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