用keras小试CNN卷积神经网络应用于人脸识别

Keras是一个简约，高度模块化的神经网络库。采用Python / Theano开发。

使用Keras如果你需要一个深度学习库：

• 可以很容易和快速实现原型（通过总模块化，极简主义，和可扩展性）
• 同时支持卷积网络（vision）和复发性的网络（序列数据）。以及两者的组合。
• 无缝地运行在CPU和GPU上。

keras的资源库网址为https://github.com/fchollet/keras

本文使用keras尝试配置了一个CNN（仿造http://blog.csdn.net/u012162613/article/details/43277187，用的图片资源也是来自于此），程序更简洁，检测正确率可以达到95%。下面是测试的代码：

'''Train a simple convnet on the part olivetti faces dataset.

Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python mnist_cnn.py

Get to 95% test accuracy after 25 epochs (there is still a lot of margin for parameter tuning).
'''

from __future__ import print_function
import numpy
numpy.random.seed(1337)  # for reproducibility

from PIL import Image

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
from keras.utils import np_utils

# There are 40 different classes
nb_classes = 40
nb_epoch = 40
batch_size = 40

# input image dimensions
img_rows, img_cols = 57, 47
# number of convolutional filters to use
nb_filters1, nb_filters2 = 5, 10
# size of pooling area for max pooling
nb_pool = 2
# convolution kernel size
nb_conv = 3

def load_data(dataset_path):
    img = Image.open(dataset_path)
    img_ndarray = numpy.asarray(img, dtype='float64')/256
    #400pictures,size:57*47=2679
    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)

    #train:320,valid:40,test:40
    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]
     
    rval = [(train_data, train_label), (valid_data, valid_label),
            (test_data, test_label)]
    return rval

def Net_model(lr=0.005,decay=1e-6,momentum=0.9):
	model = Sequential()
	model.add(Convolution2D(nb_filters1, nb_conv, nb_conv,
							border_mode='valid',
							input_shape=(1, img_rows, img_cols)))
	model.add(Activation('tanh'))
	model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))

	model.add(Convolution2D(nb_filters2, nb_conv, nb_conv))
	model.add(Activation('tanh'))
	model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
	#model.add(Dropout(0.25))

	model.add(Flatten())
	model.add(Dense(1000)) #Full connection
	model.add(Activation('tanh'))
	#model.add(Dropout(0.5))
	model.add(Dense(nb_classes))
	model.add(Activation('softmax'))

	sgd = SGD(lr=lr, decay=decay, momentum=momentum, nesterov=True)
	model.compile(loss='categorical_crossentropy', optimizer=sgd)
	
	return model

def train_model(model,X_train,Y_train,X_val,Y_val):
	model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
          show_accuracy=True, verbose=1, validation_data=(X_val, Y_val))
	model.save_weights('model_weights.h5',overwrite=True)
	return model

def test_model(model,X,Y):
	model.load_weights('model_weights.h5')
	score = model.evaluate(X, Y, show_accuracy=True, verbose=0)
	print('Test score:', score[0])
	print('Test accuracy:', score[1])
	return score

if __name__ == '__main__':
	# the data, shuffled and split between tran and test sets
	(X_train, y_train), (X_val, y_val),(X_test, y_test) = load_data('olivettifaces.gif')

	X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
	X_val = X_val.reshape(X_val.shape[0], 1, img_rows, img_cols)
	X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
	print('X_train shape:', X_train.shape)
	print(X_train.shape[0], 'train samples')
	print(X_val.shape[0], 'validate samples')
	print(X_test.shape[0], 'test samples')

	# convert class vectors to binary class matrices
	Y_train = np_utils.to_categorical(y_train, nb_classes)
	Y_val = np_utils.to_categorical(y_val, nb_classes)
	Y_test = np_utils.to_categorical(y_test, nb_classes)

	model=Net_model()
	#train_model(model,X_train,Y_train,X_val,Y_val)	
	#score=test_model(model,X_test,Y_test)

	model.load_weights('model_weights.h5')
	classes=model.predict_classes(X_test,verbose=0)
	test_accuracy = numpy.mean(numpy.equal(y_test,classes))
	print("accuarcy:",test_accuracy)