60 lines
1.4 KiB
Python
60 lines
1.4 KiB
Python
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from test2 import *
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import tensorflow as tf
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from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score
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from matplotlib import pyplot as plt
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import numpy as np
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td = tf.keras.utils.image_dataset_from_directory(
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"../dataset-ultimate-2",
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image_size = (300, 300),
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labels = "inferred",
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label_mode = "binary",
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batch_size = 96)
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m.load_weights("model3.keras")
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p = [i >= 0 for i in m.predict(td)]
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r = np.concatenate([y for x, y in td])
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#print(r)
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#exit()
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def __prep_conf_matr(data):
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output_matrix = np.zeros([2, 2])
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for p, r in zip(*data):
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#print(p, r)
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output_matrix[int(p)][int(r[0])] += 1
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return output_matrix
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def __plot_conf_matr(data):
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matr = __prep_conf_matr(data)
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_, ax = plt.subplots()
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ax.matshow(matr, cmap = plt.cm.Blues)
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for i, x in enumerate(matr):
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for j, y in enumerate(x):
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ax.text(i,
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j,
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str(round(y)),
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va = "center",
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ha = "center")
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plt.show()
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#p = mod.predict(ds)
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#r = np.concatenate([y for x, y in ds])
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print(f"Accuracy : {accuracy_score(p, r)}")
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print(f"Precision : {precision_score(p, r, average = 'binary')}")
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print(f"Recall : {recall_score(p, r, average = 'binary')}")
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print(f"F1 Score : {f1_score(p, r, average = 'binary')}")
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__plot_conf_matr([p, r])
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