59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
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import tensorflow as tf
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import glob
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import numpy as np
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import os
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from PIL import Image
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from sklearn.model_selection import train_test_split
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from tensorflow import keras
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images = []
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labels = []
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class_names = ['fragile', 'priority', 'skull']
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image_dir = 'C:\\Users\\mateu\\Desktop\\SI projekt\\resources'
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image_size = (32, 32)
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for class_index, class_name in enumerate(class_names):
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class_path = os.path.join(image_dir, class_name)
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file_names = os.listdir(class_path)
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print(class_path)
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for file_name in file_names:
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image_path = os.path.join(class_path, file_name)
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image = Image.open(image_path).convert('L')
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image = image.resize(image_size)
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image_array = np.array(image) / 255.0
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images.append(image_array)
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labels.append(class_index)
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images = np.array(images)
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labels = np.array(labels)
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X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)
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num_classes = 3
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y_train_encoded = keras.utils.to_categorical(y_train, num_classes)
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y_test_encoded = keras.utils.to_categorical(y_test, num_classes)
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model = keras.Sequential([
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keras.layers.Flatten(input_shape=image_size),
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keras.layers.Dense(64, activation='relu'),
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keras.layers.Dense(num_classes, activation='softmax')
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])
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model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
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batch_size = 32
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epochs = 10
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model.fit(X_train, y_train_encoded, batch_size=batch_size, epochs=epochs,
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validation_data=(X_test, y_test_encoded))
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test_loss, test_acc = model.evaluate(X_test, y_test_encoded)
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print('Test accuracy:', test_acc)
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predictions = model.predict(X_test)
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model.save('resources/model.h5')
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