用keras小試CNN卷積神經網路應用於人臉識別
阿新 • • 發佈:2018-12-30
Keras是一個簡約,高度模組化的神經網路庫。採用Python / Theano開發。
使用Keras如果你需要一個深度學習庫:
- 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
本文使用keras嘗試配置了一個CNN(仿造http://blog.csdn.net/u012162613/article/details/43277187,用的圖片資源也是來自於此),程式更簡潔,檢測正確率可以達到95%。下面是測試的程式碼:'''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)