olivettifaces数据集实现人脸识别代码
数据集：
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 24 18:21:21 2019
@author: 92958
"""
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
dataset_path='./olivettifaces.gifa'
#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()
控制台信息：
runfile('F:/python/TensorFlow/人脸识别/olive1.py', wdir='F:/python/TensorFlow/人脸识别')
WARNING:tensorflow:From C:\Users\92958\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
- https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
- https://github.com/tensorflow/addons
If you depend on functionality not listed there, please file an issue.
WARNING:tensorflow:From C:\Users\92958\Anaconda3\lib\site-packages\tensorflow\contrib\layers\python\layers\layers.py:1624: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.
Instructions for updating:
Use keras.layers.flatten instead.
WARNING:tensorflow:From F:/python/TensorFlow/人脸识别/olive1.py:158: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.
See tf.nn.softmax_cross_entropy_with_logits_v2.
0 : 2671140.984375
create the directory: ./model
Model saved in file: ./model/best.ckpt
1 : 610905.9375
Model saved in file: ./model/best.ckpt
2 : 181258.35693359375
Model saved in file: ./model/best.ckpt
3 : 54391.228271484375
Model saved in file: ./model/best.ckpt
4 : 24234.38525390625
Model saved in file: ./model/best.ckpt
5 : 9868.018524169922
Model saved in file: ./model/best.ckpt
6 : 3433.5851974487305
Model saved in file: ./model/best.ckpt
7 : 826.4495697021484
Model saved in file: ./model/best.ckpt
8 : 200.12329292297363
Model saved in file: ./model/best.ckpt
9 : 194.84842109680176
Model saved in file: ./model/best.ckpt
10 : 63.74338483810425
Model saved in file: ./model/best.ckpt
11 : 10.006996154785156
Model saved in file: ./model/best.ckpt
12 : 7.118054211139679
Model saved in file: ./model/best.ckpt
13 : 0.0
Model saved in file: ./model/best.ckpt
14 : 0.0
15 : 0.0
16 : 0.0
17 : 0.0
18 : 0.0
19 : 0.0
WARNING:tensorflow:From C:\Users\92958\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
INFO:tensorflow:Restoring parameters from ./model/best.ckpt
valid set accuracy: 0.8
picture person is 4, but mis-predicted as person 8
picture person is 18, but mis-predicted as person 14
picture person is 21, but mis-predicted as person 27
picture person is 35, but mis-predicted as person 17
原文：https://blog.csdn.net/hanghangaidoudou/article/details/79347080
