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

Makefile

@@ -66,6 +66,6 @@ hyper:
 	python3 main.py --mode hyperband --batch 64 --train data/rk_data.csv.gz
 
 clean:
-	rm -r results/test/test*
+	rm -r results/test/
 	rm data/rk_mini.csv.gz_raw.h5
 	rm data/rk_mini.csv.gz.h5

models/__init__.py

@@ -1,7 +1,6 @@
 import keras.backend as K
 
-from . import pauls_networks
-from . import renes_networks
+from . import flat_2, pauls_networks, renes_networks
 
 
 def get_models_by_params(params: dict):
@@ -27,6 +26,8 @@ def get_models_by_params(params: dict):
     # create models
     if network_depth == "small":
         networks = pauls_networks
+    elif network_depth == "flat":
+        networks = flat_2
     elif network_depth == "medium":
         networks = renes_networks
     else:

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)

models/pauls_networks.py

@@ -52,7 +52,8 @@ def get_model(cnnDropout, flow_features, domain_features, window_size, domain_le
     # CNN processing a small slides of flow windows
     y = Conv1D(cnn_dims,
                kernel_size,
-               activation='relu'
+               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)

models/renes_networks.py

@@ -66,7 +66,7 @@ def get_new_model(dropout, flow_features, domain_features, window_size, domain_l
     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=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

visualize.py

@@ -35,8 +35,10 @@ def plot_clf():
     plt.clf()
 
 
-def plot_save(path, dpi=600):
-    plt.savefig(path, dpi=dpi)
+def plot_save(path, dpi=300):
+    fig = plt.gcf()
+    fig.set_size_inches(18.5, 10.5)
+    fig.savefig(path, dpi=dpi)
     plt.close()