
commit 461d4cab8f
4 changed files with 43 additions and 20 deletions
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13
models/__init__.py
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@@ -26,6 +26,7 @@ def get_models_by_params(params: dict):
K.clear_session()
# decomposing param section
# mainly embedding model
embedding_type = params.get("embedding_type", "small")
network_type = params.get("type")
# network_depth = params.get("depth")
embedding_size = params.get("embedding")
@@ -42,8 +43,14 @@ def get_models_by_params(params: dict):
dense_dim = params.get("dense_main")
model_output = params.get("model_output", "both")
domain_cnn = networks.get_domain_embedding_model(embedding_size, domain_length, filter_embedding, kernel_embedding,
hidden_embedding, 0.5)
if embedding_type == "small":
domain_cnn = networks.get_domain_embedding_model(embedding_size, domain_length, filter_embedding,
kernel_embedding, hidden_embedding, 0.5)
elif embedding_type == "deep":
domain_cnn = networks.get_domain_embedding_model2(embedding_size, domain_length, filter_embedding,
kernel_embedding, hidden_embedding, 0.5)
else:
raise ValueError("embedding type not found")
if network_type == "final":
model = networks.get_final_model(0.25, flow_features, window_size, domain_length,
@@ -65,7 +72,7 @@ def get_models_by_params(params: dict):
conv_client = model.get_layer("conv_client").trainable_weights
l1 = [0.001 * K.sum(K.abs(x - y)) for (x, y) in zip(conv_server, conv_client)]
model.add_loss(l1)
dense_server = model.get_layer("dense_server").trainable_weights
dense_client = model.get_layer("dense_client").trainable_weights
l2 = [0.001 * K.sum(K.abs(x - y)) for (x, y) in zip(dense_server, dense_client)]