Update of SVD model

README.md

@@ -17,8 +17,8 @@ It will split scenes and generate all data you need for your neural network.
 You can specify the number of sub images you want in the script by modifying **_NUMBER_SUB_IMAGES_** variables.
 
 There are 3 kinds of Neural Networks :
-- **classification_cnn_keras.py** : *based croped on images*
-- **classification_cnn_keras_crossentropy.py** : *based croped on images which are randomly split for training*
+- **classification_cnn_keras.py** : *based on cropped images and do convolution*
+- **classification_cnn_keras_cross_validation.py** : *based on cropped images and do convolution. Data are randomly split for training*
 - **classification_cnn_keras_svd.py** : *based on svd metrics of image*
 
 Note that the image input size need to change in you used specific size for your croped images.

classification_cnn_keras_svd.py

@@ -80,24 +80,44 @@ def generate_model():
     model.add(MaxPooling2D(pool_size=(2, 1)))
 
     model.add(Flatten())
+    model.add(Dense(50, kernel_regularizer=l2(0.01)))
+    model.add(Activation('relu'))
     model.add(BatchNormalization())
-    model.add(Dense(300, kernel_regularizer=l2(0.01)))
+    model.add(Dropout(0.1))
+
+    model.add(Dense(100, kernel_regularizer=l2(0.01)))
     model.add(Activation('relu'))
-    model.add(Dropout(0.4))
+    model.add(BatchNormalization())
+    model.add(Dropout(0.1))
 
-    model.add(Dense(30, kernel_regularizer=l2(0.01)))
+    model.add(Dense(200, kernel_regularizer=l2(0.01)))
+    model.add(Activation('relu'))
     model.add(BatchNormalization())
+    model.add(Dropout(0.2))
+
+    model.add(Dense(300, kernel_regularizer=l2(0.01)))
     model.add(Activation('relu'))
+    model.add(BatchNormalization())
     model.add(Dropout(0.3))
 
+    model.add(Dense(200, kernel_regularizer=l2(0.01)))
+    model.add(Activation('relu'))
+    model.add(BatchNormalization())
+    model.add(Dropout(0.2))
+
     model.add(Dense(100, kernel_regularizer=l2(0.01)))
+    model.add(Activation('relu'))
     model.add(BatchNormalization())
+    model.add(Dropout(0.1))
+
+    model.add(Dense(50, kernel_regularizer=l2(0.01)))
     model.add(Activation('relu'))
-    model.add(Dropout(0.2))
+    model.add(BatchNormalization())
+    model.add(Dropout(0.1))
 
     model.add(Dense(20, kernel_regularizer=l2(0.01)))
-    model.add(BatchNormalization())
     model.add(Activation('relu'))
+    model.add(BatchNormalization())
     model.add(Dropout(0.1))
 
     model.add(Dense(1))