2024-05-19 22:06:27 +00:00
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import numpy as np
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import tensorflow as tf
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import os
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#Import Data
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PATH = os.path.join(os.getcwd(), "crop-data")
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test_data = os.path.join(PATH, "test")
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test_data_32 = tf.keras.utils.image_dataset_from_directory(test_data,
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shuffle=True,
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batch_size=32,
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image_size=(160,160))
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test_data_64 = tf.keras.utils.image_dataset_from_directory(test_data,
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shuffle=True,
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batch_size=64,
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image_size=(160,160))
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AUTOTUNE = tf.data.AUTOTUNE
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test_data_32 = test_data_32.prefetch(buffer_size=AUTOTUNE)
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test_data_64 = test_data_64.prefetch(buffer_size=AUTOTUNE)
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test_data_32 = test_data_32.map(lambda x,y: (x, tf.one_hot(y,38)))
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test_data_64 = test_data_64.map(lambda x,y: (x, tf.one_hot(y,38)))
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model1 = tf.keras.models.load_model("crop-classifier.keras")
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model2 = tf.keras.models.load_model("crop-classifier-64-batch.keras")
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model3 = tf.keras.models.load_model("crop-classifier-callbacks.keras")
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result = model1.evaluate(test_data_32)
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print(f"Test Loss: {result[0]}")
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print(f"Test Accuracy: {result[1]}")
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result = model2.evaluate(test_data_64)
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print(f"Test Loss: {result[0]}")
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print(f"Test Accuracy: {result[1]}")
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result = model3.evaluate(test_data_64)
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print(f"Test Loss: {result[0]}")
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print(f"Test Accuracy: {result[1]}")
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