基于Anaconda+Keras的深度学习入门程序
0赞#Anaconda 3.X上安装Keras
pip install keras #后面提示theano缺少g++,性能下降的Warning,暂未解决
#选择Keras理由:python,开源
#bing搜出一篇Keras入门,有实例入门程序,跑通无问题
程序如下:
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 17 10:30:40 2016
@author: Administrator
"""
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, nb_epoch=150, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))