寻找最小误差（loss） tensorflow

亦凉 2023-10-02 12:48 40阅读 0赞

import os
    import cv2
    import numpy as np
    import tensorflow as tf
    
    h_pool2_flat = 12288
    n_classes = 1
    # 设置路径
    data_home = 'data/imgdata/'
    
    # 标签映射字典
    image_to_label = {}
    with open('data/label.txt', 'r') as fr:
        lines = fr.readlines()
        for line in lines:
            line = line.strip()
            key, value = line.split(' ')
            image_to_label[key] = value
    
    # 构造训练集
    X_train = []
    y_train = []
    for jpg in os.listdir(data_home):
        image = cv2.imread(data_home + jpg)
        x = cv2.resize(image, (64, 64))
        X_train.append(x)
        y = image_to_label[jpg]
        y_train.append(y)
    
    X_train = np.array(X_train).reshape((1000, -1))
    y_train = np.array(y_train).reshape((-1, 1))
    
    
    # print(X_train.shape)
    # print(y_train)
    
    def get_biases(shape):
        '''
        函数的偏置定义。tf.get_variable(name,shape，dtype，initializer)
        参数
            1.name:“biases”
            2.shape：[size]，即[输出维度]
            3.dtype:tf.float32
            4.initializer：初始化方式initial，我们选择常量初始化
                语法：tf.constant_initializer(value)
                参数：
                1）value:常数。0.1
    
        :param shape: 偏置输出维度
        :return: 偏置变量
        '''
        # TODO：1.使用tf.constant_initializer(0.1)函数定义初始化方式
        initial = tf.constant_initializer(0.1)
        # TODO：2.使用tf.get_variable函数定义偏置变量
        biases = tf.get_variable("biases",
                                 shape=shape,
                                 dtype=tf.float32,
                                 initializer=initial)
        return biases
    
    
    def get_weights(shape):
        '''
        定义参数，卷积核/权值
        语法：tf.get_variable(name,shape，dtype，initializer)
        参数：
            1.name:“weights”
            2.shape：根据不同的变量，输入不同的shape
            3.dtype:tf.float32
            4.initializer：初始化方式initial，我们选择初始化方式为截断的正太分布
                语法：tf.truncated_normal_initializer(stddev,dtype)
                参数：
                    1）stedev:要生成的随机值的标准偏差。可设置stedev=0.005
                    2) dtype：数据类型。只支持浮点类型 可设置dtype=tf.float32。
    
        :param shape:参数shape
        :return: 变量参数
        '''
        # TODO：1.使用tf.truncated_normal_initializer定义变量的初始化方式
        initial = tf.truncated_normal_initializer(stddev=0.005, dtype=tf.float32)
        # TODO：2.使用tf.get_variable函数定义变量,
        weights = tf.get_variable("weights",
                                  shape=shape,
                                  dtype=tf.float32,
                                  initializer=initial)
        return weights
    
    
    # 一层全连接
    def add_fc_layer(inputs, insize, outsize, activation_function=None):
        '''
        添加全连接层
        1.定义权值weights
        2.定义偏置bias
        3.完成全连接操作：inputs*weights+bias
        4.如果没有激活函数，返回softmax结果
          如果有激活函数则，返回activation_function结果
    
        :param inputs: 输入图像
        :param insize: 输入维度
        :param outsize: 输出维度
        :param activation_function: 激活函数
        :return: 计算结果
        '''
        # TODO：1.调用get_weights()获取权值
        weights = get_weights([insize, outsize])
        # TODO：2.调用get_biases()获取偏置
        bias = get_biases([outsize])
        # TODO：3.使用tf.matmul完成inputs和weights乘法运算;使用+完成与bias的加法运算
        wx_plus_b = tf.matmul(inputs, weights) + bias
        # TODO：4.如果激活函数None，返回tf.nn.softmax结果，否则返回activation_function结果
        if activation_function == None:
            outputs = tf.nn.softmax(wx_plus_b)
        else:
            outputs = activation_function(wx_plus_b)
        return outputs
    
    
    # 全部模型
    
    def inference(inputs):
        # TODO：1.调用add_fc_layer完成全连接操作1
        with tf.variable_scope("fc1", reuse=tf.AUTO_REUSE) as scope:
            # h_pool2_flat = tf.reshape(inputs, shape=[-1, 12288])
            fc1 = add_fc_layer(inputs, h_pool2_flat, 128, tf.nn.relu)
        # TODO：2.调用add_fc_layer完成全连接操作2
        with tf.variable_scope("fc2", reuse=tf.AUTO_REUSE) as scope:
            fc2 = add_fc_layer(fc1, 128, 64, tf.nn.tanh)
        # TODO：3.调用add_fc_layer完成全连接操作3
        with tf.variable_scope("fc3", reuse=tf.AUTO_REUSE) as scope:
            fc3 = add_fc_layer(fc2, 64, 32, tf.nn.sigmoid)
            # TODO：4.调用Dropout激活概率为0.5
            fc3_drop = tf.nn.dropout(fc3, 0.5)
    
        # TODO：5.调用add_fc_layer完成全连接操作4
        with tf.variable_scope("fc4", reuse=tf.AUTO_REUSE) as scope:
            fc4 = add_fc_layer(fc3_drop, 32, n_classes, tf.nn.relu)
        return fc4
    
    
    # 占位符
    x_pl = tf.placeholder(tf.float32)
    y_pl = tf.placeholder(tf.float32)
    
    # 预测值
    y_pred = inference(x_pl)
    
    # 损失值、优化方法
    k = (y_train - y_pred)
    loss = tf.reduce_mean(tf.square(k))
    optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
    train_op = optimizer.minimize(loss)
    
    # 模型训练
    saver = tf.train.Saver()
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch in range(150000):
            _, train_loss = sess.run([train_op, loss], feed_dict={x_pl: X_train, y_pl: y_train})
            if epoch % 1000 == 0:
                print('epoch:{}, train_losses:{}'.format(epoch, train_loss))
        saver.save(sess, "model/model.ckpt", global_step=epoch)
        print('epoch:{}, save model'.format(epoch))

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