一、实验介绍

（1）实验目的

1. 掌握全连接神经网络分类器的训练与测试方法。
2. 掌握基于RBF分类器训练与测试方法。
3. 掌握CNN的基本原理、训练与测试方法。

（2）数据集简介

• 上述数据集均一.mat文件存放在/datasets文件夹下。

（3）实验内容

1. 编写程序实现全连接神经网络分类器设计。
2. 编写程序实现基于RBF分类器设计。
3. 编写程序实现基于CNN分类器设计。

（4）评价指标

• 本次实验主要利用Acc指标对聚类结果进行评价，值越大表明聚类效果越好。

二、全连接神经网络

import tensorflow as tf
import datadvi
import numpy as np

def FCNN(filename, batch_size=50,learning_rate=0.0001,iteration_times=5000):
    dataX_train,dataX_predict,dataY_train,dataY_predict = datadvi.divdata(filename)
    X = dataX_train
    Y1 = dataY_train
    # print(Y1)
    num_label = len(np.unique(Y1))
    Y = (np.arange(num_label)+1 == Y1[:, None]).astype(np.float32)
    dataY_predict1 = (np.arange(num_label)+1 == dataY_predict[:, None]).astype(np.float32)
    x = tf.placeholder(tf.float32, shape=[None, len(X[0])])
    y = tf.placeholder(tf.float32, shape=[None, num_label])

    w1 = tf.Variable(tf.random_normal([len(X[0]), 256], stddev=1, seed=1))
    w2 = tf.Variable(tf.random_normal([256, 256], stddev=1, seed=1))
    w_out = tf.Variable(tf.random_normal([256, num_label], stddev=1, seed=1))

    b1 = tf.Variable(tf.random_normal([256]))
    b2 = tf.Variable(tf.random_normal([256]))
    b_out = tf.Variable(tf.random_normal([num_label]))

    def Fully_neural_network(X):
        layer_1 = tf.nn.relu(tf.add(tf.matmul(X, w1), b1))
        layer_2 = tf.nn.relu(tf.add(tf.matmul(layer_1, w2), b2))
        layer_out = tf.matmul(layer_2, w_out) + b_out

        return layer_out

    net_out = Fully_neural_network(x)

    pre = tf.nn.softmax(net_out)
    pre1 = tf.argmax(pre, 1)

    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=net_out, labels=y))
    # loss = tf.reduce_mean(tf.abs(net_out-y))

    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
    train_op = optimizer.minimize(loss)

    correct_pre = tf.equal(tf.argmax(pre, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_pre, tf.float32))

    init = tf.global_variables_initializer()

    with tf.Session() as sess:
        sess.run(init)

        for i in range(1, iteration_times + 1):
            start = (i * batch_size) % len(X)
            end = min(start + batch_size, len(X))
            batch_x = X[start:end]
            batch_y = Y[start:end]
            sess.run(train_op, feed_dict={x: batch_x, y: batch_y})
            if i == iteration_times-1:
                l, acc = sess.run([loss, accuracy], feed_dict={x: dataX_predict, y: dataY_predict1})
                print("Step " + str(i) + ", Minibatch Loss= " + "{:.4f}".format(
                    l) + ", Training Accuracy= " + "{:.3f}".format(acc))

                # print(pre1.eval(feed_dict={x: batch_x}))

三、RBF

import random

import numpy as np
import tensorflow as tf
from numpy.random import RandomState
from sklearn.cluster import KMeans

import datadvi
from Acc import ACC


def compute_sigma(c):
    sigma = np.zeros([len(c)])
    for i in range(len(c)):
        for j in range(len(c)):
            temp_dist = np.sum(np.square(c[i] - c[j]))
            if sigma[i] < temp_dist:
                sigma[i] = temp_dist
    # print(sigma)
    return sigma


def init_C(dataX, num_label, label):
    c = np.empty(shape=(num_label, dataX.shape[-1]), dtype=np.float32)
    for i in range(num_label):
        c[i] = random.choice(dataX[label[:] == i + 1])
    # print(c)
    return c


def cluster_center(dataX, num_label):
    KM = KMeans(n_clusters=num_label, random_state=0).fit(dataX)
    return KM.cluster_centers_


def gen_data():
    rdm = RandomState(1)
    dataset_size = 1280
    X = rdm.rand(dataset_size, 2)
    Y1 = [[int(x1 + x2 < 1)] for (x1, x2) in X]
    Y = np.array(Y1).T[0]

    return X, X, Y, Y


def RBFNN(filename, batch_size=50, learning_rate=0.01, iteration_times=10000):
    dataX_train, dataX_predict, dataY_train, dataY_predict = datadvi.divdata(filename)  # 划分训练集和测试集
    # dataX_train, dataX_predict, dataY_train, dataY_predict = gen_data()

    # 数据标准化
    # dataX_train =  StandardScaler().fit_transform(dataX_train)
    # dataX_predict = StandardScaler().fit_transform(dataX_predict)
    # print(len(dataX_predict[0]))
    # dataX_train = (dataX_train-np.mean(dataX_train,axis=0))/np.std(dataX_train,axis=0)
    # dataX_predict = (dataX_predict - np.mean(dataX_predict, axis=0)) / np.std(dataX_predict, axis=0)
    # print(dataX_predict)

    num_label = len(np.unique(dataY_train))  # 数据种类
    num_feature = len(dataX_train[0])  # 数据维度
    # print(num_label)

    # 将标签转换成onehot形式
    dataY_train_onehot = (np.arange(num_label) + 1 == dataY_train[:, None]).astype(np.float32)
    dataY_predict_onehot = (np.arange(num_label) + 1 == dataY_predict[:, None]).astype(np.float32)

    # 使用原始标签数据
    dataY_train_origin = np.array([dataY_train]).T
    dataY_predict_origin = np.array([dataY_predict]).T

    # 定义占位
    X = tf.placeholder(tf.float32, shape=[None, num_feature])
    # Y = tf.placeholder(tf.float32,shape=[None,num_label])
    Y = tf.placeholder(tf.float32, shape=[None, 1])

    # 初始化中心点c和sigma（隐藏节点数与数据种类相同）
    # c = tf.Variable(tf.random_normal([num_label,num_feature]))
    # c = tf.Variable(c1)
    c = cluster_center(dataX_train, num_label)
    # c = tf.Variable(tf.cast(c,tf.float32))

    # sigma = tf.Variable(tf.ones([num_label]))
    sigma1 = compute_sigma(c)
    sigma = tf.Variable(tf.cast(sigma1, tf.float32))
    # sigma = tf.Variable(tf.cast(c[:,0],tf.float32))
    # print(sigma)

    # 初始化权重W（使用onehot表示则输出节点数与种类相同)
    # W = tf.Variable(tf.random_normal([num_label,num_label]))

    W = tf.Variable(tf.random_normal([num_label, 1]))

    # 计算隐藏节点输出K1
    # sigma2 = tf.square(sigma)
    K = []
    for i in range(num_label):
        K.append(tf.reduce_sum(tf.square((X - c[i])), 1) / sigma[i])
    K_tensor = tf.convert_to_tensor(K)
    K1 = tf.exp(-tf.transpose(K_tensor))

    # 计算输出层output
    output = tf.matmul(K1, W)

    # 四舍五入得到预测标签
    pred = tf.round(output)
    # pred = tf.argmax(output,1)

    # 定义损失函数loss（使用softmax交叉熵方法）
    # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=Y))
    loss = tf.reduce_sum(tf.abs(tf.subtract(output, Y)))

    # 选择优化方法
    # optimization = tf.train.AdamOptimizer(learning_rate).minimize(loss)
    optimization = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for i in range(1, 1 + iteration_times):
            start = (i * batch_size) % dataX_train.shape[0]
            end = min(start + batch_size, dataX_train.shape[0])
            batch_x = dataX_train[start:end]
            batch_y = dataY_train_origin[start:end]
            # print(K1.eval(feed_dict={X:batch_x}))
            # print(b.eval())
            sess.run(optimization, feed_dict={X: batch_x, Y: batch_y})
            if i == iteration_times-1:
                print("loss of step {} is {}".format(i, loss.eval(feed_dict={X: dataX_train, Y: dataY_train_origin})))
                print("ACC is: ", ACC(np.array(pred.eval(feed_dict={X: dataX_train})).T[0], dataY_train))

四、CNN

import tensorflow as tf
import datadvi
import numpy as np
import math

sess = tf.InteractiveSession()


def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)  # 标准差为0.1的正态分布
    return tf.Variable(initial)


def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)  # 偏差初始化为0.1
    return tf.Variable(initial)


def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1], padding='SAME')


def cnn(filename, batch_size=50,learning_rate=0.0001,iteration_times=5000):
    dataX_train, dataX_predict, dataY_train, dataY_predict = datadvi.divdata(filename)
    num_label = len(np.unique(dataY_train))

    dataY_train_onehot = (np.arange(num_label) + 1 == dataY_train[:, None]).astype(np.float32)
    dataY_predict_onehot = (np.arange(num_label) + 1 == dataY_predict[:,None]).astype(np.float32)

    x = tf.placeholder(tf.float32, [None, len(dataX_train[0])])
    y_ = tf.placeholder(tf.float32, [None, num_label])
    # -1代表先不考虑输入的图片例子多少这个维度，1是channel的数量
    mat_XY = int(math.sqrt(len(dataX_train[0])))

    if mat_XY*mat_XY == len(dataX_train[0]):
        x_image = tf.reshape(x, [-1, mat_XY, mat_XY, 1]) #这适合数据维度能够开方得到整数的数据集, MNIST 和  COIL20等
    else:
        x_image = tf.reshape(x,[-1,50,100,1]) #这是gisette的数据参数大小设置


    keep_prob = tf.placeholder(tf.float32)

    # 构建卷积层1
    W_conv1 = weight_variable([5, 5, 1, 32])  # 卷积核5*5，1个channel，32个卷积核，形成32个featuremap
    b_conv1 = bias_variable([32])  # 32个featuremap的偏置
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)  # 用relu非线性处理
    h_pool1 = max_pool_2x2(h_conv1)  # pooling池化


    # 构建卷积层2
    W_conv2 = weight_variable([5, 5, 32, 64])  # 注意这里channel值是32
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)


    # 构建全连接层1
    W_fc1 = weight_variable([ int(mat_XY/4* mat_XY/4 * 64), 1024])
    #W_fc1 = weight_variable([int(50*100/4*64),1024]) #这是gisette的数据参数大小设置
    b_fc1 = bias_variable([1024])
    h_pool3 = tf.reshape(h_pool2, [-1, int(mat_XY/4* mat_XY/4 * 64)])
    #h_pool3 = tf.reshape(h_pool2,[-1,int(50*100/4*64)]) #这是gisette的数据参数大小设置
    h_fc1 = tf.nn.relu(tf.matmul(h_pool3, W_fc1) + b_fc1)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # 构建全连接层2
    W_fc2 = weight_variable([1024, num_label])
    b_fc2 = bias_variable([num_label])
    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1]))
    train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)
    correct_prediction = tf.equal(tf.arg_max(y_conv, 1), tf.arg_max(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    tf.global_variables_initializer().run()

    for i in range(iteration_times):
        start = (i * batch_size) % dataX_train.shape[0]
        end = min(start + batch_size, dataX_train.shape[0])
        batch_x = dataX_train[start:end]
        batch_y = dataY_train_onehot[start:end]

        # if i % 100 == 0:
        #     train_accuracy = accuracy.eval(feed_dict={x: batch_x, y_: batch_y, keep_prob: 1.0})
        #     print("step %d, training accuracy %g" % (i, train_accuracy))
        #     print("loss is: ", cross_entropy.eval(feed_dict={x:batch_x,y_:batch_y,keep_prob:1.0}))

        train_step.run(feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.5})
    print("test accuracy %g" % accuracy.eval(feed_dict={x: dataX_predict,
                                                        y_: dataY_predict_onehot, keep_prob: 1.0}))

五、结果

（1）acc结果对比

（2）结果分析

• 通过实验可以发现由于lung数据集的数据量最大，标签类别只有5，因此每一类标签的训练数据集较大，因此结果较高，可以到百分之八十，yale数据量小，标签类别为15，因此每一类标签的训练数据集较小，结果也相对较差。可以通过优化模型，或者增加数据记得方式来提高精确度。
• 这三种方法中，可以发现cnn的效果最好，rbf次之，全连接的效果最差，因此，在对图像进行分类时，推荐使用cnn神经网络模型