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


(train_images, train_labels),(test_images,test_labels)=mnist.load_data()

network=models.Sequential()
network.add(layers.Dense(512,activation='relu',input_shape=(28*28,)))
network.add(layers.Dense(10,activation='softmax'))

network.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])

train_images=train_images.reshape((60000,28*28))
train_images=train_images.astype('float32')/255

test_images=test_images.reshape((10000,784))
test_images=test_images.astype('float32')/255


from keras.utils import to_categorical

train_labels=to_categorical(train_labels)
test_labels=to_categorical(test_labels)

network.fit(train_images,train_labels,epochs=5,batch_size=3000)

test_loss,test_acc=network.evaluate(test_images,test_labels)
print('test_acc',test_acc)
