Minor modification to remove pylint messages

linear_regression.py

@@ -1,5 +1,6 @@
 """
-Simple linear regression application.
+Simple example on how to fine tune models in Keras and how to use them.
+Part 2
 
 Source: https://www.guru99.com/keras-tutorial.html
 """

prepare_train_model.py

@@ -1,65 +1,66 @@
-# Source: https://www.guru99.com/keras-tutorial.html
+"""
+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
+"""
 
 #### Data preparation
 from keras.preprocessing.image import ImageDataGenerator
 import numpy as np
 import matplotlib.pyplot as plt
+import keras
+from keras.layers import Flatten, Dense
+from keras.applications.vgg16 import VGG16
 
-train_path = 'images/train/'
+TRAIN_PATH  = 'images/train/'
-test_path = 'images/test/'
+TEST_PATH   = 'images/test/'
-batch_size = 16
+BATCH_SIZE  = 16
-image_size = 224
+IMAGE_SIZE  = 224
-num_class = 8
+NUM_CLASSES = 8
+
+# The ImageDataGenerator will make an X_training data from a directory.
+# The sub-directory in that directory will be used as a class for each object.
+# The image will be loaded with the RGB color mode, with the categorical class
+# mode for the Y_training data, with a batch size of 16. Finally, shuffle the
+# data.
 
-'''
-The ImageDataGenerator will make an X_training data from a directory. 
-The sub-directory in that directory will be used as a class for each object. 
-The image will be loaded with the RGB color mode, with the categorical class 
-mode for the Y_training data, with a batch size of 16. Finally, shuffle the 
-data.
-'''
 train_datagen = ImageDataGenerator(validation_split=0.3,
                                    shear_range=0.2,
                                    zoom_range=0.2,
                                    horizontal_flip=True)
 
 train_generator = train_datagen.flow_from_directory(
-                        directory=train_path,
+                        directory=TRAIN_PATH,
-                        target_size=(image_size,image_size),
+                        target_size=(IMAGE_SIZE,IMAGE_SIZE),
-                        batch_size=batch_size,
+                        batch_size=BATCH_SIZE,
                         class_mode='categorical',
                         color_mode='rgb',
                         shuffle=True)
 
-'''
+
-Let's see our images randomly by plotting them with matplotlib
+# Let's see our images randomly by plotting them with matplotlib
-'''
+# x_batch, y_batch = train_generator.next()
-x_batch, y_batch = train_generator.next()
+x_batch, y_batch = next(train_generator)
 fig=plt.figure()
-columns = 4
+
-rows = 4
+COLUMNS = 4
-for i in range(1, columns*rows):
+ROWS    = 4
-    num = np.random.randint(batch_size)
+
+for i in range(1, COLUMNS*ROWS):
+    num = np.random.randint(BATCH_SIZE)
     image = x_batch[num].astype(np.int)
-    fig.add_subplot(rows, columns, i)
+    fig.add_subplot(ROWS, COLUMNS, i)
     plt.imshow(image)
 plt.show()
 
 ### Creating model
-import keras
+# Let's create our network model from VGG16 with imageNet pre-trained weight.
-from keras.models import Model, load_model
+# We will freeze these layers so that the layers are not trainable to help us
-from keras.layers import Activation, Dropout, Flatten, Dense
+# reduce the computation time.
-from keras.preprocessing.image import ImageDataGenerator
-from keras.applications.vgg16 import VGG16
-
-'''
-Let's create our network model from VGG16 with imageNet pre-trained weight. 
-We will freeze these layers so that the layers are not trainable to help us 
-reduce the computation time.
-'''
 
 # Load the VGG model
-base_model = VGG16(weights='imagenet', include_top=False, input_shape=(image_size, image_size, 3))
+base_model = VGG16(weights='imagenet', include_top=False, input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3))
 
 print(base_model.summary())
 
@@ -77,45 +78,39 @@ model.add(base_model)
 model.add(Flatten())
 model.add(Dense(1024, activation='relu'))
 model.add(Dense(1024, activation='relu'))
-model.add(Dense(num_class, activation='softmax'))
+model.add(Dense(NUM_CLASSES, activation='softmax'))
 
 # Show a summary of the model. Check the number of trainable parameters
 print(model.summary())
 
 input("Press Enter to continue...")
 
-'''
+# As you can see, the summary of our network model. From an input from
-As you can see, the summary of our network model. From an input from 
+# VGG16 Layers, then we add 2 Fully Connected Layer which will extract 1024
-VGG16 Layers, then we add 2 Fully Connected Layer which will extract 1024 
+# features and an output layer that will compute the 8 classes with the softmax
-features and an output layer that will compute the 8 classes with the softmax 
+# activation.
-activation.
-'''
 
 #### Training
 # Compile the model
-from keras.optimizers import SGD
+# from keras.optimizers import SGD
 
-model.compile(loss='categorical_crossentropy',
+# model.compile(loss='categorical_crossentropy',
-              optimizer=SGD(lr=1e-3),
+#               optimizer=SGD(lr=1e-3),
-              metrics=['accuracy'])
+#               metrics=['accuracy'])
 
-'''
 # Start the training process
-model.fit(x_train, y_train, validation_split=0.30, batch_size=32, epochs=50, verbose=2)
+# model.fit(x_train, y_train, validation_split=0.30, batch_size=32, epochs=50, verbose=2)
 
 # Save the model
-model.save('catdog.h5')
+# model.save('catdog.h5')
-'''
 
 history = model.fit_generator(train_generator,
-                              steps_per_epoch=train_generator.n/batch_size,
+                              steps_per_epoch=train_generator.n/BATCH_SIZE,
                               epochs=10)
 
 model.save('fine_tune.h5')
 
-# summarize history for accuracy
+# Summarize history for accuracy
-import matplotlib.pyplot as plt
-
 plt.plot(history.history['loss'])
 plt.title('loss')
 plt.ylabel('loss')
@@ -123,8 +118,7 @@ plt.xlabel('epoch')
 plt.legend(['loss'], loc='upper left')
 plt.show()
 
-'''
+
-As you can see, our losses are dropped significantly and the accuracy is 
+# Our losses are dropped significantly and the accuracy is almost 100%. For
-almost 100%. For testing our model, we randomly picked images over the internet 
+# testing our model, we randomly picked images over the internet and put it on
-and put it on the test folder with a different class to test
+# the test folder with a different class to test
-'''

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
@@ -9,27 +15,27 @@ train_datagen = ImageDataGenerator(validation_split=0.3,
                                    zoom_range=0.2,
                                    horizontal_flip=True)
 
-train_path = 'images/train/'
+TRAIN_PATH  = 'images/train/'
-test_path = 'images/test/'
+TEST_PATH   = 'images/test/'
-batch_size = 16
+BATCH_SIZE  = 16
-image_size = 224
+IMAGE_SIZE  = 224
-num_class = 8
+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,
+                        directory=TRAIN_PATH,
-                        target_size=(image_size,image_size),
+                        target_size=(IMAGE_SIZE, IMAGE_SIZE),
-                        batch_size=batch_size,
+                        batch_size=BATCH_SIZE,
                         class_mode='categorical',
                         color_mode='rgb',
                         shuffle=True)
 
 test_generator = test_datagen.flow_from_directory(
-                        directory=test_path, 
+                        directory=TEST_PATH,
-                        target_size=(image_size, image_size),
+                        target_size=(IMAGE_SIZE, IMAGE_SIZE),
                         color_mode='rgb',
                         shuffle=False,
                         class_mode='categorical',
@@ -39,10 +45,15 @@ filenames = test_generator.filenames
 nb_samples = len(filenames)
 
 fig = plt.figure()
-columns = 4
+
-rows = 4
+COLUMNS  = 4
-for i in range(1, columns*rows -1):
+ROWS     = 4
-    x_batch, y_batch = test_generator.next()
+
+# 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!
-'''