Minor modification to remove pylint messages

test_model.py

@@ -1,5 +1,11 @@
-import keras
-from keras.models import Model, load_model
+"""
+Simple example on how to fine tune models in Keras and how to use them.
+Part 1
+
+Source: https://www.guru99.com/keras-tutorial.html
+"""
+
+from keras.models import load_model
 from keras.preprocessing.image import ImageDataGenerator
 import matplotlib.pyplot as plt
 import numpy as np
@@ -9,40 +15,45 @@ train_datagen = ImageDataGenerator(validation_split=0.3,
                                    zoom_range=0.2,
                                    horizontal_flip=True)
 
-train_path = 'images/train/'
-test_path = 'images/test/'
-batch_size = 16
-image_size = 224
-num_class = 8
+TRAIN_PATH  = 'images/train/'
+TEST_PATH   = 'images/test/'
+BATCH_SIZE  = 16
+IMAGE_SIZE  = 224
+NUM_CLASSES = 8
 
 ### Testing our model
 model = load_model('fine_tune.h5')
 
 test_datagen = ImageDataGenerator()
 train_generator = train_datagen.flow_from_directory(
-                        directory=train_path,
-                        target_size=(image_size,image_size),
-                        batch_size=batch_size,
+                        directory=TRAIN_PATH,
+                        target_size=(IMAGE_SIZE, IMAGE_SIZE),
+                        batch_size=BATCH_SIZE,
                         class_mode='categorical',
                         color_mode='rgb',
                         shuffle=True)
 
 test_generator = test_datagen.flow_from_directory(
-                        directory=test_path, 
-                        target_size=(image_size, image_size),
+                        directory=TEST_PATH,
+                        target_size=(IMAGE_SIZE, IMAGE_SIZE),
                         color_mode='rgb',
                         shuffle=False,
                         class_mode='categorical',
                         batch_size=1)
 
-filenames = test_generator.filenames
+filenames  = test_generator.filenames
 nb_samples = len(filenames)
 
-fig=plt.figure()
-columns = 4
-rows = 4
-for i in range(1, columns*rows -1):
-    x_batch, y_batch = test_generator.next()
+fig = plt.figure()
+
+COLUMNS  = 4
+ROWS     = 4
+
+# And our test is as given below! Only 1 image is predicted wrong from a test of
+# 14 images!
+
+for i in range(1, COLUMNS*ROWS -1):
+    x_batch, y_batch = next(test_generator)
 
     name = model.predict(x_batch)
     name = np.argmax(name, axis=-1)
@@ -55,12 +66,7 @@ for i in range(1, columns*rows -1):
     true_value = [label_map[k] for k in true_name]
 
     image = x_batch[0].astype(np.int)
-    fig.add_subplot(rows, columns, i)
+    fig.add_subplot(ROWS, COLUMNS, i)
     plt.title(str(predictions[0]) + ':' + str(true_value[0]))
     plt.imshow(image)
 plt.show()
-
-'''
-And our test is as given below! Only 1 image is predicted wrong from a test of 
-14 images!
-'''