Keras是由纯python编写的基于theano/tensorflow的深度学习框架。

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Demo:

import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense,Dropout,Activation
from keras.layers import Conv2D,MaxPooling2D,Flatten
from keras.optimizers import SGD,Adam
from keras.utils import np_utils
from keras.datasets import mnist

def load_data():
    (x_train,y_train),(x_test,y_test)=mnist.load_data()
    number=10000
    x_train=x_train[0:number]
    y_train=y_train[0:number]
    x_train=x_train.reshape(number,28*28)
    x_test=x_test.reshape(x_test.shape[0],28*28)
    x_train=x_train.astype('float32')
    x_test=x_test.astype('float32')
    y_train=np_utils.to_catagorical(y_train,10)
    y_test=np_utils.to_categorical(y_test,10)
    x_train=x_train/255
    x_test=x_test/255
    return (x_train,y_train),(x_test,y_test)

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

model=Sequential()
model.add(Dense(input_dim=28*28,units=633,activation='sigmoid'))
for i in range(10):
    model.add(Dense(units=633,activation='sigmoid'))

model.compile(loss='mse',optimizer=SGD(lr=0.1),metrics=['accuracy'])
model.fit(x_train,y_train,batch_size=100,epochs=20)
result=model.evaluate(x_test,y_test)
print(result)