tensorflow1.1/人脸识别Yale数据集

环境tensorflow1.1，python3, matplotlib2.02

Yale人脸数据集共有165张图片，15个人，每个人对应十一张图片

#coding:utf-8"""python 3tensorflow 1.1matplotlib 2.02"""import tensorflow as tfimport scipy.io as sioimport numpy as npimport matplotlib.pyplot as pltlearning_rate = 0.0001batch_size=5#加载数据def read_data(filename):    with open(filename,'rb') as f:        #记载matlab文件        dict = sio.loadmat(f)    return dict['fea'],dict['gnd']train_data,train_labels = read_data('Yale_64x64.mat')#将标签转为0-14train_labels = train_labels-1#shuffle datanp.random.seed(100)train_data = np.random.permutation(train_data)np.random.seed(100)train_labels = np.random.permutation(train_labels)test_data = train_data[0:50,:]test_labels = train_labels[0:50]np.random.seed(200)test_data = np.random.permutation(test_data)np.random.seed(200)test_labels = np.random.permutation(test_labels)#将标签转为one_hot类型def label_to_one_hot(labels_dense, num_classes=15):    num_labels = labels_dense.shape[0]    index_offset = np.arange(num_labels) * num_classes    labels_one_hot = np.zeros((num_labels, num_classes))    labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1    return labels_one_hot#将图片转为灰度图def to4d(img):    return img.reshape(img.shape[0],64,64,1).astype(np.float32)/255train_data = to4d(train_data)train_labels = label_to_one_hot(train_labels,15)test_data = to4d(test_data)test_labels = label_to_one_hot(test_labels,15)xs = tf.placeholder(tf.float32,[None,64,64,1])ys = tf.placeholder(tf.float32,[None,15])keep_prob = tf.placeholder(tf.float32)#开始构建卷积神经网络conv1 = tf.layers.conv2d(inputs=xs,filters=32,kernel_size=2,strides=1,padding='same',activation=tf.nn.relu)pool1 = tf.layers.max_pooling2d(conv1,pool_size=2,strides=2)conv2 = tf.layers.conv2d(pool1,filters=72,kernel_size=2,strides=1,padding='same',activation=tf.nn.relu)pool2 = tf.layers.max_pooling2d(conv2,pool_size=2,strides=2)flat = tf.reshape(pool2,[-1,16*16*72])dense = tf.layers.dense(flat,512,tf.nn.relu)dropout = tf.nn.dropout(dense,keep_prob)output = tf.layers.dense(dropout,15)#计算lossloss = tf.losses.softmax_cross_entropy(onehot_labels=ys,logits=output)train = tf.train.AdamOptimizer(learning_rate).minimize(loss)#返回两个参数一个train_opt,一个accaccuracy = tf.metrics.accuracy(labels=tf.argmax(ys,axis=1),predictions=tf.argmax(output,axis=1))[1]with tf.Session() as sess:    init = tf.group(tf.global_variables_initializer(),tf.local_variables_initializer())    sess.run(init)    for step in range(1000):        i = 0        while i < len(train_data):            start = i            end = i+batch_size            batch_x = np.array(train_data[start:end])            batch_y = np.array(train_labels[start:end])            _,c = sess.run([train,loss],feed_dict={xs:batch_x,ys:batch_y,keep_prob:0.75})            i += batch_size        if step % 1 ==0:            acc = sess.run(accuracy,feed_dict={xs:test_data,ys:test_labels,keep_prob:1})            print('= = = = = = > > > > > > ','step:',step,'loss: %.4f'%c,'accuracy: %.2f' %acc)

数据集：链接：http://pan.baidu.com/s/1jIggvEu 密码：fz15