import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
# flatten 扁平化
from keras.layers import Dense,Dropout,Convolution2D,MaxPooling2D,Flatten
from keras.optimizers import Adam

(x_train,y_train),(x_test,y_test) = mnist.load_data()

x_train = x_train.reshape(-1,28,28,1)/255.0
x_test = x_test.reshape(-1,28,28,1)/255.0

y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)

# 定义顺序模型
model = Sequential()

# 第一个卷积层
# input_shape 输入平面
# filters卷积核、滤波器个数
# kernel_size 卷积窗口大小
# strides 步长
# padding padding方式 same/valid
# activation 激活函数
model.add(Convolution2D(
    input_shape=(28, 28, 1),
    filters=32,
    kernel_size=5,
    strides=1,
    padding='same',
    activation='relu'
))

# 第一个池化层
model.add(MaxPooling2D(
    pool_size=2,
    strides=2,
    padding='same'
))

# 第二个卷积层
model.add(Convolution2D(64,5,strides=1,padding='same',activation='relu'))

# 第二个池化层
model.add(MaxPooling2D(2, 2, 'same'))

# 把第二个池化层的输出扁平化为1维
model.add(Flatten())

# 第一个全连接层
model.add(Dense(1024, activation='relu'))
# Dropout
model.add(Dropout(0.5))

# 第二个全连接层
model.add(Dense(10, activation='softmax'))

# 定义优化器
adam = Adam(lr=1e-4)

# 定义优化器 loss function 训练过程中的计算准确率
model.compile(optimizer=adam,loss='categorical_crossentropy',metrics=['accuracy'])

# 训练模型
model.fit(x_train,y_train,batch_size=64,epochs=10)

# 评估模型
loss, accuracy = model.evaluate(x_test, y_test)
print('test loss', loss)
print('test accuracy', accuracy)
