fix missing parameters, add flat network structure, make larger graphics

models/flat_2.py

@@ -0,0 +1,82 @@
+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, 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) -> 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=selu)(y)
+    y = GlobalAveragePooling1D()(y)
+    y = Dense(hidden_dims, activation=selu)(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=selu,
+               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=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") -> 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=selu)(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)
+    # 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)