4-1卷积神经网络(CNN)--Keras实现

点击查看完整代码http://www.daimapi.com/neuralnetwork4_1/

该代码利用Python3实现，利用到了深度学习工具包Keras。

Keras是一个高层神经网络API，Keras由纯Python编写而成并基Tensorflow、Theano以及CNTK后端。Keras的主要特点：1.简易和快速的原型设计（keras具有高度模块化，极简，和可扩充特性）；2.支持CNN和RNN，或二者的结合；3.无缝CPU和GPU切换。

# -*- coding: utf-8 -*-
import numpy as np
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

# 全局变量
batch_size = 128
nb_classes = 10
epochs = 12
# 输入图像的尺寸
img_rows, img_cols = 28, 28
# 卷积滤波器的使用次数
nb_filters = 32
# 最大池合并池尺寸
pool_size = (2, 2)
# 卷积核尺寸
kernel_size = (3, 3)

# 训练集合和测试集合
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# 根据不同的backend定下不同的格式
if K.image_dim_ordering() == 'th':
    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    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')

# 转换为one_hot类型
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)