65 lines
1.6 KiB
Python
65 lines
1.6 KiB
Python
from sklearn.neural_network import MLPClassifier
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from sklearn.metrics import accuracy_score
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from PIL import Image
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import numpy as np
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import os
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number_file_count = []
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max_num = 0
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print('开始计算训练数据个数')
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for i in range(10):
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for filename in os.listdir('CNN_LeNet_test'):
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if filename[0] == str(i):
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num_str = filename.split('_')[-1].split('.')[0]
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num = int(num_str)
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max_num = max(max_num, num)
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number_file_count.append(max_num)
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max_num = 0
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print('计算完毕')
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print(number_file_count)
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print()
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print('开始加载训练数据')
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# 加载训练数据
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X_train, y_train = [], []
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for i in range(10):
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for j in range(number_file_count[i]):
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img_path = f'CNN_LeNet_test/{i}_{j}.bmp'
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img = np.array(Image.open(img_path).convert('L')) / 255.0
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X_train.append(img.flatten())
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y_train.append(i)
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print('加载完毕')
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print()
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print('开始训练模型')
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# 构建模型并进行训练
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model = MLPClassifier(hidden_layer_sizes=(100,), max_iter=500)
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model.fit(X_train, y_train)
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print('训练完毕')
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print()
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print('开始测试模型')
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# 对测试数据进行预测
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test_dir = 'CNN_LeNet_test/test_image'
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y_true, y_pred = [], []
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for filename in os.listdir(test_dir):
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if filename.endswith('.bmp'):
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img_path = os.path.join(test_dir, filename)
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img = np.array(Image.open(img_path).convert('L')) / 255.0
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X_test = img.flatten().reshape(1, -1)
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y_pred.append(model.predict(X_test)[0])
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y_true.append(int(filename[0]))
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# 计算识别准确率
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accuracy = accuracy_score(y_true, y_pred)
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print(f'识别准确率为：{accuracy:.2f}')
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