from collections import namedtuple import keras from keras.activations import elu from keras.engine import Input, Model as KerasModel from keras.layers import Conv1D, Dense, Dropout, Embedding, GlobalAveragePooling1D, GlobalMaxPooling1D, MaxPool1D, \ TimeDistributed import dataset def selu(x): """Scaled Exponential Linear Unit. (Klambauer et al., 2017) # Arguments x: A tensor or variable to compute the activation function for. # References - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) # copied from keras.io """ alpha = 1.6732632423543772848170429916717 scale = 1.0507009873554804934193349852946 return scale * elu(x, alpha) Model = namedtuple("Model", ["in_domains", "in_flows", "out_client", "out_server"]) def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden_dims, drop_out=0.5): 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=5, activation=selu)(y) y = Conv1D(filter_size, kernel_size=3, activation=selu)(y) y = Conv1D(filter_size, kernel_size=3, activation=selu)(y) y = GlobalAveragePooling1D()(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"): ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains") encoded = TimeDistributed(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(filters=cnn_dims, kernel_size=kernel_size, activation=selu, padding="same", input_shape=(window_size, domain_features + flow_features))(merged) y = MaxPool1D(pool_size=3, strides=1)(y) y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation=selu, padding="same")(y) y = MaxPool1D(pool_size=3, strides=1)(y) y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation=selu, padding="same")(y) # remove temporal dimension by global max pooling y = GlobalMaxPooling1D()(y) y = Dropout(cnnDropout)(y) y = Dense(dense_dim, activation=selu)(y) y = Dense(dense_dim // 2, activation=selu)(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"): 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")(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(filters=cnn_dims, kernel_size=kernel_size, activation=selu, padding="same", input_shape=(window_size, domain_features + flow_features))(merged) y = MaxPool1D(pool_size=3, strides=1)(y) y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation=selu, padding="same")(y) y = MaxPool1D(pool_size=3, strides=1)(y) y = Conv1D(filters=cnn_dims, kernel_size=kernel_size, activation=selu, padding="same")(y) # remove temporal dimension by global max pooling y = GlobalMaxPooling1D()(y) y = Dropout(dropout)(y) y = Dense(dense_dim, activation=selu, name="dense_client")(y) out_client = Dense(1, activation='sigmoid', name="client")(y) return Model(ipt_domains, ipt_flows, out_client, out_server)