Need add regression to DenseNet.

transfer_learning/inceptionv3_fine_tune.py

@@ -4,7 +4,7 @@ from keras.preprocessing import image
 from keras.models import Model
 from keras.layers import Dense, GlobalAveragePooling2D
 from keras import backend as K
-from generate_arrays_fddb import generate_arrays_fddb
+from generate_arrays_fddb import load_arrays_fddb 
 
 # create the base pre-trained model
 base_model = InceptionV3(weights='imagenet', include_top=False)
@@ -28,17 +28,31 @@ for layer in base_model.layers:
 # compile the model (should be done *after* setting layers to non-trainable)
 model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
 
-fddb_path = "../fddb/FDDB-folds/FDDB-fold-01-ellipseList.txt"
+fddb_path_train = "../fddb/FDDB-folds/FDDB-fold-01-ellipseList.txt"
+fddb_path_valid = "../fddb/FDDB-folds/FDDB-fold-02-ellipseList.txt"
+
+# loading the training and validation data from FDDB.
+x_train, y_train = load_arrays_fddb(fddb_path_train) 
+x_valid, y_valid = load_arrays_fddb(fddb_path_valid)
 
 ## train the model on the new data for a few epochs
 try:
-    model.fit_generator(
-        generator=generate_arrays_fddb(fddb_path),
-        steps_per_epoch=1000, epochs=10
+    model.fit(
+        x_train, y_train,
+        batch_size=32,
+        epochs=10,
+        verbose=1,
+        validation_data=(x_valid, y_valid)
     )
 except Exception as e:
     print(e)
 
+# Make predictions
+predictions_valid = model.predict(x_valid, batch_size=32, verbose=1)
+
+# Cross-entropy loss score
+performance = log_loss(y_valid, predictions_valid)
+
 # at this point, the top layers are well trained and we can start fine-tuning
 # convolutional layers from inception V3. We will freeze the bottom N layers
 # and train the remaining top layers.