68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
import tensorflow as tf
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from keras import layers
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from keras.models import Sequential
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from keras.optimizers import Adam
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from keras.utils import to_categorical
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from keras.preprocessing.image import ImageDataGenerator
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# Normalizes the pixel values of an image to the range [0, 1].
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def normalize(image, label):
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return image / 255, label
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# Set the paths to the folder containing the training data
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train_data_dir = "Network/Training/"
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# Set the number of classes and batch size
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num_classes = 3
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batch_size = 32
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# Set the image size and input shape
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img_width, img_height = 100, 100
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input_shape = (img_width, img_height, 1)
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# Load the training and validation data
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train_ds = tf.keras.utils.image_dataset_from_directory(
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train_data_dir,
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validation_split=0.2,
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subset="training",
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shuffle=True,
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seed=123,
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image_size=(img_height, img_width),
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batch_size=batch_size)
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val_ds = tf.keras.utils.image_dataset_from_directory(
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train_data_dir,
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validation_split=0.2,
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subset="validation",
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shuffle=True,
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seed=123,
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image_size=(img_height, img_width),
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batch_size=batch_size)
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# Get the class names
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class_names = train_ds.class_names
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print(class_names)
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# Normalize the training and validation data
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train_ds = train_ds.map(normalize)
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val_ds = val_ds.map(normalize)
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# Define the model architecture
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model = tf.keras.Sequential([
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layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(img_height, img_width, 1)),
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layers.MaxPooling2D(),
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layers.Conv2D(32, 3, padding='same', activation='relu'),
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layers.MaxPooling2D(),
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layers.Conv2D(64, 3, padding='same', activation='relu'),
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layers.MaxPooling2D(),
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layers.Flatten(),
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layers.Dense(128, activation='relu'),
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layers.Dense(num_classes, activation='softmax')
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])
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# Compile the model
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model.compile(optimizer='adam',
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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metrics=['accuracy'])
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# Print the model summary
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model.summary()
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# Train the model
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epochs=10
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model.fit(train_ds,
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validation_data=val_ds,
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epochs=epochs)
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# Save the trained model
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model.save('Network/trained_model.h5') |