33 lines
1.4 KiB
Python
33 lines
1.4 KiB
Python
from . import pauls_networks
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from . import renes_networks
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def get_models_by_params(params: dict):
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# decomposing param section
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# mainly embedding model
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network_type = params.get("type")
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vocab_size = params.get("vocab_size")
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embedding_size = params.get("embedding_size")
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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("hidden_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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domain_features = params.get("domain_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("kernels_main")
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dense_dim = params.get("dense_main")
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# create models
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networks = renes_networks if network_type == "rene" else pauls_networks
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embedding_model = networks.get_embedding(vocab_size, embedding_size, input_length,
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filter_embedding, kernel_embedding, hidden_embedding, drop_out=dropout)
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predict_model = networks.get_model(dropout, flow_features, domain_features, window_size, domain_length,
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filter_main, kernel_main, dense_dim, embedding_model)
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return embedding_model, predict_model
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