add bulk embedding visualization and deep1 network

models/__init__.py

@@ -1,5 +1,6 @@
 import keras.backend as K
 
+from models import deep1
 from models.renes_networks import selu
 from . import flat_2, pauls_networks, renes_networks
 
@@ -10,7 +11,6 @@ def get_models_by_params(params: dict):
     # network_type = params.get("type")
     network_depth = params.get("depth")
     embedding_size = params.get("embedding")
-    input_length = params.get("input_length")
     filter_embedding = params.get("filter_embedding")
     kernel_embedding = params.get("kernel_embedding")
     hidden_embedding = params.get("dense_embedding")
@@ -32,7 +32,7 @@ def get_models_by_params(params: dict):
         networks = renes_networks
     else:
         raise Exception("network not found")
-    embedding_model = networks.get_embedding(embedding_size, input_length, filter_embedding, kernel_embedding,
+    embedding_model = networks.get_embedding(embedding_size, domain_length, filter_embedding, kernel_embedding,
                                              hidden_embedding, 0.5)
 
     old_model = networks.get_model(0.25, flow_features, hidden_embedding, window_size, domain_length,
@@ -63,6 +63,8 @@ def get_server_model_by_params(params: dict):
     elif network_depth == "flat2":
         networks = flat_2
     elif network_depth == "deep1":
+        networks = deep1
+    elif network_depth == "deep2":
         networks = renes_networks
     else:
         raise Exception("network not found")

models/deep1.py

@@ -0,0 +1,70 @@
+from collections import namedtuple
+
+import keras
+from keras.engine import Input, Model as KerasModel
+from keras.layers import Conv1D, Dense, Dropout, Embedding, GlobalAveragePooling1D, GlobalMaxPooling1D, TimeDistributed
+
+import dataset
+
+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=kernel_size, activation="relu")(y)
+    y = Conv1D(filter_size, kernel_size=3, activation="relu")(y)
+    y = Conv1D(filter_size, kernel_size=3, activation="relu")(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, 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(filters=cnn_dims, kernel_size=kernel_size, activation="relu", padding="same",
+               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)
+    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"):
+    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)
+    y = Dense(dense_dim,
+              activation="relu",
+              name="dense_server")(y)
+    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="relu",
+               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="relu")(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)

models/pauls_networks.py

@@ -95,6 +95,8 @@ 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",