add deeper domain cnn; refactor hyperband using load_data function

models/networks.py

@@ -2,7 +2,7 @@ from collections import namedtuple
 
 import keras
 from keras.engine import Input, Model as KerasModel
-from keras.layers import Conv1D, Dense, Dropout, Embedding, GlobalMaxPooling1D, TimeDistributed
+from keras.layers import Conv1D, Dense, Dropout, Embedding, GlobalAveragePooling1D, GlobalMaxPooling1D, TimeDistributed
 
 import dataset
 
@@ -22,6 +22,24 @@ def get_domain_embedding_model(embedding_size, input_length, filter_size, kernel
     return KerasModel(x, y)
 
 
+def get_domain_embedding_model2(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='relu')(y)
+    y = Conv1D(filter_size,
+               kernel_size,
+               activation='relu')(y)
+    y = Conv1D(filter_size,
+               kernel_size,
+               activation='relu')(y)
+    y = GlobalAveragePooling1D()(y)
+    y = Dense(hidden_dims, activation="relu")(y)
+    return KerasModel(x, y)
+
+
 def get_final_model(cnnDropout, flow_features, window_size, domain_length, cnn_dims, kernel_size,
                     dense_dim, cnn) -> Model:
     ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")