TensorFlow基础笔记(8) TensorFlow简单人脸识别
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
olivettifaces.gif
#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 pooling def 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 = 40 kernel_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 = 0 for 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 += cost print(epoch, ' : ', epoch_loss) if best_loss > epoch_loss: best_loss = epoch_loss if 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 // 2 column = index % 2 currentAxis.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()
C:\python36\python.exe X:/DeepLearning/code/face/TensorFlow_CNN_face/facerecognition_main.py
valid set accuracy: 0.825
picture person is 0, but mis-predicted as person 23
picture person is 6, but mis-predicted as person 38
picture person is 8, but mis-predicted as person 34
picture person is 15, but mis-predicted as person 11
picture person is 24, but mis-predicted as person 7
picture person is 29, but mis-predicted as person 7
picture person is 33, but mis-predicted as person 39
