from keras import layers
from keras import models
from keras.utils import to_categorical
from keras.datasets import mnist

#------------------model-------------------
model = models.Sequential()
model.add(layers.Conv2D(32,(3,3),activation ='relu',input_shape=(28,28,1)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,(3,3),activation ='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,(3,3),activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64,activation='relu'))
model.add(layers.Dense(10,activation='softmax'))
#-----------------tap train-------------------
(train_images,train_labels) ,(test_images,test_labels)= mnist.load_data()
train_images =  train_images.reshape((60000,28,28,1))
train_images = train_images.astype('float32')/255

test_images = test_images.reshape((10000,28,28,1))
test_images = test_images.astype('float32')/255

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])
model.fit(train_images,train_labels,epochs=5,batch_size=64)
model.save('train.h5')
#------------------test-----------------------
