- '''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)
来源: http://blog.csdn.net/eagelangel/article/details/50759993