forked from s444519/Waiter_group
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
##My cnn, classyfing the plates as dirty, clean or full.
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#imports
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from keras.models import Sequential
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from keras.layers import Convolution2D
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from keras.layers import MaxPooling2D
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from keras.layers import Flatten
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from keras.layers import Dense
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from keras.callbacks import EarlyStopping
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from keras.callbacks import ModelCheckpoint
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#initializing:
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classifier = Sequential()
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#Convolution:
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classifier.add(Convolution2D(32, (3, 3), input_shape =(256, 256, 3), activation = "relu"))
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#Pooling:
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classifier.add(MaxPooling2D(pool_size = (2,2)))
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# Adding a second convolutional layer
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classifier.add(Convolution2D(32, 3, 3, activation = 'relu'))
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classifier.add(MaxPooling2D(pool_size = (2, 2)))
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#Flattening:
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classifier.add(Flatten())
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#Fully connected layers::
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classifier.add(Dense(units = 128, activation = "relu"))
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classifier.add(Dense(units = 3, activation = "softmax"))
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#Making CNN:
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classifier.compile(optimizer = "adam", loss = "categorical_crossentropy", metrics = ["accuracy"])
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#From KERAS:
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from keras.preprocessing.image import ImageDataGenerator
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#Data augmentation:
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train_datagen = ImageDataGenerator(
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rescale=1./255,
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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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width_shift_range=0.2,
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height_shift_range=0.1,
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fill_mode='nearest')
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test_datagen = ImageDataGenerator(rescale=1./255)
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training_set = train_datagen.flow_from_directory('plates/training_set',
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target_size=(256, 256),
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batch_size=16,
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class_mode='categorical')
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test_set = test_datagen.flow_from_directory('plates/test_set',
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target_size=(256, 256),
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batch_size=16,
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class_mode='categorical')
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# callbacks:
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es = EarlyStopping(monitor='val_loss', mode='min', baseline=1, patience = 10)
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mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', save_best_only=True, verbose = 1, period = 10)
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classifier.fit_generator(
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training_set,
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steps_per_epoch = 88,
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epochs=200,
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callbacks=[es, mc],
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validation_data=test_set,
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validation_steps=10)
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