TensorFlow&Theano&Kerash环境测试代码

#-*- coding:utf-8 -*-
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

global mnist

#远程下载MNIST数据，建议先下载好并保存在MNIST_data目录下
def DownloadData():
global mnist
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)  #编码格式：one-hot
print(mnist.train.images.shape, mnist.train.labels.shape)
print(mnist.test.images.shape, mnist.test.labels.shape)
print(mnist.validation.images.shape, mnist.validation.labels.shape)

def Train():
sess = tf.InteractiveSession()

#Step 1
#定义算法公式Softmax Regression
x = tf.placeholder(tf.float32, [None, 784])            #构建占位符，代表输入的图像，None表示样本的数量可以是任意的
W = tf.Variable(tf.zeros([784,10]))                    #构建一个变量，代表训练目标weights，初始化为0
b = tf.Variable(tf.zeros([10]))                        #构建一个变量，代表训练目标biases，初始化为0
y = tf.nn.softmax(tf.matmul(x, W) + b)                 #构建了一个softmax的模型：y = softmax(Wx + b)，y指样本标签的预测值

#Step 2
#定义损失函数，选定优化器，并指定优化器优化损失函数
y_ = tf.placeholder(tf.float32, [None, 10])             # 构建占位符，代表样本标签的真实值
# 交叉熵损失函数
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
#y = tf.matmul(x, W) + b
#cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))

# 使用梯度下降法（0.01的学习率）来最小化这个交叉熵损失函数
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

#Step 3
#使用随机梯度下降训练数据
tf.global_variables_initializer().run()
for i in range(1000):                                                  #迭代次数为1000
batch_xs, batch_ys = mnist.train.next_batch(100)                   #使用minibatch的训练数据，一个batch的大小为100
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})        #用训练数据替代占位符来执行训练

#Step 4
#在测试集上对准确率进行评测
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))          #tf.argmax()返回的是某一维度上其数据最大所在的索引值，在这里即代表预测值和真值
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))      #用平均值来统计测试准确率
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))  #打印测试信息

sess.close()

if __name__ == '__main__':
DownloadData();
Train();

from theano import function,config,shared,sandbox
import  theano.tensor as T
import numpy
import  time

vlen = 10 * 30 * 768  # 10 x #cores x # threads per core
iters = 1000

rng = numpy.random.RandomState(22)
x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
f = function([], T.exp(x))
print f.maker.fgraph.toposort()
t0 = time.time()
for i in xrange(iters):
r = f()
t1 = time.time()
print 'Looping %d times took' % iters, t1 - t0, 'seconds'
print 'Result is', r
if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
print 'Used the cpu'
else:
print 'Used the gpu'

# encoding: utf-8
"""
@version: 1.0
@license: Apache Licence
@file: test_keras2.py
@time: 2016/8/17 16:51
"""

import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.optimizers import SGD
from keras.utils import np_utils
from keras.utils.vis_utils import plot_model

def run():
# 构建神经网络
model = Sequential()
model.add(Dense(4, input_dim=2, init='uniform'))
model.add(Activation('relu'))
model.add(Dense(2, init='uniform'))
model.add(Activation('sigmoid'))
sgd = SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])

# 神经网络可视化
plot_model(model, to_file='model.png')

if __name__ == '__main__':
run()