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最新推荐文章于 2023-09-27 09:40:27 发布

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本文介绍了一种使用Keras框架实现的卷积神经网络（CNN）模型，用于MNIST手写数字数据集的分类任务。模型包含多个卷积层、池化层、全连接层及Dropout层，通过训练提高识别准确率。

from __future__ import print_function
import numpy as np
np.random.seed(1337)
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
import keras.backend.tensorflow_backend as KTF
import tensorflow as tf

batch_size = 128
nb_classes = 10
nb_epoch = 12

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

Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
# 建立序贯模型
model = Sequential()

model.add(Convolution2D(nb_filters, kernel_size[0] ,kernel_size[1],border_mode='valid',input_shape=input_shape))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=pool_size))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))


config = tf.ConfigProto()
config.gpu_options.allow_growth = True  # 不全部占满显存, 按需分配
config.gpu_options.per_process_gpu_memory_fraction = 0.6  #限制GPU内存占用率

sess = tf.Session(config=config)
KTF.set_session(sess)  # 设置session
    
model.summary()
model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,verbose=1, validation_data=(X_test, Y_test))

score = model.evaluate(X_test, Y_test, verbose=1)
print('Test score:', score[0])
print('Test accuracy:', score[1])