diff --git a/mini_proj/waldo_model.py b/mini_proj/waldo_model.py
index 08160e0..c29b8c4 100644
--- a/mini_proj/waldo_model.py
+++ b/mini_proj/waldo_model.py
@@ -38,12 +38,12 @@ def FCN():
     conv3 = Conv2D(32, (3, 3), activation='relu', padding='same')(m_pool2)
     # drop2 = Dropout(0.2)(conv3) # Drop some portion of features to prevent overfitting
     # m_pool2 = MaxPooling2D(pool_size=(2, 2))(drop2)
-    
+
     # conv4 = Conv2D(64, (2, 2), activation='relu', padding='same')(m_pool2)
 
     flat = Flatten()(conv3)                             # Makes data 1D
     dense = Dense(64, activation='relu')(flat)         # Fully connected layer
-    drop3 = Dropout(0.2)(dense)                         
+    drop3 = Dropout(0.2)(dense)
     classif = Dense(2, activation='sigmoid')(drop3)    # Final layer to classify
 
     ## Define the model structure
@@ -53,6 +53,22 @@ def FCN():
 
     return model
 
+def precision(y_true, y_pred):
+    y_pred = np.round(y_pred)
+    num = np.sum(np.logical_and(y_true, y_pred))
+    den = np.sum(y_pred)
+    return np.divide(num, den)
+
+def recall(y_true, y_pred):
+    y_pred = np.round(y_pred)
+    num = np.sum(np.logical_and(y_true, y_pred))
+    den = np.sum(y_true)
+    return np.divide(num, den)
+
+def f_measure(y_true, y_pred):
+    p = precision(y_true, y_pred)
+    r = recall(y_true, y_pred)
+    return 2 * p * r / (p + r)
 
 ## Open data
 im_train = np.load('Waldo_train_data.npy')
@@ -71,7 +87,7 @@ model = FCN()
 # ensemble_iclf = ImageClassifier(ensemble.RandomForestClassifier)
 
 ## Define training parameters
-epochs = 20             # an epoch is one forward pass and back propogation of all training data 
+epochs = 20             # an epoch is one forward pass and back propogation of all training data
 batch_size = 150        # batch size - number of training example used in one forward/backward pass
 # (higher batch size uses more memory, smaller batch size takes more time)
 #lrate = 0.01           # Learning rate of the model - controls magnitude of weight changes in training the NN
@@ -131,4 +147,3 @@ accuracy = accuracy_score(lbl_test, pred_lbl)
 print("Accuracy: " + str(accuracy))
 print("Images generated in {} seconds".format(end - start))
 np.save('predicted_results.npy', pred_lbl)
-