使用keras实现CNN,直接上代码:
from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Convolution2D, MaxPooling2D from keras.utils import np_utils from keras import backend as K class LossHistory(keras.callbacks.Callback): def on_train_begin(self, logs={}): self.losses = {'batch':[], 'epoch':[]} self.accuracy = {'batch':[], 'epoch':[]} self.val_loss = {'batch':[], 'epoch':[]} self.val_acc = {'batch':[], 'epoch':[]} def on_batch_end(self, batch, logs={}): self.losses['batch'].append(logs.get('loss')) self.accuracy['batch'].append(logs.get('acc')) self.val_loss['batch'].append(logs.get('val_loss')) self.val_acc['batch'].append(logs.get('val_acc')) def on_epoch_end(self, batch, logs={}): self.losses['epoch'].append(logs.get('loss')) self.accuracy['epoch'].append(logs.get('acc')) self.val_loss['epoch'].append(logs.get('val_loss')) self.val_acc['epoch'].append(logs.get('val_acc')) def loss_plot(self, loss_type): iters = range(len(self.losses[loss_type])) plt.figure() # acc plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc') # loss plt.plot(iters, self.losses[loss_type], 'g', label='train loss') if loss_type == 'epoch': # val_acc plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc') # val_loss plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss') plt.grid(True) plt.xlabel(loss_type) plt.ylabel('acc-loss') plt.legend(loc="upper right") plt.show() history = LossHistory() batch_size = 128 nb_classes = 10 nb_epoch = 20 img_rows, img_cols = 28, 28 nb_filters = 32 pool_size = (2,2) kernel_size = (3,3) (X_train, y_train), (X_test, y_test) = mnist.load_data() X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1) X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) X_train = X_train.astype('float32') X_test = X_test.astype('float32') X_train /= 255 X_test /= 255 print('X_train shape:', X_train.shape) print(X_train.shape[0], 'train samples') print(X_test.shape[0], 'test samples') Y_train = np_utils.to_categorical(y_train, nb_classes) Y_test = np_utils.to_categorical(y_test, nb_classes) model3 = Sequential() model3.add(Convolution2D(nb_filters, kernel_size[0] ,kernel_size[1], border_mode='valid', input_shape=input_shape)) model3.add(Activation('relu')) model3.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1])) model3.add(Activation('relu')) model3.add(MaxPooling2D(pool_size=pool_size)) model3.add(Dropout(0.25)) model3.add(Flatten()) model3.add(Dense(128)) model3.add(Activation('relu')) model3.add(Dropout(0.5)) model3.add(Dense(nb_classes)) model3.add(Activation('softmax')) model3.summary() model3.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']) model3.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epoch, verbose=1, validation_data=(X_test, Y_test),callbacks=[history]) score = model3.evaluate(X_test, Y_test, verbose=0) print('Test score:', score[0]) print('Test accuracy:', score[1]) #acc-loss history.loss_plot('epoch')