Another test fix

main.py

@@ -185,6 +185,21 @@ def save_model(sess, training_loss_metrics, validation_loss_metrics,
     with open('validation_accuracy_history', 'wb') as f:
         pickle.dump(validation_accuracy_history, f)
 
+def evaluate(image_paths, data_folder, image_shape, sess, input_image,correct_label, keep_prob, loss_op, accuracy_op, is_training):
+    data_generator_function = helper.gen_batch_function(data_folder, image_shape, image_paths, augment=False)
+    batch_size = 8
+    data_generator = data_generator_function(batch_size)
+    num_examples = int(math.floor(len(image_paths)/batch_size)*batch_size)
+    total_loss = 0
+    total_acc = 0
+    for offset in range(0, num_examples, batch_size):
+        X_batch, y_batch = next(data_generator)
+        loss, accuracy = sess.run([loss_op, accuracy_op], feed_dict={input_image: X_batch, correct_label: y_batch, keep_prob: 1.0,
+                                              is_training: False})
+        total_loss += (loss * X_batch.shape[0])
+        total_acc += (accuracy * X_batch.shape[0])
+    return total_loss/num_examples, total_acc/num_examples
+
 
 def train_nn(sess, epochs, data_folder, image_shape, batch_size, training_image_paths, validation_image_paths, train_op,
              cross_entropy_loss, accuracy_op, input_image, correct_label, keep_prob, learning_rate, is_training):
@@ -249,22 +264,6 @@ def train_nn(sess, epochs, data_folder, image_shape, batch_size, training_image_
 tests.test_train_nn(train_nn)
 
 
-def evaluate(image_paths, data_folder, image_shape, sess, input_image,correct_label, keep_prob, loss_op, accuracy_op, is_training):
-    data_generator_function = helper.gen_batch_function(data_folder, image_shape, image_paths, augment=False)
-    batch_size = 8
-    data_generator = data_generator_function(batch_size)
-    num_examples = int(math.floor(len(image_paths)/batch_size)*batch_size)
-    total_loss = 0
-    total_acc = 0
-    for offset in range(0, num_examples, batch_size):
-        X_batch, y_batch = next(data_generator)
-        loss, accuracy = sess.run([loss_op, accuracy_op], feed_dict={input_image: X_batch, correct_label: y_batch, keep_prob: 1.0,
-                                              is_training: False})
-        total_loss += (loss * X_batch.shape[0])
-        total_acc += (accuracy * X_batch.shape[0])
-    return total_loss/num_examples, total_acc/num_examples
-
-
 def run():
     global LEARNING_RATE
     global KEEP_PROB