# -*-coding:utf8-*-#
"""
本程序基于python+numpy+theano+PIL开发,采用类似LeNet5的CNN模型,应用于olivettifaces人脸数据库,
实现人脸识别的功能,模型的误差降到了5%以下。
本程序只是个人学习过程的一个toy implement,模型可能存在overfitting,因为样本小,这一点也无从验证。
但是,本程序意在理清程序开发CNN模型的具体步骤,特别是针对图像识别,从拿到图像数据库,到实现一个针对这个图像数据库的CNN模型,
我觉得本程序对这些流程的实现具有参考意义。
@author:wepon(http://2hwp.com)
讲解这份代码的文章:http://blog.csdn.net/u012162613/article/details/43277187
"""
import os
import sys
import time
import numpy
from PIL import Image
import theano
import theano.tensor as T
from theano.tensor.signal import downsample
from theano.tensor.nnet import conv
"""
加载图像数据的函数,dataset_path即图像olivettifaces的路径
加载olivettifaces后,划分为train_data,valid_data,test_data三个数据集
函数返回train_data,valid_data,test_data以及对应的label
"""
def load_data(dataset_path):
img = Image.open(dataset_path)
img_ndarray = numpy.asarray(img, dtype='float64')/256
faces=numpy.empty((400,2679))
for row in range(20):
for column in range(20):
faces[row*20+column]=numpy.ndarray.flatten(img_ndarray [row*57:(row+1)*57,column*47:(column+1)*47])
label=numpy.empty(400)
for i in range(40):
label[i*10:i*10+10]=i
label=label.astype(numpy.int)
#分成训练集、验证集、测试集,大小如下
train_data=numpy.empty((320,2679))
train_label=numpy.empty(320)
valid_data=numpy.empty((40,2679))
valid_label=numpy.empty(40)
test_data=numpy.empty((40,2679))
test_label=numpy.empty(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]
valid_data[i]=faces[i*10+8]
valid_label[i]=label[i*10+8]
test_data[i]=faces[i*10+9]
test_label[i]=label[i*10+9]
#将数据集定义成shared类型,才能将数据复制进GPU,利用GPU加速程序。
def shared_dataset(data_x, data_y, borrow=True):
shared_x = theano.shared(numpy.asarray(data_x,
dtype=theano.config.floatX),
borrow=borrow)
shared_y = theano.shared(numpy.asarray(data_y,
dtype=theano.config.floatX),
borrow=borrow)
return shared_x, T.cast(shared_y, 'int32')
train_set_x, train_set_y = shared_dataset(train_data,train_label)
valid_set_x, valid_set_y = shared_dataset(valid_data,valid_label)
test_set_x, test_set_y = shared_dataset(test_data,test_label)
rval = [(train_set_x, train_set_y), (valid_set_x, valid_set_y),
(test_set_x, test_set_y)]
return rval
#分类器,即CNN最后一层,采用逻辑回归(softmax)
class LogisticRegression(object):
def __init__(self, input, n_in, n_out):
self.W = theano.shared(
value=numpy.zeros(
(n_in, n_out),
dtype=theano.config.floatX
),
name='W',
borrow=True
)
self.b = theano.shared(
value=numpy.zeros(
(n_out,),
dtype=theano.config.floatX
),
name='b',
borrow=True
)
self.p_y_given_x = T.nnet.softmax(T.dot(input, self.W) + self.b)
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
self.params = [self.W, self.b]
def negative_log_likelihood(self, y):
return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
def errors(self, y):
if y.ndim != self.y_pred.ndim:
raise TypeError(
'y should have the same shape as self.y_pred',
('y', y.type, 'y_pred', self.y_pred.type)
)
if y.dtype.startswith('int'):
return T.mean(T.neq(self.y_pred, y))
else:
raise NotImplementedError()
#全连接层,分类器前一层
class HiddenLayer(object):
def __init__(self, rng, input, n_in, n_out, W=None, b=None,
activation=T.tanh):
self.input = input
if W is None:
W_values = numpy.asarray(
rng.uniform(
low=-numpy.sqrt(6. / (n_in + n_out)),
high=numpy.sqrt(6. / (n_in + n_out)),
size=(n_in, n_out)
),
dtype=theano.config.floatX
)
if activation == theano.tensor.nnet.sigmoid:
W_values *= 4
W = theano.shared(value=W_values, name='W', borrow=True)
if b is None:
b_values = numpy.zeros((n_out,), dtype=theano.config.floatX)
b = theano.shared(value=b_values, name='b', borrow=True)
self.W = W
self.b = b
lin_output = T.dot(input, self.W) + self.b
self.output = (
lin_output if activation is None
else activation(lin_output)
)
# parameters of the model
self.params = [self.W, self.b]
#卷积+采样层(conv+maxpooling)
class LeNetConvPoolLayer(object):
def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):
assert image_shape[1] == filter_shape[1]
self.input = input
fan_in = numpy.prod(filter_shape[1:])
fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /
numpy.prod(poolsize))
# initialize weights with random weights
W_bound = numpy.sqrt(6. / (fan_in + fan_out))
self.W = theano.shared(
numpy.asarray(
rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX
),
borrow=True
)
# the bias is a 1D tensor -- one bias per output feature map
b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
self.b = theano.shared(value=b_values, borrow=True)
# 卷积
conv_out = conv.conv2d(
input=input,
filters=self.W,
filter_shape=filter_shape,
image_shape=image_shape
)
# 子采样
pooled_out = downsample.max_pool_2d(
input=conv_out,
ds=poolsize,
ignore_border=True
)
self.output = T.tanh(pooled_out + self.b.dimshuffle('x', 0, 'x', 'x'))
# store parameters of this layer
self.params = [self.W, self.b]
#保存训练参数的函数
def save_params(param1,param2,param3,param4):
import cPickle
write_file = open('params.pkl', 'wb')
cPickle.dump(param1, write_file, -1)
cPickle.dump(param2, write_file, -1)
cPickle.dump(param3, write_file, -1)
cPickle.dump(param4, write_file, -1)
write_file.close()
"""
上面定义好了CNN的一些基本构件,下面的函数将CNN应用于olivettifaces这个数据集,CNN的模型基于LeNet。
采用的优化算法是批量随机梯度下降算法,minibatch SGD,所以下面很多参数都带有batch_size,比如image_shape=(batch_size, 1, 57, 47)
可以设置的参数有:
batch_size,但应注意n_train_batches、n_valid_batches、n_test_batches的计算都依赖于batch_size
nkerns=[5, 10]即第一二层的卷积核个数可以设置
全连接层HiddenLayer的输出神经元个数n_out可以设置,要同时更改分类器的输入n_in
另外,还有一个很重要的就是学习速率learning_rate.
"""
def evaluate_olivettifaces(learning_rate=0.05, n_epochs=200,
dataset='olivettifaces.gif',
nkerns=[5, 10], batch_size=40):
#随机数生成器,用于初始化参数
rng = numpy.random.RandomState(23455)
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FaceRecognition_CNN(olivettifaces).zip (4个子文件)
olivettifaces.gif 1.13MB
use_CNN_olivettifaces.py 7KB
params.pkl 14.07MB
train_CNN_olivettifaces.py 15KB
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