from collections import namedtuple import keras from keras.engine import Input, Model as KerasModel from keras.layers import Conv1D, Dense, Dropout, Embedding, GlobalMaxPooling1D, TimeDistributed import dataset Model = namedtuple("Model", ["in_domains", "in_flows", "out_client", "out_server"]) best_config = { "type": "paul", "batch_size": 64, "window_size": 10, "domain_length": 40, "flow_features": 3, # 'dropout': 0.5, 'domain_features': 32, 'drop_out': 0.5, 'embedding_size': 64, 'filter_main': 512, 'flow_features': 3, 'dense_main': 32, 'filter_embedding': 32, 'hidden_embedding': 32, 'kernel_embedding': 8, 'kernels_main': 8, 'input_length': 40 } def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden_dims, drop_out=0.5) -> KerasModel: x = y = Input(shape=(input_length,)) y = Embedding(input_dim=dataset.get_vocab_size(), output_dim=embedding_size)(y) y = Conv1D(filter_size, kernel_size, activation='relu')(y) y = GlobalMaxPooling1D()(y) y = Dropout(drop_out)(y) y = Dense(hidden_dims, activation="relu")(y) return KerasModel(x, y) def get_model(cnnDropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size, dense_dim, cnn, model_output="both") -> Model: ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains") encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains) ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows") merged = keras.layers.concatenate([encoded, ipt_flows], -1) # CNN processing a small slides of flow windows y = Conv1D(cnn_dims, kernel_size, activation='relu', input_shape=(window_size, domain_features + flow_features))(merged) # remove temporal dimension by global max pooling y = GlobalMaxPooling1D()(y) y = Dropout(cnnDropout)(y) y = Dense(dense_dim, activation='relu')(y) out_client = Dense(1, activation='sigmoid', name="client")(y) out_server = Dense(1, activation='sigmoid', name="server")(y) return Model(ipt_domains, ipt_flows, out_client, out_server) def get_new_model(dropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size, dense_dim, cnn, model_output="both") -> Model: ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains") ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows") encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains) merged = keras.layers.concatenate([encoded, ipt_flows], -1) y = Dense(dense_dim, activation="relu", name="dense_server")(merged) out_server = Dense(1, activation="sigmoid", name="server")(y) merged = keras.layers.concatenate([merged, y], -1) # CNN processing a small slides of flow windows y = Conv1D(cnn_dims, kernel_size, activation='relu')(merged) # remove temporal dimension by global max pooling y = GlobalMaxPooling1D()(y) y = Dropout(dropout)(y) y = Dense(dense_dim, activation='relu', name="dense_client")(y) out_client = Dense(1, activation='sigmoid', name="client")(y) return Model(ipt_domains, ipt_flows, out_client, out_server) def get_server_model(flow_features, domain_length, dense_dim, cnn): ipt_domains = Input(shape=(domain_length,), name="ipt_domains") ipt_flows = Input(shape=(flow_features,), name="ipt_flows") encoded = cnn(ipt_domains) cnn.name = "domain_cnn" merged = keras.layers.concatenate([encoded, ipt_flows], -1) y = Dense(dense_dim, activation="relu", name="dense_server")(merged) out_server = Dense(1, activation="sigmoid", name="server")(y) return KerasModel(inputs=[ipt_domains, ipt_flows], outputs=out_server) def get_new_model2(dropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size, dense_dim, cnn, model_output="both") -> Model: ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains") ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows") encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains) merged = keras.layers.concatenate([encoded, ipt_flows], -1) y = Conv1D(cnn_dims, kernel_size, activation='relu')(merged) # remove temporal dimension by global max pooling y = GlobalMaxPooling1D()(y) y = Dropout(dropout)(y) y = Dense(dense_dim, activation="relu", name="dense_server")(y) out_server = Dense(1, activation="sigmoid", name="server")(y) # CNN processing a small slides of flow windows y = Conv1D(cnn_dims, kernel_size, activation='relu')(merged) # remove temporal dimension by global max pooling y = GlobalMaxPooling1D()(y) y = Dropout(dropout)(y) y = Dense(dense_dim, activation='relu', name="dense_client")(y) out_client = Dense(1, activation='sigmoid', name="client")(y) return Model(ipt_domains, ipt_flows, out_client, out_server) import keras.backend as K def get_new_soft(dropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size, dense_dim, cnn, model_output="both") -> Model: def dist_reg(distant_layer): def dist_reg_h(weights): print("REG FUNCTION") print(weights) print(distant_layer) return 0.01 * K.sum(K.abs(weights - distant_layer)) return dist_reg_h ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains") ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows") encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains) merged = keras.layers.concatenate([encoded, ipt_flows], -1) y = conv_server = Conv1D(cnn_dims, kernel_size, activation='relu', name="conv_server")(merged) # remove temporal dimension by global max pooling y = GlobalMaxPooling1D()(y) y = Dropout(dropout)(y) y = dense_server = Dense(dense_dim, activation="relu", name="dense_server")(y) out_server = Dense(1, activation="sigmoid", name="server")(y) # CNN processing a small slides of flow windows y = Conv1D(cnn_dims, kernel_size, activation='relu', name="conv_client")(merged) # remove temporal dimension by global max pooling y = GlobalMaxPooling1D()(y) y = Dropout(dropout)(y) y = Dense(dense_dim, activation='relu', name="dense_client")(y) out_client = Dense(1, activation='sigmoid', name="client")(y) # model = KerasModel(inputs=(ipt_domains, ipt_flows), outputs=(out_client, out_server)) return Model(ipt_domains, ipt_flows, out_client, out_server)