CV之IC之AlexNet：基于tensorflow框架采用CNN卷积神经网络算法(改进的AlexNet,训练/评估/推理)实现猫狗分类识别案例应用
2024-05-16 13:53:28
CV之IC之AlexNet：基于tensorflow框架采用CNN卷积神经网络算法(改进的AlexNet,训练/评估/推理)实现猫狗分类识别案例应用
目录
基于tensorflow框架采用CNN(改进的AlexNet,训练/评估/推理)卷积神经网络算法实现猫狗图像分类识别
数据集介绍
输出结果
使用model.ckpt-6000模型预测
预测错误的只有一个案例，如下所示
训练结果
核心代码
基于tensorflow框架采用CNN(改进的AlexNet,训练/评估/推理)卷积神经网络算法实现猫狗图像分类识别
数据集介绍
数据下载：Dogs vs. Cats Redux: Kernels Edition | Kaggle
train文件夹里有25000张狗和猫的图片。这个文件夹中的每个图像都有标签作为文件名的一部分。测试文件夹包含12500张图片，根据数字id命名。对于测试集中的每个图像，您应该预测图像是一只狗的概率(1 =狗，0 =猫)。
输出结果
使用model.ckpt-6000模型预测
预测错误的只有一个案例，如下所示
| 序号 | 使用model.ckpt-4000模型预测 | 使用model.ckpt-6000模型预测 | 使用model.ckpt-8000模型预测 | 使用model.ckpt-10000模型预测 | 使用model.ckpt-12000模型预测 | |
| 1 | cat | cat (1).jpg 猫的概率 0.631 | cat (1).jpg 狗的概率 0.740 | cat (1).jpg 狗的概率 0.781 | cat (1).jpg 狗的概率 0.976 | cat (1).jpg 狗的概率 0.991 |
| 2 | cat (10).jpg 狗的概率 0.697 | cat (10).jpg 猫的概率 0.566 | cat (10).jpg 猫的概率 0.925 | cat (10).jpg 猫的概率 0.925 | cat (10).jpg 猫的概率 0.816 | |
| 3 | cat (11).jpg 猫的概率 0.927 | cat (11).jpg 猫的概率 0.988 | cat (11).jpg 猫的概率 1.000 | cat (11).jpg 猫的概率 1.000 | cat (11).jpg 猫的概率 1.000 | |
| 4 | cat (12).jpg 狗的概率 0.746 | cat (12).jpg 狗的概率 0.723 | cat (12).jpg 狗的概率 0.822 | cat (12).jpg 狗的概率 0.998 | cat (12).jpg 狗的概率 1.000 | |
| 5 | cat (13).jpg 猫的概率 0.933 | cat (13).jpg 猫的概率 0.983 | cat (13).jpg 猫的概率 0.997 | cat (13).jpg 猫的概率 1.000 | cat (13).jpg 猫的概率 1.000 | |
| 6 | cat (14).jpg 狗的概率 0.657 | cat (14).jpg 猫的概率 0.597 | cat (14).jpg 狗的概率 0.758 | cat (14).jpg 狗的概率 0.695 | cat (14).jpg 猫的概率 0.544 | |
| 7 | cat (15).jpg 狗的概率 0.578 | cat (15).jpg 狗的概率 0.535 | cat (15).jpg 狗的概率 0.526 | cat (15).jpg 狗的概率 0.750 | cat (15).jpg 狗的概率 0.569 | |
| 8 | cat (2).jpg 猫的概率 0.649 | cat (2).jpg 猫的概率 0.637 | cat (2).jpg 猫的概率 0.844 | cat (2).jpg 猫的概率 0.996 | cat (2).jpg 猫的概率 0.998 | |
| 9 | cat (3).jpg 狗的概率 0.668 | cat (3).jpg 猫的概率 0.538 | cat (3).jpg 猫的概率 0.710 | cat (3).jpg 猫的概率 0.968 | cat (3).jpg 猫的概率 0.995 | |
| 10 | cat (4).jpg 狗的概率 0.856 | cat (4).jpg 狗的概率 0.780 | cat (4).jpg 狗的概率 0.831 | cat (4).jpg 狗的概率 0.974 | cat (4).jpg 狗的概率 0.976 | |
| 11 | cat (5).jpg 猫的概率 0.812 | cat (5).jpg 猫的概率 0.776 | cat (5).jpg 猫的概率 0.505 | cat (5).jpg 猫的概率 0.732 | cat (5).jpg 狗的概率 0.608 | |
| 12 | cat (6).jpg 猫的概率 0.524 | cat (6).jpg 狗的概率 0.661 | cat (6).jpg 狗的概率 0.748 | cat (6).jpg 狗的概率 0.970 | cat (6).jpg 狗的概率 0.987 | |
| 13 | cat (7).jpg 狗的概率 0.612 | cat (7).jpg 猫的概率 0.845 | cat (7).jpg 猫的概率 0.894 | cat (7).jpg 猫的概率 0.987 | cat (7).jpg 猫的概率 0.728 | |
| 14 | cat (8).jpg 狗的概率 0.823 | cat (8).jpg 狗的概率 0.948 | cat (8).jpg 狗的概率 0.920 | cat (8).jpg 狗的概率 0.982 | cat (8).jpg 狗的概率 0.999 | |
| 15 | cat (9).jpg 猫的概率 0.697 | cat (9).jpg 猫的概率 0.704 | cat (9).jpg 狗的概率 0.819 | cat (9).jpg 猫的概率 0.930 | cat (9).jpg 狗的概率 0.718 | |
| 16 | dog | dog (1).jpg 狗的概率 0.987 | dog (1).jpg 狗的概率 0.995 | dog (1).jpg 狗的概率 0.999 | dog (1).jpg 狗的概率 1.000 | dog (1).jpg 狗的概率 1.000 |
| 17 | dog (10).jpg 狗的概率 0.628 | dog (10).jpg 猫的概率 0.629 | dog (10).jpg 猫的概率 0.994 | dog (10).jpg 猫的概率 1.000 | dog (10).jpg 猫的概率 1.000 | |
| 18 | dog (11).jpg 狗的概率 0.804 | dog (11).jpg 狗的概率 0.879 | dog (11).jpg 狗的概率 0.993 | dog (11).jpg 狗的概率 1.000 | dog (11).jpg 狗的概率 1.000 | |
| 19 | dog (12).jpg 猫的概率 0.704 | dog (12).jpg 猫的概率 0.758 | dog (12).jpg 狗的概率 0.503 | dog (12).jpg 狗的概率 0.653 | dog (12).jpg 猫的概率 0.985 | |
| 20 | dog (13).jpg 狗的概率 0.987 | dog (13).jpg 狗的概率 0.997 | dog (13).jpg 狗的概率 1.000 | dog (13).jpg 狗的概率 1.000 | dog (13).jpg 狗的概率 1.000 | |
| 21 | dog (14).jpg 狗的概率 0.815 | dog (14).jpg 狗的概率 0.844 | dog (14).jpg 狗的概率 0.904 | dog (14).jpg 狗的概率 0.996 | dog (14).jpg 狗的概率 0.950 | |
| 22 | dog (15).jpg 狗的概率 0.917 | dog (15).jpg 狗的概率 0.984 | dog (15).jpg 狗的概率 0.999 | dog (15).jpg 狗的概率 1.000 | dog (15).jpg 狗的概率 1.000 | |
| 23 | dog (16).jpg 狗的概率 0.883 | dog (16).jpg 狗的概率 0.931 | dog (16).jpg 狗的概率 0.830 | dog (16).jpg 狗的概率 0.975 | dog (16).jpg 狗的概率 0.983 | |
| 24 | dog (2).jpg 狗的概率 0.934 | dog (2).jpg 狗的概率 0.982 | dog (2).jpg 狗的概率 0.998 | dog (2).jpg 狗的概率 1.000 | dog (2).jpg 狗的概率 1.000 | |
| 25 | dog (3).jpg 狗的概率 0.993 | dog (3).jpg 狗的概率 1.000 | dog (3).jpg 狗的概率 1.000 | dog (3).jpg 狗的概率 1.000 | dog (3).jpg 狗的概率 1.000 | |
| 26 | dog (4).jpg 狗的概率 0.693 | dog (4).jpg 狗的概率 0.754 | dog (4).jpg 狗的概率 0.976 | dog (4).jpg 狗的概率 0.515 | dog (4).jpg 狗的概率 0.995 | |
| 27 | dog (5).jpg 狗的概率 0.916 | dog (5).jpg 狗的概率 0.976 | dog (5).jpg 狗的概率 0.993 | dog (5).jpg 狗的概率 0.998 | dog (5).jpg 狗的概率 1.000 | |
| 28 | dog (6).jpg 狗的概率 0.947 | dog (6).jpg 狗的概率 0.989 | dog (6).jpg 狗的概率 0.999 | dog (6).jpg 狗的概率 1.000 | dog (6).jpg 狗的概率 1.000 | |
| 29 | dog (7).jpg 猫的概率 0.526 | dog (7).jpg 猫的概率 0.685 | dog (7).jpg 猫的概率 0.961 | dog (7).jpg 猫的概率 1.000 | dog (7).jpg 猫的概率 1.000 | |
| 30 | dog (8).jpg 狗的概率 0.981 | dog (8).jpg 狗的概率 0.998 | dog (8).jpg 狗的概率 1.000 | dog (8).jpg 狗的概率 1.000 | dog (8).jpg 狗的概率 1.000 | |
| 31 | dog (9).jpg 狗的概率 0.899 | dog (9).jpg 狗的概率 0.983 | dog (9).jpg 狗的概率 0.999 | dog (9).jpg 狗的概率 1.000 | dog (9).jpg 狗的概率 1.000 |
训练结果
Step 0, train loss = 0.69, train accuracy = 78.12%
Step 50, train loss = 0.69, train accuracy = 43.75%
Step 100, train loss = 0.70, train accuracy = 46.88%
Step 150, train loss = 0.65, train accuracy = 75.00%
Step 200, train loss = 0.66, train accuracy = 59.38%
Step 250, train loss = 0.66, train accuracy = 62.50%
Step 300, train loss = 0.72, train accuracy = 40.62%
Step 350, train loss = 0.66, train accuracy = 62.50%
Step 400, train loss = 0.58, train accuracy = 68.75%
Step 450, train loss = 0.70, train accuracy = 65.62%
Step 500, train loss = 0.68, train accuracy = 56.25%
Step 550, train loss = 0.51, train accuracy = 81.25%
Step 600, train loss = 0.54, train accuracy = 75.00%
Step 650, train loss = 0.64, train accuracy = 68.75%
Step 700, train loss = 0.69, train accuracy = 53.12%
Step 750, train loss = 0.57, train accuracy = 71.88%
Step 800, train loss = 0.80, train accuracy = 50.00%
Step 850, train loss = 0.62, train accuracy = 59.38%
Step 900, train loss = 0.59, train accuracy = 65.62%
Step 950, train loss = 0.54, train accuracy = 71.88%
Step 1000, train loss = 0.57, train accuracy = 68.75%
Step 1050, train loss = 0.56, train accuracy = 78.12%
Step 1100, train loss = 0.66, train accuracy = 59.38%
Step 1150, train loss = 0.50, train accuracy = 84.38%
Step 1200, train loss = 0.46, train accuracy = 81.25%
Step 1250, train loss = 0.57, train accuracy = 59.38%
Step 1300, train loss = 0.37, train accuracy = 81.25%
Step 1350, train loss = 0.64, train accuracy = 62.50%
Step 1400, train loss = 0.44, train accuracy = 81.25%
Step 1450, train loss = 0.46, train accuracy = 84.38%
Step 1500, train loss = 0.50, train accuracy = 71.88%
Step 1550, train loss = 0.58, train accuracy = 62.50%
Step 1600, train loss = 0.43, train accuracy = 75.00%
Step 1650, train loss = 0.55, train accuracy = 71.88%
Step 1700, train loss = 0.50, train accuracy = 71.88%
Step 1750, train loss = 0.46, train accuracy = 75.00%
Step 1800, train loss = 0.81, train accuracy = 53.12%
Step 1850, train loss = 0.41, train accuracy = 90.62%
Step 1900, train loss = 0.65, train accuracy = 68.75%
Step 1950, train loss = 0.37, train accuracy = 84.38%
Step 2000, train loss = 0.39, train accuracy = 81.25%
Step 2050, train loss = 0.45, train accuracy = 84.38%
Step 2100, train loss = 0.44, train accuracy = 78.12%
Step 2150, train loss = 0.59, train accuracy = 65.62%
Step 2200, train loss = 0.51, train accuracy = 78.12%
Step 2250, train loss = 0.42, train accuracy = 81.25%
Step 2300, train loss = 0.32, train accuracy = 87.50%
Step 2350, train loss = 0.48, train accuracy = 75.00%
Step 2400, train loss = 0.54, train accuracy = 71.88%
Step 2450, train loss = 0.51, train accuracy = 71.88%
Step 2500, train loss = 0.73, train accuracy = 59.38%
Step 2550, train loss = 0.52, train accuracy = 78.12%
Step 2600, train loss = 0.65, train accuracy = 62.50%
Step 2650, train loss = 0.52, train accuracy = 71.88%
Step 2700, train loss = 0.48, train accuracy = 71.88%
Step 2750, train loss = 0.37, train accuracy = 84.38%
Step 2800, train loss = 0.46, train accuracy = 78.12%
Step 2850, train loss = 0.40, train accuracy = 84.38%
Step 2900, train loss = 0.45, train accuracy = 81.25%
Step 2950, train loss = 0.36, train accuracy = 84.38%
Step 3000, train loss = 0.46, train accuracy = 75.00%
Step 3050, train loss = 0.53, train accuracy = 71.88%
Step 3100, train loss = 0.37, train accuracy = 84.38%
Step 3150, train loss = 0.53, train accuracy = 75.00%
Step 3200, train loss = 0.52, train accuracy = 75.00%
Step 3250, train loss = 0.62, train accuracy = 65.62%
Step 3300, train loss = 0.58, train accuracy = 71.88%
Step 3350, train loss = 0.71, train accuracy = 65.62%
Step 3400, train loss = 0.43, train accuracy = 78.12%
Step 3450, train loss = 0.46, train accuracy = 78.12%
Step 3500, train loss = 0.46, train accuracy = 71.88%
Step 3550, train loss = 0.53, train accuracy = 68.75%
Step 3600, train loss = 0.44, train accuracy = 75.00%
Step 3650, train loss = 0.55, train accuracy = 65.62%
Step 3700, train loss = 0.62, train accuracy = 75.00%
Step 3750, train loss = 0.48, train accuracy = 75.00%
Step 3800, train loss = 0.66, train accuracy = 53.12%
Step 3850, train loss = 0.53, train accuracy = 75.00%
Step 3900, train loss = 0.36, train accuracy = 81.25%
Step 3950, train loss = 0.37, train accuracy = 87.50%
Step 4000, train loss = 0.46, train accuracy = 78.12%
Step 4050, train loss = 0.36, train accuracy = 84.38%
Step 4100, train loss = 0.34, train accuracy = 78.12%
Step 4150, train loss = 0.48, train accuracy = 78.12%
Step 4200, train loss = 0.43, train accuracy = 87.50%
Step 4250, train loss = 0.34, train accuracy = 84.38%
Step 4300, train loss = 0.28, train accuracy = 87.50%
Step 4350, train loss = 0.19, train accuracy = 96.88%
Step 4400, train loss = 0.46, train accuracy = 71.88%
Step 4450, train loss = 0.33, train accuracy = 84.38%
Step 4500, train loss = 0.55, train accuracy = 75.00%
Step 4550, train loss = 0.31, train accuracy = 93.75%
Step 4600, train loss = 0.30, train accuracy = 84.38%
Step 4650, train loss = 0.38, train accuracy = 84.38%
Step 4700, train loss = 0.36, train accuracy = 84.38%
Step 4750, train loss = 0.32, train accuracy = 87.50%
Step 4800, train loss = 0.36, train accuracy = 81.25%
Step 4850, train loss = 0.36, train accuracy = 87.50%
Step 4900, train loss = 0.49, train accuracy = 71.88%
Step 4950, train loss = 0.51, train accuracy = 68.75%
Step 5000, train loss = 0.59, train accuracy = 68.75%
Step 5050, train loss = 0.55, train accuracy = 75.00%
Step 5100, train loss = 0.71, train accuracy = 68.75%
Step 5150, train loss = 0.48, train accuracy = 71.88%
Step 5200, train loss = 0.39, train accuracy = 90.62%
Step 5250, train loss = 0.49, train accuracy = 81.25%
Step 5300, train loss = 0.36, train accuracy = 81.25%
Step 5350, train loss = 0.31, train accuracy = 90.62%
Step 5400, train loss = 0.39, train accuracy = 87.50%
Step 5450, train loss = 0.34, train accuracy = 78.12%
Step 5500, train loss = 0.29, train accuracy = 84.38%
Step 5550, train loss = 0.21, train accuracy = 93.75%
Step 5600, train loss = 0.41, train accuracy = 78.12%
Step 5650, train loss = 0.38, train accuracy = 84.38%
Step 5700, train loss = 0.27, train accuracy = 87.50%
Step 5750, train loss = 0.24, train accuracy = 90.62%
Step 5800, train loss = 0.17, train accuracy = 96.88%
Step 5850, train loss = 0.23, train accuracy = 93.75%
Step 5900, train loss = 0.37, train accuracy = 71.88%
Step 5950, train loss = 0.49, train accuracy = 71.88%
Step 6000, train loss = 0.43, train accuracy = 81.25%
Step 6050, train loss = 0.33, train accuracy = 87.50%
Step 6100, train loss = 0.46, train accuracy = 75.00%
Step 6150, train loss = 0.61, train accuracy = 81.25%
Step 6200, train loss = 0.34, train accuracy = 84.38%
Step 6250, train loss = 0.63, train accuracy = 71.88%
Step 6300, train loss = 0.21, train accuracy = 90.62%
Step 6350, train loss = 0.21, train accuracy = 90.62%
Step 6400, train loss = 0.27, train accuracy = 87.50%
Step 6450, train loss = 0.17, train accuracy = 87.50%
Step 6500, train loss = 0.34, train accuracy = 87.50%
Step 6550, train loss = 0.34, train accuracy = 87.50%
Step 6600, train loss = 0.32, train accuracy = 84.38%
Step 6650, train loss = 0.39, train accuracy = 84.38%
Step 6700, train loss = 0.38, train accuracy = 84.38%
Step 6750, train loss = 0.41, train accuracy = 84.38%
Step 6800, train loss = 0.49, train accuracy = 81.25%
Step 6850, train loss = 0.36, train accuracy = 84.38%
Step 6900, train loss = 0.20, train accuracy = 93.75%
Step 6950, train loss = 0.13, train accuracy = 93.75%
Step 7000, train loss = 0.31, train accuracy = 87.50%
Step 7050, train loss = 0.18, train accuracy = 93.75%
Step 7100, train loss = 0.23, train accuracy = 90.62%
Step 7150, train loss = 0.13, train accuracy = 96.88%
Step 7200, train loss = 0.14, train accuracy = 96.88%
Step 7250, train loss = 0.32, train accuracy = 84.38%
Step 7300, train loss = 0.18, train accuracy = 93.75%
Step 7350, train loss = 0.14, train accuracy = 100.00%
Step 7400, train loss = 0.60, train accuracy = 75.00%
Step 7450, train loss = 0.20, train accuracy = 93.75%
Step 7500, train loss = 0.13, train accuracy = 93.75%
Step 7550, train loss = 0.22, train accuracy = 90.62%
Step 7600, train loss = 0.13, train accuracy = 96.88%
Step 7650, train loss = 0.20, train accuracy = 93.75%
Step 7700, train loss = 0.24, train accuracy = 90.62%
Step 7750, train loss = 0.19, train accuracy = 93.75%
Step 7800, train loss = 0.16, train accuracy = 93.75%
Step 7850, train loss = 0.08, train accuracy = 100.00%
Step 7900, train loss = 0.10, train accuracy = 96.88%
Step 7950, train loss = 0.13, train accuracy = 93.75%
Step 8000, train loss = 0.18, train accuracy = 90.62%
Step 8050, train loss = 0.27, train accuracy = 93.75%
Step 8100, train loss = 0.04, train accuracy = 100.00%
Step 8150, train loss = 0.27, train accuracy = 87.50%
Step 8200, train loss = 0.06, train accuracy = 96.88%
Step 8250, train loss = 0.12, train accuracy = 100.00%
Step 8300, train loss = 0.28, train accuracy = 87.50%
Step 8350, train loss = 0.24, train accuracy = 90.62%
Step 8400, train loss = 0.16, train accuracy = 93.75%
Step 8450, train loss = 0.11, train accuracy = 93.75%
Step 8500, train loss = 0.13, train accuracy = 96.88%
Step 8550, train loss = 0.05, train accuracy = 100.00%
Step 8600, train loss = 0.10, train accuracy = 93.75%
Step 8650, train loss = 0.14, train accuracy = 100.00%
Step 8700, train loss = 0.21, train accuracy = 90.62%
Step 8750, train loss = 0.09, train accuracy = 96.88%
Step 8800, train loss = 0.11, train accuracy = 96.88%
Step 8850, train loss = 0.10, train accuracy = 96.88%
Step 8900, train loss = 0.12, train accuracy = 93.75%
Step 8950, train loss = 0.48, train accuracy = 81.25%
Step 9000, train loss = 0.07, train accuracy = 100.00%
Step 9050, train loss = 0.03, train accuracy = 100.00%
Step 9100, train loss = 0.10, train accuracy = 93.75%
Step 9150, train loss = 0.05, train accuracy = 96.88%
Step 9200, train loss = 0.04, train accuracy = 100.00%
Step 9250, train loss = 0.03, train accuracy = 100.00%
Step 9300, train loss = 0.04, train accuracy = 96.88%
Step 9350, train loss = 0.08, train accuracy = 100.00%
Step 9400, train loss = 0.05, train accuracy = 100.00%
Step 9450, train loss = 0.15, train accuracy = 90.62%
Step 9500, train loss = 0.03, train accuracy = 100.00%
Step 9550, train loss = 0.05, train accuracy = 100.00%
Step 9600, train loss = 0.15, train accuracy = 96.88%
Step 9650, train loss = 0.03, train accuracy = 100.00%
Step 9700, train loss = 0.02, train accuracy = 100.00%
Step 9750, train loss = 0.08, train accuracy = 96.88%
Step 9800, train loss = 0.04, train accuracy = 100.00%
Step 9850, train loss = 0.06, train accuracy = 96.88%
Step 9900, train loss = 0.03, train accuracy = 100.00%
Step 9950, train loss = 0.03, train accuracy = 100.00%
Step 10000, train loss = 0.11, train accuracy = 93.75%
Step 10050, train loss = 0.02, train accuracy = 100.00%
Step 10100, train loss = 0.01, train accuracy = 100.00%
Step 10150, train loss = 0.05, train accuracy = 96.88%
Step 10200, train loss = 0.07, train accuracy = 96.88%
Step 10250, train loss = 0.06, train accuracy = 96.88%
Step 10300, train loss = 0.03, train accuracy = 100.00%
Step 10350, train loss = 0.08, train accuracy = 96.88%
Step 10400, train loss = 0.05, train accuracy = 96.88%
Step 10450, train loss = 0.02, train accuracy = 100.00%
Step 10500, train loss = 0.22, train accuracy = 93.75%
Step 10550, train loss = 0.06, train accuracy = 100.00%
Step 10600, train loss = 0.02, train accuracy = 100.00%
Step 10650, train loss = 0.02, train accuracy = 100.00%
Step 10700, train loss = 0.03, train accuracy = 100.00%
Step 10750, train loss = 0.15, train accuracy = 96.88%
Step 10800, train loss = 0.05, train accuracy = 100.00%
Step 10850, train loss = 0.02, train accuracy = 100.00%
Step 10900, train loss = 0.04, train accuracy = 96.88%
Step 10950, train loss = 0.05, train accuracy = 96.88%
Step 11000, train loss = 0.02, train accuracy = 100.00%
Step 11050, train loss = 0.10, train accuracy = 96.88%
Step 11100, train loss = 0.08, train accuracy = 96.88%
Step 11150, train loss = 0.02, train accuracy = 100.00%
Step 11200, train loss = 0.01, train accuracy = 100.00%
Step 11250, train loss = 0.06, train accuracy = 96.88%
Step 11300, train loss = 0.18, train accuracy = 93.75%
Step 11350, train loss = 0.02, train accuracy = 100.00%
Step 11400, train loss = 0.04, train accuracy = 100.00%
Step 11450, train loss = 0.03, train accuracy = 100.00%
Step 11500, train loss = 0.01, train accuracy = 100.00%
Step 11550, train loss = 0.02, train accuracy = 100.00%核心代码
weights = tf.get_variable('weights', shape=[3, 3, 3, 16], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32)) biases = tf.get_variable('biases', shape=[16], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME') pre_activation = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(pre_activation, name=scope.name) with tf.variable_scope('pooling1_lrn') as scope: pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pooling1') norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1') with tf.variable_scope('conv2') as scope: weights = tf.get_variable('weights', shape=[3, 3, 16, 16], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.1, dtype=tf.float32)) biases = tf.get_variable('biases', shape=[16], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME') pre_activation = tf.nn.bias_add(conv, biases) conv2 = tf.nn.relu(pre_activation, name='conv2') with tf.variable_scope('pooling2_lrn') as scope: norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2') pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='pooling2') with tf.variable_scope('local3') as scope: reshape = tf.reshape(pool2, shape=[batch_size, -1]) dim = reshape.get_shape()[1].value weights = tf.get_variable('weights', shape=[dim, 128], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32)) biases = tf.get_variable('biases', shape=[128], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) # local4 with tf.variable_scope('local4') as scope: weights = tf.get_variable('weights', shape=[128, 128], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32)) biases = tf.get_variable('biases', shape=[128], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4') with tf.variable_scope('softmax_linear') as scope: weights = tf.get_variable('softmax_linear', shape=[128, n_classes], dtype=tf.float32, initializer=tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32)) biases = tf.get_variable('biases', shape=[n_classes], dtype=tf.float32, initializer=tf.constant_initializer(0.1)) softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear') CV之IC之AlexNet：基于tensorflow框架采用CNN卷积神经网络算法(改进的AlexNet,训练/评估/推理)实现猫狗分类识别案例应用相关推荐
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