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)