add model

processing/models.py

@@ -9,6 +9,7 @@ from tensorflow.keras.layers import Dense
 from tensorflow.keras.layers import Flatten
 from tensorflow.keras.layers import Input
 from tensorflow.keras.layers import concatenate
+from tensorflow.keras.layers import GaussianNoise
 
 
 
@@ -61,6 +62,17 @@ def create_mlp(dim, regress=True):
     # return our model
     return model
 
+def create_mlp2(dim,regress = True):
+    model = Sequential()
+    model.add(GaussianNoise(0.2, input_shape=(dim,)))
+    model.add(Dense(8, activation="relu"))
+    model.add(Dense(4, activation="relu"))
+    # add dense for regression
+    model.add(Dense(1))
+    return model
+
+
+
 def create_hybrid(nb_attributes,shape=(240,240,1)):
     # create cnn and mlp models
     mlp = create_mlp(nb_attributes)
@@ -69,4 +81,14 @@ def create_hybrid(nb_attributes,shape=(240,240,1)):
     x = Dense(4, activation="relu")(combinedInput)
     x = Dense(1, activation="linear")(x)
     model = Model(inputs=[mlp.input, cnn.input], outputs=x)
+    return model
+
+def create_hybrid2(nb_attributes,shape=(240,240,1)):
+    # create cnn and mlp models
+    mlp = create_mlp2(nb_attributes)
+    cnn = create_cnn(*shape)
+    combinedInput = concatenate([mlp.output, cnn.output])
+    x = Dense(4, activation="relu")(combinedInput)
+    x = Dense(1, activation="linear")(x)
+    model = Model(inputs=[mlp.input, cnn.input], outputs=x)
     return model
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