# -*- coding: utf-8 -*- import numpy as np np.random.seed(1337) #for reproducibility再现性 from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential#按层 from keras.layers import Dense, Activation,Convolution2D, MaxPooling2D, Flatten import matplotlib.pyplot as plt from keras.optimizers import RMSprop from keras.optimizers import Adam
从mnist下载手写数字图片数据集，图片为28*28，将每个像素的颜色（0到255）改为（0倒1），将标签y变为10个长度，若为1，则在1处为1，剩下的都标为0。
#dowmload the mnisst the path '~/.keras/datasets/' if it is the first time to be called #x shape (60000 28*28),y shape(10000,) (x_train,y_train),(x_test,y_test) = mnist.load_data()#0-9的图片数据集 #data pre-processing x_train = x_train.reshape(-1,1,28,28)#-1代表个数不限，1为高度，黑白照片高度为1 x_test = x_test.reshape(-1,1,28,28) y_train = np_utils.to_categorical(y_train, num_classes=10) #把标签变为10个长度，若为1，则在1处为1，剩下的都标为0 y_test = np_utils.to_categorical(y_test,num_classes=10)
接下来搭建CNN
卷积->池化->卷积->池化
使图片从（1,28,28）->（32,28,28）->（32,14,14）-> (64,14,14) -> (64,7,7）
#Another way to build CNN model = Sequential() #Conv layer 1 output shape (32,28,28) model.add(Convolution2D( nb_filter =32,#滤波器装了32个，每个滤波器都会扫过这个图片，会得到另外一整张图片，所以之后得到的告诉是32层 nb_row=5, nb_col=5, border_mode='same', #padding method input_shape=(1, #channels 通道数 28,28), #height & width 长和宽 )) model.add(Activation('relu')) #Pooling layer 1 (max pooling) output shape (32,14,14) model.add(MaxPooling2D( pool_size=(2,2), #2*2 strides=(2,2), #长和宽都跳两个再pool一次 border_mode='same', #paddingmethod )) #Conv layers 2 output shape (64,14,14) model.add(Convolution2D(64,5,5,border_mode='same')) model.add(Activation('relu')) #Pooling layers 2 (max pooling) output shape (64,7,7) model.add(MaxPooling2D(pool_size=(2,2), border_mode='same'))
构建全连接神经网络
#Fully connected layer 1 input shape (64*7*7) = (3136) #Flatten 把三维抹成一维，全连接 model.add(Flatten()) model.add(Dense(1024)) model.add(Activation('relu')) #Fully connected layer 2 to shape (10) for 10 classes model.add(Dense(10)) #输出10个单位 model.add(Activation('softmax')) #softmax用来分类 #Another way to define optimizer adam = Adam(lr=1e-4) # We add metrics to get more results you want to see model.compile( #编译 optimizer = adam, loss = 'categorical_crossentropy', metrics=['accuracy'], #在更新时同时计算一下accuracy )
训练和测试
print("Training~~~~~~~~") #Another way to train the model model.fit(x_train,y_train, epochs=1, batch_size=32) #训练2大批，每批32个 print("\nTesting~~~~~~~~~~") #Evalute the model with the metrics we define earlier loss,accuracy = model.evaluate(x_test,y_test) print('\ntest loss:',loss) print('\ntest accuracy:', accuracy)
全代码：
# -*- coding: utf-8 -*- import numpy as np np.random.seed(1337) #for reproducibility再现性 from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential#按层 from keras.layers import Dense, Activation,Convolution2D, MaxPooling2D, Flatten import matplotlib.pyplot as plt from keras.optimizers import RMSprop from keras.optimizers import Adam #dowmload the mnisst the path '~/.keras/datasets/' if it is the first time to be called #x shape (60000 28*28),y shape(10000,) (x_train,y_train),(x_test,y_test) = mnist.load_data()#0-9的图片数据集 #data pre-processing x_train = x_train.reshape(-1,1,28,28)#-1代表个数不限，1为高度，黑白照片高度为1 x_test = x_test.reshape(-1,1,28,28) y_train = np_utils.to_categorical(y_train, num_classes=10) #把标签变为10个长度，若为1，则在1处为1，剩下的都标为0 y_test = np_utils.to_categorical(y_test,num_classes=10) #Another way to build CNN model = Sequential() #Conv layer 1 output shape (32,28,28) model.add(Convolution2D( nb_filter =32,#滤波器装了32个，每个滤波器都会扫过这个图片，会得到另外一整张图片，所以之后得到的告诉是32层 nb_row=5, nb_col=5, border_mode='same', #padding method input_shape=(1, #channels 通道数 28,28), #height & width 长和宽 )) model.add(Activation('relu')) #Pooling layer 1 (max pooling) output shape (32,14,14) model.add(MaxPooling2D( pool_size=(2,2), #2*2 strides=(2,2), #长和宽都跳两个再pool一次 border_mode='same', #paddingmethod )) #Conv layers 2 output shape (64,14,14) model.add(Convolution2D(64,5,5,border_mode='same')) model.add(Activation('relu')) #Pooling layers 2 (max pooling) output shape (64,7,7) model.add(MaxPooling2D(pool_size=(2,2), border_mode='same')) #Fully connected layer 1 input shape (64*7*7) = (3136) #Flatten 把三维抹成一维，全连接 model.add(Flatten()) model.add(Dense(1024)) model.add(Activation('relu')) #Fully connected layer 2 to shape (10) for 10 classes model.add(Dense(10)) #输出10个单位 model.add(Activation('softmax')) #softmax用来分类 #Another way to define optimizer adam = Adam(lr=1e-4) # We add metrics to get more results you want to see model.compile( #编译 optimizer = adam, loss = 'categorical_crossentropy', metrics=['accuracy'], #在更新时同时计算一下accuracy ) print("Training~~~~~~~~") #Another way to train the model model.fit(x_train,y_train, epochs=1, batch_size=32) #训练2大批，每批32个 print("\nTesting~~~~~~~~~~") #Evalute the model with the metrics we define earlier loss,accuracy = model.evaluate(x_test,y_test) print('\ntest loss:',loss) print('\ntest accuracy:', accuracy)
输出：
