深度学习笔记(4.numpy实现DNN识别猫实验)

傷城~ 2023-08-17 17:42 48阅读 0赞

### 前言 ###

重新又实现了一遍DNN，调整代码结构向课程提供的课件看齐，还是稍有不同，主要为了自己容易理解。完成课程作业——识别猫，实验程序较简单，主要是根据课件给的做数据预处理，直接调用dnn训练即可。  
程序学习地址（一个同学学习小组，自己太没意思了）：[https://github.com/ConstellationBJUT/Coursera-DL-Study-Notes][https_github.com_ConstellationBJUT_Coursera-DL-Study-Notes]

### 数据预处理 ###

这部分代码直接copy的课件。  
主要是数据预处理，读取图片数据->resize图片->每个图片变换为一列作为输入特征->标准化->送入DNN  
数据变换过程如下：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2Jqam95MjAwOQ_size_16_color_FFFFFF_t_70]

### 程序涉及文件 ###

这次作业涉及到的程序文件：week4\_dnn文件夹下  
dnn\_v3\_cat\_classfier.py  
dnn\_v2.py  
datasets文件夹  
images文件夹

### 结果 ###

测试图片  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2Jqam95MjAwOQ_size_16_color_FFFFFF_t_70 1]  
模型训练准确率：  
train: 0.9856459330143541  
test: 0.8  
y = 1.0, your L-layer model predicts a “cat” picture.

### 主要代码 ###

""" @Time : 2019/10/1 19:47 PM @Author : bjjoy2009 判断图片是否是猫的实验，week4作业2，使用课程提供数据 结果与课程提供notebook一样 """
    import h5py
    import numpy as np
    from PIL import Image
    
    from week4_dnn.dnn_v2 import DNN
    
    
    def load_data():
        train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r")
        train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
        train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
    
        test_dataset = h5py.File('datasets/test_catvnoncat.h5', "r")
        test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
        test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
    
        classes = np.array(test_dataset["list_classes"][:]) # the list of classes
    
        train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
        test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
    
        return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
    
    
    def is_cat(my_image, nn, num_px, classes, is_show=False):
        image = Image.open(my_image)
        x = np.array(image.resize((num_px, num_px)))
        x = x.reshape(num_px*num_px*3, -1)
        x = x/255
        my_predicted_image = nn.score(x, np.array([1]))
        print("y = " + str(np.squeeze(my_predicted_image)) + ', your L-layer model predicts a \"' + classes[int(np.squeeze(my_predicted_image)),].decode("utf-8") +  "\" picture.")
        if is_show:
            image.show()
    
    
    if __name__ == '__main__':
        np.random.seed(1)
        train_x_orig, train_y, test_x_orig, test_y, classes = load_data()
        # Reshape the training and test examples
        train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T
        test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T
        # Standardize data to have feature values between 0 and 1.
        train_x = train_x_flatten/255.
        test_x = test_x_flatten/255.
        layers_dims = [12288, 20, 7, 5, 1]
        nn = DNN(X=train_x, Y=train_y, layer_dims=layers_dims, max_iter=2500, alpha=0.0075, print_loss=True, activation='relu')
        nn.fit()
        accuracy = nn.score(train_x, train_y)
        print('train:', accuracy)
        print('test:', nn.score(test_x, test_y))
    
        is_cat('images/my_image.jpg', nn, 64, classes, is_show=True)

[https_github.com_ConstellationBJUT_Coursera-DL-Study-Notes]: https://github.com/ConstellationBJUT/Coursera-DL-Study-Notes
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2Jqam95MjAwOQ_size_16_color_FFFFFF_t_70 1]: /images/20230808/fc821af10c794a49826d1f855564f64f.png