数据材料

这是一个小型的人脸数据库，一共有40个人，每个人有10张照片作为样本数据。这些图片都是黑白照片，意味着这些图片都只有灰度0-255，没有rgb三通道。于是我们需要对这张大图片切分成一个个的小脸。整张图片大小是1190 × 942，一共有20 × 20张照片。那么每张照片的大小就是（1190 / 20）× （942 / 20）= 57 × 47 （大约，以为每张图片之间存在间距）。

问题解决：

10类样本，利用CNN训练可以分类10类数据的神经网络，与手写字符识别类似

#coding=utf-8
#http://www.jianshu.com/p/3e5ddc44aa56
#tensorflow 1.3.1
#python 3.6
import os
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.patches as patches
import numpy
from PIL import Image#获取dataset
def load_data(dataset_path):img = Image.open(dataset_path)# 定义一个20 × 20的训练样本，一共有40个人，每个人都10张样本照片img_ndarray = np.asarray(img, dtype='float64') / 256#img_ndarray = np.asarray(img, dtype='float32') / 32# 记录脸数据矩阵，57 * 47为每张脸的像素矩阵faces = np.empty((400, 57 * 47))for row in range(20):for column in range(20):faces[20 * row + column] = np.ndarray.flatten(img_ndarray[row * 57: (row + 1) * 57, column * 47 : (column + 1) * 47])label = np.zeros((400, 40))for i in range(40):label[i * 10: (i + 1) * 10, i] = 1# 将数据分成训练集，验证集，测试集train_data = np.empty((320, 57 * 47))train_label = np.zeros((320, 40))vaild_data = np.empty((40, 57 * 47))vaild_label = np.zeros((40, 40))test_data = np.empty((40, 57 * 47))test_label = np.zeros((40, 40))for i in range(40):train_data[i * 8: i * 8 + 8] = faces[i * 10: i * 10 + 8]train_label[i * 8: i * 8 + 8] = label[i * 10: i * 10 + 8]vaild_data[i] = faces[i * 10 + 8]vaild_label[i] = label[i * 10 + 8]test_data[i] = faces[i * 10 + 9]test_label[i] = label[i * 10 + 9]train_data = train_data.astype('float32')vaild_data = vaild_data.astype('float32')test_data = test_data.astype('float32')return [(train_data, train_label),(vaild_data, vaild_label),(test_data, test_label)]def convolutional_layer(data, kernel_size, bias_size, pooling_size):kernel = tf.get_variable("conv", kernel_size, initializer=tf.random_normal_initializer())bias = tf.get_variable('bias', bias_size, initializer=tf.random_normal_initializer())conv = tf.nn.conv2d(data, kernel, strides=[1, 1, 1, 1], padding='SAME')linear_output = tf.nn.relu(tf.add(conv, bias))pooling = tf.nn.max_pool(linear_output, ksize=pooling_size, strides=pooling_size, padding="SAME")return poolingdef linear_layer(data, weights_size, biases_size):weights = tf.get_variable("weigths", weights_size, initializer=tf.random_normal_initializer())biases = tf.get_variable("biases", biases_size, initializer=tf.random_normal_initializer())return tf.add(tf.matmul(data, weights), biases)def convolutional_neural_network(data):# 根据类别个数定义最后输出层的神经元n_ouput_layer = 40kernel_shape1=[5, 5, 1, 32]kernel_shape2=[5, 5, 32, 64]full_conn_w_shape = [15 * 12 * 64, 1024]out_w_shape = [1024, n_ouput_layer]bias_shape1=[32]bias_shape2=[64]full_conn_b_shape = [1024]out_b_shape = [n_ouput_layer]data = tf.reshape(data, [-1, 57, 47, 1])# 经过第一层卷积神经网络后，得到的张量shape为：[batch, 29, 24, 32]with tf.variable_scope("conv_layer1") as layer1:layer1_output = convolutional_layer(data=data,kernel_size=kernel_shape1,bias_size=bias_shape1,pooling_size=[1, 2, 2, 1])# 经过第二层卷积神经网络后，得到的张量shape为：[batch, 15, 12, 64]with tf.variable_scope("conv_layer2") as layer2:layer2_output = convolutional_layer(data=layer1_output,kernel_size=kernel_shape2,bias_size=bias_shape2,pooling_size=[1, 2, 2, 1])with tf.variable_scope("full_connection") as full_layer3:# 讲卷积层张量数据拉成2-D张量只有有一列的列向量layer2_output_flatten = tf.contrib.layers.flatten(layer2_output)layer3_output = tf.nn.relu(linear_layer(data=layer2_output_flatten,weights_size=full_conn_w_shape,biases_size=full_conn_b_shape))# layer3_output = tf.nn.dropout(layer3_output, 0.8)with tf.variable_scope("output") as output_layer4:output = linear_layer(data=layer3_output,weights_size=out_w_shape,biases_size=out_b_shape)return output;def train_facedata(dataset, model_dir,model_path):# train_set_x = data[0][0]# train_set_y = data[0][1]# valid_set_x = data[1][0]# valid_set_y = data[1][1]# test_set_x = data[2][0]# test_set_y = data[2][1]# X = tf.placeholder(tf.float32, shape=(None, None), name="x-input")  # 输入数据# Y = tf.placeholder(tf.float32, shape=(None, None), name='y-input')  # 输入标签
batch_size = 40# train_set_x, train_set_y = dataset[0]# valid_set_x, valid_set_y = dataset[1]# test_set_x, test_set_y = dataset[2]train_set_x = dataset[0][0]train_set_y = dataset[0][1]valid_set_x = dataset[1][0]valid_set_y = dataset[1][1]test_set_x = dataset[2][0]test_set_y = dataset[2][1]X = tf.placeholder(tf.float32, [batch_size, 57 * 47])Y = tf.placeholder(tf.float32, [batch_size, 40])predict = convolutional_neural_network(X)cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predict, labels=Y))optimizer = tf.train.AdamOptimizer(1e-2).minimize(cost_func)# 用于保存训练的最佳模型saver = tf.train.Saver()#model_dir = './model'#model_path = model_dir + '/best.ckpt'
    with tf.Session() as session:# 若不存在模型数据，需要训练模型参数if not os.path.exists(model_path + ".index"):session.run(tf.global_variables_initializer())best_loss = float('Inf')for epoch in range(20):epoch_loss = 0for i in range((int)(np.shape(train_set_x)[0] / batch_size)):x = train_set_x[i * batch_size: (i + 1) * batch_size]y = train_set_y[i * batch_size: (i + 1) * batch_size]_, cost = session.run([optimizer, cost_func], feed_dict={X: x, Y: y})epoch_loss += costprint(epoch, ' : ', epoch_loss)if best_loss > epoch_loss:best_loss = epoch_lossif not os.path.exists(model_dir):os.mkdir(model_dir)print("create the directory: %s" % model_dir)save_path = saver.save(session, model_path)print("Model saved in file: %s" % save_path)# 恢复数据并校验和测试
        saver.restore(session, model_path)correct = tf.equal(tf.argmax(predict,1), tf.argmax(Y,1))valid_accuracy = tf.reduce_mean(tf.cast(correct,'float'))print('valid set accuracy: ', valid_accuracy.eval({X: valid_set_x, Y: valid_set_y}))test_pred = tf.argmax(predict, 1).eval({X: test_set_x})test_true = np.argmax(test_set_y, 1)test_correct = correct.eval({X: test_set_x, Y: test_set_y})incorrect_index = [i for i in range(np.shape(test_correct)[0]) if not test_correct[i]]for i in incorrect_index:print('picture person is %i, but mis-predicted as person %i'%(test_true[i], test_pred[i]))plot_errordata(incorrect_index, "olivettifaces.gif")#画出在测试集中错误的数据
def plot_errordata(error_index, dataset_path):img = mpimg.imread(dataset_path)plt.imshow(img)currentAxis = plt.gca()for index in error_index:row = index // 2column = index % 2currentAxis.add_patch(patches.Rectangle(xy=(47 * 9 if column == 0 else 47 * 19,row * 57),width=47,height=57,linewidth=1,edgecolor='r',facecolor='none'))plt.savefig("result.png")plt.show()def main():dataset_path = "olivettifaces.gif"data = load_data(dataset_path)model_dir = './model'model_path = model_dir + '/best.ckpt'train_facedata(data, model_dir, model_path)if __name__ == "__main__" :main()