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2020-10-23 10:49:35 +02:00
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 """
 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
 from keras.optimizers import SGD
 TRAIN_PATH  = 'images/train/'
 TEST_PATH   = 'images/test/'
 BATCH_SIZE  = 16
 IMAGE_SIZE  = 224
 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.
 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,
                        target_size=(IMAGE_SIZE,IMAGE_SIZE),
                        batch_size=BATCH_SIZE,
                        class_mode='categorical',
                        color_mode='rgb',
                        shuffle=True)
 # Let's see our images randomly by plotting them with matplotlib
 # x_batch, y_batch = train_generator.next()
 x_batch, y_batch = next(train_generator)
 fig=plt.figure()
 COLUMNS = 4
 ROWS    = 4
 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)
    plt.imshow(image)
 plt.show()
 ### Creating model
 # 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))
 print(base_model.summary())
 # Freeze the layers
 for layer in base_model.layers:
    layer.trainable = False
 # Create the model
 model = keras.models.Sequential()
 # Add the vgg convolutional base model
 model.add(base_model)
 # Add new layers
 model.add(Flatten())
 model.add(Dense(1024, activation='relu'))
 model.add(Dense(1024, activation='relu'))
 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
 # 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
 # activation.
 #### Training
 # Compile the model
 model.compile(loss='categorical_crossentropy',
              optimizer=SGD(lr=1e-3),
              metrics=['accuracy'])
 # Start the training process
 # model.fit(x_train, y_train, validation_split=0.30, batch_size=32, epochs=50, verbose=2)
 # Save the model
 # model.save('catdog.h5')
 history = model.fit_generator(train_generator,
                              steps_per_epoch=train_generator.n/BATCH_SIZE,
                              epochs=10)
 model.save('fine_tune.h5')
 # Summarize history for accuracy
 plt.plot(history.history['loss'])
 plt.title('loss')
 plt.ylabel('loss')
 plt.xlabel('epoch')
 plt.legend(['loss'], loc='upper left')
 plt.show()
 # Our losses are dropped significantly and the accuracy is almost 100%. For
 # testing our model, we randomly picked images over the internet and put it on
 # the test folder with a different class to test
@@ -1,72 +0,0 @@
 """
 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
 """
 from keras.models import load_model
 from keras.preprocessing.image import ImageDataGenerator
 import matplotlib.pyplot as plt
 import numpy as np
 train_datagen = ImageDataGenerator(validation_split=0.3,
                                   shear_range=0.2,
                                   zoom_range=0.2,
                                   horizontal_flip=True)
 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,
                        class_mode='categorical',
                        color_mode='rgb',
                        shuffle=True)
 test_generator = test_datagen.flow_from_directory(
                        directory=TEST_PATH,
                        target_size=(IMAGE_SIZE, IMAGE_SIZE),
                        color_mode='rgb',
                        shuffle=False,
                        class_mode='categorical',
                        batch_size=1)
 filenames  = test_generator.filenames
 nb_samples = len(filenames)
 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)
    true_name = y_batch
    true_name = np.argmax(true_name, axis=-1)
    label_map = (test_generator.class_indices)
    label_map = dict((v,k) for k,v in label_map.items()) #flip k,v
    predictions = [label_map[k] for k in name]
    true_value = [label_map[k] for k in true_name]
    image = x_batch[0].astype(np.int)
    fig.add_subplot(ROWS, COLUMNS, i)
    plt.title(str(predictions[0]) + ':' + str(true_value[0]))
    plt.imshow(image)
 plt.show()