31 lines
842 B
Python
31 lines
842 B
Python
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from m import *
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from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
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ds = tf.keras.preprocessing.image_dataset_from_directory(
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'../dataset-orig-aug-1-mini-1/',
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labels = 'inferred',
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label_mode = 'categorical',
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color_mode = 'rgb',
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image_size = (300, 300),
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batch_size = 32,
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verbose = True
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)
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p = []
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r = []
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with open('results.txt', 'r') as f:
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for i in f.read().split('\n'):
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if i == '':
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continue
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res = tuple(map(float, i.split()))
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p.append([res[0] > 0.5, res[1] > 0.5])
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r.append([res[2], res[3]])
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print(f"Accuracy : {accuracy_score(p, r)}")
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print(f"Precision : {precision_score(p, r, average = 'micro')}")
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print(f"Recall : {recall_score(p, r, average = 'micro')}")
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print(f"F1 Score : {f1_score(p, r, average = 'micro')}")
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