refactor models package: create separate modules for pauls and renes networks
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14
main.py
14
main.py
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@ -87,10 +87,6 @@ def main():
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kernel_size = 3
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drop_out = 0.5
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filters = 128
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hidden_dims = 100
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vocabSize = 40
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threshold = 3
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minFlowsPerUser = 10
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char_dict = dataset.get_character_dict()
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user_flow_df = dataset.get_user_flow_data()
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@ -110,13 +106,13 @@ def main():
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client_labels = client_labels[idx]
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server_labels = server_tr[idx]
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shared_cnn = models.get_embedding_network_rene(len(char_dict) + 1, args.embedding, args.domain_length,
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args.hidden_char_dims, args.domain_embedding, 0.5)
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shared_cnn = models.renes_networks.get_embedding(len(char_dict) + 1, args.embedding, args.domain_length,
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args.hidden_char_dims, kernel_size, args.domain_embedding, 0.5)
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shared_cnn.summary()
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model = models.get_top_cnn_rene(cnnDropout, flowFeatures, args.domain_embedding,
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args.window, args.domain_length, filters, kernel_size,
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cnnHiddenDims, shared_cnn)
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model = models.renes_networks.get_model(cnnDropout, flowFeatures, args.domain_embedding,
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args.window, args.domain_length, filters, kernel_size,
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cnnHiddenDims, shared_cnn)
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model.summary()
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model.compile(optimizer='adam',
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81
models.py
81
models.py
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@ -1,81 +0,0 @@
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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, Activation, TimeDistributed, MaxPool1D
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# designed by paul
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def get_embedding_network_paul(vocab_size, embedding_size, input_length, filters, kernel_size,
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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 = Dense(hidden_dims)(y)
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y = Dropout(drop_out)(y)
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y = Activation('relu')(y)
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return Model(x, y)
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def get_embedding_network_rene(vocab_size, embedding_size, input_length,
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hidden_char_dims, 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, mask_zero=True)(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 = Dense(hidden_dims)(y)
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y = Dropout(drop_out)(y)
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y = Activation('relu')(y)
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return Model(x, y)
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def get_full_model(vocabSize, embeddingSize, maxLen, domainFeatures, flowFeatures,
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filters, h1, h2, dropout, dense):
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pass
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# designed by paul
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def get_top_cnn(cnnDropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size,
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dense_dim,
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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(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(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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def get_top_cnn_rene(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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# 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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@ -0,0 +1,2 @@
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from . import pauls_networks
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from . import renes_networks
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@ -0,0 +1,37 @@
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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, Activation, TimeDistributed
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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 = Dense(hidden_dims)(y)
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y = Dropout(drop_out)(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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# TODO: add more layers?
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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(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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@ -0,0 +1,40 @@
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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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