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