Keras是一個簡約，高度模組化的神經網路庫。

可以很容易和快速實現原型（通過總模組化，極簡主義，和可擴充套件性）

同時支援卷積網路（vision）和複發性的網路（序列資料）。以及兩者的組合。

無縫地執行在CPU和GPU上。

keras的資源庫網址為https://github.com/fchollet/keras

olivettifaces人臉資料庫介紹

Olivetti Faces是紐約大學的一個比較小的人臉庫，由 40個人的400張圖片構成，即每個人的人臉圖片為10張。每張圖片的灰度級為8位，每個畫素的灰度大小位於0-255之間，每張圖片大小為64×64。 如下圖，這個圖片大小是1140942，一共有2020張人臉，故每張人臉大小是（1140/20）（942/20）即5747=2679：

預處理模組

使用了PIL（Python Imaging Library）模組，是Python平臺事實上的影象處理標準庫。

預處理流程是：開啟檔案-》歸一化-》將圖片轉為資料集-》生成label-》使用pickle序列化資料集

numpy.ndarray.flatten函式的功能是將一個矩陣平鋪為向量

from PIL import Image

import numpy

import cPickle

img = Image.open('G:dataolivettifaces.gif')

# numpy supports conversion from image to ndarray and normalization by dividing 255

# 1140 * 942 ndarray

img_ndarray = numpy.asarray(img, dtype='float64') / 255

# create numpy array of 400*2679

img_rows, img_cols = 57, 47

face_data = numpy.empty((400, img_rows*img_cols))

# convert 1140*942 ndarray to 400*2679 matrix

for row in range(20):

for col in range(20):

face_data[row*20+col] = numpy.ndarray.flatten(img_ndarray[row*img_rows:(row+1)*img_rows, col*img_cols:(col+1)*img_cols])

# create label

face_label = numpy.empty(400, dtype=int)

for i in range(400):

face_label[i] = i / 10

# pickling file

f = open('G:dataolivettifaces.pkl','wb')

# store data and label as a tuple

cPickle.dump((face_data,face_label), f)

f.close()

分類模型

程式參考了官方示例：https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py

一共有40個類，每個類10個樣本，共400個樣本。其中320個樣本用於訓練，40個用於驗證，剩下40個測試

注意給第一層指定input_shape，如果是MLP，程式碼為：

model = Sequential()

# Dense(64) is a fully-connected layer with 64 hidden units.

# in the first layer, you must specify the expected input data shape:

# here, 20-dimensional vectors.

model.add(Dense(64, input_dim=20, init='uniform'))

後面可以不指定Dense的input shape

from __future__ import print_function

import numpy as np

import cPickle

np.random.seed(1337) # for reproducibililty

from keras.datasets import mnist

from keras.models import Sequential

from keras.layers.core import Dense, Dropout, Activation, Flatten

from keras.layers.convolutional import Convolution2D, MaxPooling2D

from keras.utils import np_utils

# split data into train,vavlid and test

# train:320

# valid:40

# test:40

def split_data(fname):

f = open(fname, 'rb')

face_data,face_label = cPickle.load(f)

X_train = np.empty((320, img_rows * img_cols))

Y_train = np.empty(320, dtype=int)

X_valid = np.empty((40, img_rows* img_cols))

Y_valid = np.empty(40, dtype=int)

X_test = np.empty((40, img_rows* img_cols))

Y_test = np.empty(40, dtype=int)

for i in range(40):

X_train[i*8:(i+1)*8,:] = face_data[i*10:i*10+8,:]

Y_train[i*8:(i+1)*8] = face_label[i*10:i*10+8]

X_valid[i] = face_data[i*10+8,:]

Y_valid[i] = face_label[i*10+8]

X_test[i] = face_data[i*10+9,:]

Y_test[i] = face_label[i*10+9]

return (X_train, Y_train, X_valid, Y_valid, X_test, Y_test)

if __name__=='__main__':

batch_size = 10

nb_classes = 40

nb_epoch = 12

# input image dimensions

img_rows, img_cols = 57, 47

# number of convolutional filters to use

nb_filters = 32

# size of pooling area for max pooling

nb_pool = 2

# convolution kernel size

nb_conv = 3

(X_train, Y_train, X_valid, Y_valid, X_test, Y_test) = split_data('G:dataolivettifaces.pkl')

X_train = X_train.reshape(X_train.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_test.shape[0], 'test samples')

# convert label to binary class matrix

Y_train = np_utils.to_categorical(Y_train, nb_classes)

Y_test = np_utils.to_categorical(Y_test, nb_classes)

model = Sequential()

# 32 convolution filters , the size of convolution kernel is 3 * 3

# border_mode can be 'valid' or 'full'

#‘valid’only apply filter to complete patches of the image.

# 'full' zero-pads image to multiple of filter shape to generate output of shape: image_shape + filter_shape - 1

# when used as the first layer, you should specify the shape of inputs

# the first number means the channel of an input image, 1 stands for grayscale imgs, 3 for RGB imgs

model.add(Convolution2D(nb_filters, nb_conv, nb_conv,

border_mode='valid',

input_shape=(1, img_rows, img_cols)))

# use rectifier linear units : max(0.0, x)

model.add(Activation('relu'))

# second convolution layer with 32 filters of size 3*3

model.add(Convolution2D(nb_filters, nb_conv, nb_conv))

model.add(Activation('relu'))

# max pooling layer, pool size is 2 * 2

model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))

# drop out of max-pooling layer , drop out rate is 0.25

model.add(Dropout(0.25))

# flatten inputs from 2d to 1d

model.add(Flatten())

# add fully connected layer with 128 hidden units

model.add(Dense(128))

model.add(Activation('relu'))

model.add(Dropout(0.5))

# output layer with softmax

model.add(Dense(nb_classes))

model.add(Activation('softmax'))

# use cross-entropy cost and adadelta to optimize params

model.compile(loss='categorical_crossentropy', optimizer='adadelta')

# train model with bath_size =10, epoch=12

# set verbose=1 to show train info

model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,

show_accuracy=True, verbose=1, validation_data=(X_test, Y_test))

# evaluate on test set

score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0)

print('Test score:', score[0])

print('Test accuracy:', score[1])

結果：

準確率有97%

via : http://www.cnblogs.com/wacc/p/5341654.htm