104 lines
4.4 KiB
Python
104 lines
4.4 KiB
Python
from collections import namedtuple
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import keras
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from keras.activations import elu
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from keras.engine import Input, Model as KerasModel
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from keras.layers import Conv1D, Dense, Dropout, Embedding, GlobalAveragePooling1D, GlobalMaxPooling1D, MaxPool1D, \
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TimeDistributed
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import dataset
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def selu(x):
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"""Scaled Exponential Linear Unit. (Klambauer et al., 2017)
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# Arguments
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x: A tensor or variable to compute the activation function for.
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# References
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- [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
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# copied from keras.io
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"""
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alpha = 1.6732632423543772848170429916717
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scale = 1.0507009873554804934193349852946
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return scale * elu(x, alpha)
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Model = namedtuple("Model", ["in_domains", "in_flows", "out_client", "out_server"])
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def get_embedding(embedding_size, input_length, filter_size, 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=dataset.get_vocab_size(), output_dim=embedding_size)(y)
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y = Conv1D(filter_size, kernel_size=5, activation=selu)(y)
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y = Conv1D(filter_size, kernel_size=3, activation=selu)(y)
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y = Conv1D(filter_size, kernel_size=3, activation=selu)(y)
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y = GlobalAveragePooling1D()(y)
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y = Dense(hidden_dims, activation=selu)(y)
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return KerasModel(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, model_output="both"):
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ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
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encoded = TimeDistributed(cnn, name="domain_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(filters=cnn_dims, kernel_size=kernel_size, activation=selu, padding="same",
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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=selu, padding="same")(y)
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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=selu, padding="same")(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=selu)(y)
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y = Dense(dense_dim, activation=selu)(y)
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out_client = Dense(1, activation='sigmoid', name="client")(y)
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out_server = Dense(1, activation='sigmoid', name="server")(y)
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return Model(ipt_domains, ipt_flows, out_client, out_server)
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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, model_output="both"):
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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, name="domain_cnn")(ipt_domains)
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merged = keras.layers.concatenate([encoded, ipt_flows], -1)
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y = Dense(dense_dim, activation=selu)(merged)
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y = Dense(dense_dim,
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activation="relu",
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name="dense_server")(y)
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out_server = Dense(1, activation="sigmoid", name="server")(y)
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merged = keras.layers.concatenate([merged, y], -1)
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# CNN processing a small slides of flow windows
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y = Conv1D(filters=cnn_dims,
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kernel_size=kernel_size,
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activation=selu,
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padding="same",
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input_shape=(window_size, domain_features + flow_features))(merged)
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y = MaxPool1D(pool_size=3,
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strides=1)(y)
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y = Conv1D(filters=cnn_dims,
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kernel_size=kernel_size,
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activation=selu,
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padding="same")(y)
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y = MaxPool1D(pool_size=3,
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strides=1)(y)
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y = Conv1D(filters=cnn_dims,
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kernel_size=kernel_size,
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activation=selu,
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padding="same")(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(dropout)(y)
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y = Dense(dense_dim, activation=selu)(y)
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y = Dense(dense_dim,
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activation=selu,
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name="dense_client")(y)
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out_client = Dense(1, activation='sigmoid', name="client")(y)
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return Model(ipt_domains, ipt_flows, out_client, out_server)
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