118 lines
5.4 KiB
Python
118 lines
5.4 KiB
Python
from collections import namedtuple
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from keras.models import Model
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from . import networks
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from .metrics import *
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NetworkParameters = namedtuple("NetworkParameters", [
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"type", "flow_features", "window_size", "domain_length", "output",
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"embedding_size",
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"domain_filter", "domain_kernel", "domain_dense", "domain_dropout",
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"main_filter", "main_kernel", "main_dense", "main_dropout",
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])
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def create_model(model, output_type):
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if output_type == "both":
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return Model(inputs=[model.in_domains, model.in_flows], outputs=(model.out_client, model.out_server))
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elif output_type == "client":
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return Model(inputs=[model.in_domains, model.in_flows], outputs=(model.out_client,))
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else:
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raise Exception("unknown model output")
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def get_models_by_params(params: dict):
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K.clear_session()
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# decomposing param section
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# mainly embedding model
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embedding_type = params.get("embedding_type", "small")
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network_type = params.get("type")
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# network_depth = params.get("depth")
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embedding_size = params.get("embedding")
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filter_embedding = params.get("filter_embedding")
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kernel_embedding = params.get("kernel_embedding")
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hidden_embedding = params.get("dense_embedding")
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# dropout = params.get("dropout")
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# mainly prediction model
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flow_features = params.get("flow_features")
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window_size = params.get("window_size")
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domain_length = params.get("domain_length")
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filter_main = params.get("filter_main")
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kernel_main = params.get("kernel_main")
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dense_dim = params.get("dense_main")
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model_output = params.get("model_output", "both")
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if embedding_type == "small":
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domain_cnn = networks.get_domain_embedding_model(embedding_size, domain_length, filter_embedding,
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kernel_embedding, hidden_embedding, 0.5)
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elif embedding_type == "deep":
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domain_cnn = networks.get_domain_embedding_model2(embedding_size, domain_length, filter_embedding,
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kernel_embedding, hidden_embedding, 0.5)
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else:
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raise ValueError("embedding type not found")
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if network_type == "final":
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model = networks.get_final_model(0.25, flow_features, window_size, domain_length,
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filter_main, kernel_main, dense_dim, domain_cnn)
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model = create_model(model, model_output)
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elif network_type in ("inter", "staggered"):
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model = networks.get_inter_model(0.25, flow_features, window_size, domain_length,
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filter_main, kernel_main, dense_dim, domain_cnn)
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model = create_model(model, model_output)
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elif network_type == "long":
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model = networks.get_long_model(0.25, flow_features, window_size, domain_length,
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filter_main, kernel_main, dense_dim, domain_cnn)
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model = create_model(model, model_output)
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elif network_type == "soft":
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model = networks.get_long_model(0.25, flow_features, window_size, domain_length,
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filter_main, kernel_main, dense_dim, domain_cnn)
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model = create_model(model, model_output)
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conv_server = model.get_layer("conv_server").trainable_weights
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conv_client = model.get_layer("conv_client").trainable_weights
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l1 = [0.001 * K.sum(K.abs(x - y)) for (x, y) in zip(conv_server, conv_client)]
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model.add_loss(l1)
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dense_server = model.get_layer("dense_server").trainable_weights
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dense_client = model.get_layer("dense_client").trainable_weights
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l2 = [0.001 * K.sum(K.abs(x - y)) for (x, y) in zip(dense_server, dense_client)]
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model.add_loss(l2)
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elif network_type == "sluice":
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model = networks.get_sluice_model(0.25, flow_features, window_size, domain_length,
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filter_main, kernel_main, dense_dim, domain_cnn)
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model = create_model(model, model_output)
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conv_server = model.get_layer("conv_server").trainable_weights
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conv_client = model.get_layer("conv_client").trainable_weights
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l1 = [0.001 * K.sum(K.abs(x - y)) for (x, y) in zip(conv_server, conv_client)]
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model.add_loss(l1)
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dense_server = model.get_layer("dense_server").trainable_weights
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dense_client = model.get_layer("dense_client").trainable_weights
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l2 = [0.001 * K.sum(K.abs(x - y)) for (x, y) in zip(dense_server, dense_client)]
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model.add_loss(l2)
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else:
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raise ValueError("network type not found")
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return model
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def get_server_model_by_params(params: dict):
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# decomposing param section
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# mainly embedding model
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network_depth = params.get("depth")
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embedding_size = params.get("embedding")
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input_length = params.get("input_length")
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filter_embedding = params.get("filter_embedding")
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kernel_embedding = params.get("kernel_embedding")
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hidden_embedding = params.get("dense_embedding")
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# mainly prediction model
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flow_features = params.get("flow_features")
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domain_length = params.get("domain_length")
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dense_dim = params.get("dense_main")
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embedding_model = networks.get_domain_embedding_model(embedding_size, input_length, filter_embedding,
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kernel_embedding,
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hidden_embedding, 0.5)
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return networks.get_server_model(flow_features, domain_length, dense_dim, embedding_model)
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