refactor network to use new input format

Makefile

@@ -0,0 +1,3 @@
+test:
+	python3 main.py --epochs 1 --batch 64
+

models.py

@@ -1,12 +1,12 @@
 import keras
 from keras.engine import Input, Model
-from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, Activation, Reshape
+from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, Activation, TimeDistributed
 
 
-def get_shared_cnn(vocabSize, embeddingSize, input_length, filters, kernel_size,
+def get_shared_cnn(vocab_size, embedding_size, input_length, filters, kernel_size,
                    hidden_dims, drop_out):
     x = y = Input(shape=(input_length,))
-    y = Embedding(input_dim=vocabSize, output_dim=embeddingSize)(y)
+    y = Embedding(input_dim=vocab_size, output_dim=embedding_size)(y)
     y = Conv1D(filters, kernel_size, activation='relu')(y)
     y = GlobalMaxPooling1D()(y)
     y = Dense(hidden_dims)(y)
@@ -21,33 +21,21 @@ def get_full_model(vocabSize, embeddingSize, maxLen, domainFeatures, flowFeature
 
 
 def get_top_cnn(cnn, numFeatures, maxLen, windowSize, domainFeatures, filters, kernel_size, cnnHiddenDims, cnnDropout):
-    inputList = []
+    ipt_domains = Input(shape=(windowSize, maxLen), name="ipt_domains")
-    encodedList = []
+    encoded = TimeDistributed(cnn)(ipt_domains)
-    # TODO: ???
+    ipt_flows = Input(shape=(windowSize, numFeatures), name="ipt_flows")
-    for i in range(windowSize):
+    merged = keras.layers.concatenate([encoded, ipt_flows], -1)
-        inputList.append(Input(shape=(maxLen,)))
-        encodedList.append(cnn(inputList[-1]))  # add shared domain model
-        inputList.append(Input(shape=(numFeatures,)))
-    # TODO: ???
-    merge_layer_input = []
-    for i in range(windowSize):
-        merge_layer_input.append(encodedList[i])
-        merge_layer_input.append(inputList[(2 * i) + 1])
-    # We can then concatenate the two vectors:
-    merged_vector = keras.layers.concatenate(merge_layer_input, axis=-1)
-    reshape = Reshape((windowSize, domainFeatures + numFeatures))(merged_vector)
     # add second cnn
-    cnn = Conv1D(filters,
+    y = Conv1D(filters,
-                 kernel_size,
+               kernel_size,
-                 activation='relu',
+               activation='relu',
-                 input_shape=(windowSize, domainFeatures + numFeatures))(reshape)
+               input_shape=(windowSize, domainFeatures + numFeatures))(merged)
+    # TODO: why global pooling? -> 3D to 2D
     # we use max pooling:
-    maxPool = GlobalMaxPooling1D()(cnn)
+    y = GlobalMaxPooling1D()(y)
-    cnnDropout = Dropout(cnnDropout)(maxPool)
+    y = Dropout(cnnDropout)(y)
-    cnnDense = Dense(cnnHiddenDims, activation='relu')(cnnDropout)
+    y = Dense(cnnHiddenDims, activation='relu')(y)
-    cnnOutput1 = Dense(2, activation='softmax', name="client")(cnnDense)
+    y1 = Dense(2, activation='softmax', name="client")(y)
-    cnnOutput2 = Dense(2, activation='softmax', name="server")(cnnDense)
+    y2 = Dense(2, activation='softmax', name="server")(y)
 
-    # We define a trainable model linking the
+    return Model(inputs=[ipt_domains, ipt_flows], outputs=(y1, y2))
-    # tweet inputs to the predictions
-    return Model(inputs=inputList, outputs=(cnnOutput1, cnnOutput2))