separating logical sections into dataset, models and main.

continued initial refactoring

models.py

@@ -0,0 +1,53 @@
+import keras
+from keras.engine import Input, Model
+from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense, Dropout, Activation, Reshape
+
+
+def get_shared_cnn(vocabSize, embeddingSize, 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 = Conv1D(filters, kernel_size, activation='relu')(y)
+    y = GlobalMaxPooling1D()(y)
+    y = Dense(hidden_dims)(y)
+    y = Dropout(drop_out)(y)
+    y = Activation('relu')(y)
+    return Model(x, y)
+
+
+def get_full_model(vocabSize, embeddingSize, maxLen, domainFeatures, flowFeatures,
+                   filters, h1, h2, dropout, dense):
+    pass
+
+
+def get_top_cnn(cnn, numFeatures, maxLen, windowSize, domainFeatures, filters, kernel_size, cnnHiddenDims, cnnDropout):
+    inputList = []
+    encodedList = []
+    # TODO: ???
+    for i in range(windowSize):
+        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,
+                 kernel_size,
+                 activation='relu',
+                 input_shape=(windowSize, domainFeatures + numFeatures))(reshape)
+    # we use max pooling:
+    maxPool = GlobalMaxPooling1D()(cnn)
+    cnnDropout = Dropout(cnnDropout)(maxPool)
+    cnnDense = Dense(cnnHiddenDims, activation='relu')(cnnDropout)
+    cnnOutput = Dense(2, activation='softmax')(cnnDense)
+
+    # We define a trainable model linking the
+    # tweet inputs to the predictions
+    model = Model(inputs=inputList, outputs=cnnOutput)
+    return model