82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
import keras
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from keras.engine import Input, Model
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from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, Activation, TimeDistributed
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best_config = {
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"type": "paul",
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"batch_size": 64,
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"window_size": 10,
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"domain_length": 40,
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"flow_features": 3,
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#
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'dropout': 0.5,
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'domain_features': 32,
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'drop_out': 0.5,
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'embedding_size': 64,
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'filter_main': 512,
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'flow_features': 3,
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'dense_main': 32,
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'filter_embedding': 32,
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'hidden_embedding': 32,
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'kernel_embedding': 8,
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'kernels_main': 8,
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'input_length': 40
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}
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def get_embedding(vocab_size, embedding_size, input_length,
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filters, kernel_size, hidden_dims, drop_out=0.5):
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x = y = Input(shape=(input_length,))
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y = Embedding(input_dim=vocab_size, output_dim=embedding_size)(y)
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y = Conv1D(filters, kernel_size, activation='relu')(y)
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y = GlobalMaxPooling1D()(y)
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y = Dropout(drop_out)(y)
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y = Dense(hidden_dims)(y)
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y = Activation('relu')(y)
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return Model(x, y)
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def get_model(cnnDropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size,
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dense_dim, cnn):
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ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
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encoded = TimeDistributed(cnn)(ipt_domains)
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ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
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merged = keras.layers.concatenate([encoded, ipt_flows], -1)
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# CNN processing a small slides of flow windows
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y = Conv1D(cnn_dims,
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kernel_size,
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activation='relu',
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input_shape=(window_size, domain_features + flow_features))(merged)
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# remove temporal dimension by global max pooling
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y = GlobalMaxPooling1D()(y)
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y = Dropout(cnnDropout)(y)
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y = Dense(dense_dim, activation='relu')(y)
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y1 = Dense(1, activation='sigmoid', name="client")(y)
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y2 = Dense(1, activation='sigmoid', name="server")(y)
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return Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
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def get_new_model(dropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size,
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dense_dim, cnn):
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ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
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ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
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encoded = TimeDistributed(cnn)(ipt_domains)
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y2 = Dense(1, activation="sigmoid", name="server")(encoded)
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merged = keras.layers.concatenate([encoded, ipt_flows, y2], -1)
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y = Conv1D(cnn_dims,
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kernel_size,
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activation='relu',
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input_shape=(window_size, domain_features + flow_features))(merged)
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# remove temporal dimension by global max pooling
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y = GlobalMaxPooling1D()(y)
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y = Dropout(dropout)(y)
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y = Dense(dense_dim, activation='relu')(y)
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y1 = Dense(1, activation='sigmoid', name="client")(y)
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model = Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
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return model
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