41 lines
1.9 KiB
Python
41 lines
1.9 KiB
Python
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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, TimeDistributed, MaxPool1D
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def get_embedding(vocab_size, embedding_size, input_length,
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hidden_char_dims, 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(hidden_char_dims, kernel_size=5, activation='relu')(y)
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y = MaxPool1D(pool_size=3, strides=1)(y)
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y = Conv1D(hidden_char_dims, kernel_size=3, activation='relu')(y)
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y = MaxPool1D(pool_size=3, strides=1)(y)
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y = Conv1D(hidden_char_dims, kernel_size=3, 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, 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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# TODO: add more layers?
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y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation='relu',
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input_shape=(window_size, domain_features + flow_features))(merged)
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y = MaxPool1D(pool_size=3, strides=1)(y)
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y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation='relu')(y)
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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(2, activation='softmax', name="client")(y)
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y2 = Dense(2, activation='softmax', name="server")(y)
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return Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
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