Change model structures and rerun CNN_inj_sup_4chann

processing/models.py

@@ -17,7 +17,7 @@ from tensorflow import TensorShape
 from sklearn.model_selection import train_test_split
 
 
-def create_cnn(width, height, depth, filters=(32, 64, 128), regress=False):
+def create_cnn(width, height, depth, filters=(32, 64, 128), regress=False, type_cnn='simple'):
     # initialize the input shape and channel dimension, assuming
     # TensorFlow/channels-last ordering
     inputShape = (height, width, depth)
@@ -41,18 +41,19 @@ def create_cnn(width, height, depth, filters=(32, 64, 128), regress=False):
     # flatten the volume, then FC => RELU => BN => DROPOUT
     x = Flatten()(x)
     x = Dense(16)(x)
-    x = Activation("relu")(x) #sigmoid ou tanh
+    x = Activation("tanh")(x) #sigmoid ou tanh
     x = BatchNormalization(axis=chanDim)(x)
     x = Dropout(0.5)(x)
     # apply another FC layer, this one to match the number of nodes
     # coming out of the MLP
     x = Dense(4)(x)
-    x = Activation("relu")(x) #sigmoid ou tanh
+    x = Activation("tanh")(x) #sigmoid ou tanh
     # check to see if the regression node should be added
     
     # la couche suivante ne sert à rien pour l'hybride, à garder pour le modèle cnn seule
-    if regress:
-        x = Dense(1, activation="linear")(x)
+    if type_cnn == 'simple':
+        if regress:
+            x = Dense(1, activation="linear")(x)
     # construct the CNN
     model = Model(inputs, x)
     # return the CNN
@@ -61,28 +62,32 @@ def create_cnn(width, height, depth, filters=(32, 64, 128), regress=False):
 def create_mlp(dim, regress=True):
     # define our MLP network
     model = Sequential()
-    model.add(Dense(8, input_dim=dim, activation="relu"))#tanh
-    model.add(Dense(4, activation="relu")) # tanh
+    model.add(Dense(8, input_dim=dim, activation="tanh"))#tanh
+    model.add(Dropout(0.3))
+    model.add(Dense(4, activation="tanh")) # tanh
     # add dense for regression
-    model.add(Dense(1, activation="linear")) #tanh
+    #model.add(Dense(1, activation="linear")) #tanh
     # return our model
     return model
 
 def create_mlp2(dim,regress = True): #mieux que mlp, 
     model = Sequential()
     model.add(GaussianNoise(0.2, input_shape=(dim,)))
-    model.add(Dense(8, activation="relu"))
-    model.add(Dense(4, activation="relu"))
+    model.add(Dropout(0.5))
+    #modified from relu to tanh
+    model.add(Dense(8, activation="tanh"))
+    model.add(Dropout(0.5))
+    model.add(Dense(4, activation="tanh"))
     # add dense for regression
-    model.add(Dense(1)) # couche à enlever trop de couches dans ce modele, et surtout pas à 1, idem pour mlp pour hybride
+    #model.add(Dense(1)) # couche à enlever trop de couches dans ce modele, et surtout pas à 1, idem pour mlp pour hybride
     return model
 
 def create_hybrid(nb_attributes,shape=(240,240,1)):
     # create cnn and mlp models
     mlp = create_mlp(nb_attributes)
-    cnn = create_cnn(*shape)
+    cnn = create_cnn(*shape,type_cnn='hybrid')
     combinedInput = concatenate([mlp.output, cnn.output]) 
-    x = Dense(4, activation="relu")(combinedInput) #tanh
+    x = Dense(4, activation="tanh")(combinedInput) #tanh
     x = Dense(1, activation="linear")(x)
     model = Model(inputs=[mlp.input, cnn.input], outputs=x)
     return model
@@ -142,7 +147,7 @@ def create_transfer_learning(new_model, custom_model, modify_name,input_channel
     x = new.output
     x = GlobalAveragePooling2D()(x)
     x = Dropout(0.5)(x) 
-    x = Dense(1)(x)
+    #x = Dense(1)(x)
     model = Model(new.input, x)
 
     for layer in new.layers:
@@ -155,7 +160,7 @@ def create_hybrid_transfer(nb_attributes,new_model, custom_model, modify_name,in
     mlp = create_mlp(nb_attributes)
     cnn = create_transfer_learning(new_model, custom_model, modify_name,input_channel)
     combinedInput = concatenate([mlp.output, cnn.output])
-    x = Dense(4, activation="relu")(combinedInput)
+    x = Dense(4, activation="tanh")(combinedInput)
     x = Dense(1, activation="linear")(x)
     model = Model(inputs=[mlp.input, cnn.input], outputs=x)
     return model