Keras实现一个简单的CNN的分类例子

import numpy as np
np.random.seed(1337)

from keras.datasets import  mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense,Activation,Convolution2D,MaxPooling2D,Flatten
from keras.optimizers import Adam

# download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
# X shape (60,000 28x28), y shape (10,000, )
(X_train, y_train), (X_test, y_test) = mnist.load_data()

# data pre-processing，-1 represents the number of samples;1 represents the num of channels,28&28 represents the length,width respectively
X_train = X_train.reshape(-1,1,28,28)  # normalize
X_test = X_test.reshape(-1,1,28,28)    # normalize
y_train = np_utils.to_categorical(y_train,nb_classes=10)
y_test = np_utils.to_categorical(y_test, nb_classes=10)

#build neural network

model=Sequential(
)

model.add(Convolution2D(
nb_filter=32,
nb_col=5,
nb_row=5,
border_mode='same', #padding method
input_shape=(1,     #channels
28,28) #length and width

))

model.add(Activation('relu'))

model.add(MaxPooling2D(
pool_size=(2,2),
strides=(2,2),
border_mode='same', #padding method
))

//这是添加第二层神经网络，卷积层，激励函数，池化层
model.add(Convolution2D(64,5,5,border_mode='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2),border_mode='same'))

//将经过池化层之后的三维特征，整理成一维。方便后面建立全链接层
model.add(Flatten())
//1024像素
model.add(Dense(1024))

model.add(Activation('relu'))
//输出压缩到10维，因为有10个标记
model.add(Dense(10))
//使用softmax进行分类
model.add(Activation('softmax'))

# Another way to define your optimize

adam=Adam(lr=1e-4)

model.compile(
loss='categorical_crossentropy',
optimizer=adam,
metrics=['accuracy'])

print('\nTraining-----------')
model.fit(X_train,y_train,nb_epoch=2,batch_size=32)

print('\nTesting------------')
loss,accuracy=model.evaluate(X_test,y_test)

print('test loss: ', loss)
print('test accuracy: ', accuracy)