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move model creation back into models package

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René Knaebel
2017-11-07 20:09:20 +01:00
parent b1f48c1895
commit e12bbda8c5
6 changed files with 67 additions and 119 deletions
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57
models/__init__.py
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@@ -1,14 +1,24 @@
import keras.backend as K
from keras.models import Model
from models import deep1
from models.renes_networks import selu
from . import flat_2, pauls_networks, renes_networks
def create_model(model, output_type):
if output_type == "both":
return Model(inputs=[model.in_domains, model.in_flows], outputs=(model.out_client, model.out_server))
elif output_type == "client":
return Model(inputs=[model.in_domains, model.in_flows], outputs=(model.out_client,))
else:
raise Exception("unknown model output")
def get_models_by_params(params: dict):
# decomposing param section
# mainly embedding model
# network_type = params.get("type")
network_type = params.get("type")
network_depth = params.get("depth")
embedding_size = params.get("embedding")
filter_embedding = params.get("filter_embedding")
@@ -33,23 +43,40 @@ def get_models_by_params(params: dict):
elif network_depth == "deep2":
networks = renes_networks
else:
raise Exception("network not found")
embedding_model = networks.get_embedding(embedding_size, domain_length, filter_embedding, kernel_embedding,
raise ValueError("network not found")
domain_cnn = networks.get_embedding(embedding_size, domain_length, filter_embedding, kernel_embedding,
hidden_embedding, 0.5)
final = networks.get_model(0.25, flow_features, hidden_embedding, window_size, domain_length,
filter_main, kernel_main, dense_dim, embedding_model, model_output)
if network_type == "final":
model = networks.get_model(0.25, flow_features, window_size, domain_length,
filter_main, kernel_main, dense_dim, domain_cnn)
model = create_model(model, model_output)
elif network_type in ("inter", "staggered"):
model = networks.get_new_model(0.25, flow_features, window_size, domain_length,
filter_main, kernel_main, dense_dim, domain_cnn)
model = create_model(model, model_output)
elif network_type == "long":
model = networks.get_new_model2(0.25, flow_features, window_size, domain_length,
filter_main, kernel_main, dense_dim, domain_cnn)
model = create_model(model, model_output)
elif network_type == "soft":
model = networks.get_new_soft(0.25, flow_features, window_size, domain_length,
filter_main, kernel_main, dense_dim, domain_cnn)
model = create_model(model, model_output)
conv_server = model.get_layer("conv_server").trainable_weights
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)]
model.add_loss(l2)
else:
raise ValueError("network type not found")
inter = networks.get_new_model(0.25, flow_features, hidden_embedding, window_size, domain_length,
filter_main, kernel_main, dense_dim, embedding_model, model_output)
long = networks.get_new_model2(0.25, flow_features, hidden_embedding, window_size, domain_length,
filter_main, kernel_main, dense_dim, embedding_model, model_output)
soft = networks.get_new_soft(0.25, flow_features, hidden_embedding, window_size, domain_length,
filter_main, kernel_main, dense_dim, embedding_model, model_output)
return embedding_model, final, inter, long, soft
return model
def get_server_model_by_params(params: dict):