# -*- 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 methodinput_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*2strides=(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)

# -*- 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 methodinput_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*2strides=(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)

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