cnn程序 matlab

# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import print_function from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation, Flatten from keras.layers.advanced_activations import PReLU from keras.layers.convolutional import Convolution2D, MaxPooling2D from keras.optimizers import SGD, Adadelta, Adagrad from keras.utils import np_utils, generic_utils from data import load_data import matplotlib.pyplot as plt ''' train the dataset of face 42000 image learning rate : 0.0015 pathc size:100 dropout :0.25 l2 = 0.01 ''' def funcnn(LR,BS): data, label = load_data() label = np_utils.to_categorical(label, 10) model = Sequential() model.add(Convolution2D(4, 1, 5, 5, border_mode='valid')) model.add(Activation('relu')) model.add(Dropout(0.25)) model.add(Convolution2D(8,4, 3, 3, border_mode='valid')) model.add(Activation('relu')) model.add(MaxPooling2D(poolsize=(2, 2))) model.add(Dropout(0.25)) model.add(Convolution2D(16, 8, 3, 3, border_mode='valid')) model.add(Activation('relu')) model.add(MaxPooling2D(poolsize=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(16*4*4, 256, init='normal')) model.add(Activation('tanh')) model.add(Dense(256, 10, init='normal')) model.add(Activation('softmax')) sgd = SGD(l2=0.001,lr=LR, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd,class_mode="categorical") #checkpointer = ModelCheckpoint(filepath="weight.hdf5",verbose=1,save_best_only=True) #model.fit(data, label, batch_size=100,nb_epoch=10,shuffle=True,verbose=1,show_accuracy=True,validation_split=0.2,callbacks=[checkpointer]) result = model.fit(data, label, batch_size=BS,nb_epoch=20,shuffle=True,verbose=1,show_accuracy=True,validation_split=0.2) #model.save_weights(weights,accuracy=False) # plot the result plt.figure plt.plot(result.epoch,result.history['acc'],label="acc") plt.plot(result.epoch,result.history['val_acc'],label="val_acc") plt.scatter(result.epoch,result.history['acc'],marker='*') plt.scatter(result.epoch,result.history['val_acc']) plt.legend(loc='under right') plt.show() plt.figure plt.plot(result.epoch,result.history['loss'],label="loss") plt.plot(result.epoch,result.history['val_loss'],label="val_loss") plt.scatter(result.epoch,result.history['loss'],marker='*') plt.scatter(result.epoch,result.history['val_loss'],marker='*') plt.legend(loc='upper right') plt.show()