71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
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# Importing libraries
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from keras.models import Sequential
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from keras.layers import Conv2D
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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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# Initialising the CNN
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classifier = Sequential()
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# Step 1 - Convolution
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classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
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# Step 2 - 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(Conv2D(32, (3, 3), activation = 'relu'))
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classifier.add(MaxPooling2D(pool_size = (2, 2)))
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# Step 3 - Flattening
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classifier.add(Flatten())
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# Step 4 - Full connection
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classifier.add(Dense(units = 128, activation = 'relu'))
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classifier.add(Dense(units = 1, activation = 'sigmoid'))
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# Compiling the CNN
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classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
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# Part 2 - Fitting the CNN to the images
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from keras.preprocessing.image import ImageDataGenerator
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train_datagen = ImageDataGenerator(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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test_datagen = ImageDataGenerator(rescale = 1./255)
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training_set = train_datagen.flow_from_directory('dataset/training_set',
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target_size = (64, 64),
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batch_size = 32,
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class_mode = 'binary')
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test_set = test_datagen.flow_from_directory('dataset/test_set',
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target_size = (64, 64),
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batch_size = 32,
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class_mode = 'binary')
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classifier.fit_generator(training_set,
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steps_per_epoch = 8000,
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epochs = 25,
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validation_data = test_set,
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validation_steps = 2000)
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# Part 3 - Making new predictions
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import numpy as np
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from keras.preprocessing import image
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test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size = (64, 64))
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test_image = image.img_to_array(test_image)
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test_image = np.expand_dims(test_image, axis = 0)
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result = classifier.predict(test_image)
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training_set.class_indices
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if result[0][0] == 1:
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prediction = 'dog'
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else:
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prediction = 'cat' |