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原创 MyEclipse JSF
1. 前言 这个文档基于下列环境编写: Sun JDK 1.5.0, Eclipse 3.1 和 MyEclipse 4.0. 假如你注重到这个文档和你正在操作的Eclipse/MyEclipse的版本不一致, 在使用这个向导时碰到困难或者无法根据向导进行操作, 请参考 用户反馈 部分来了解如何汇报这个问题. 2. 介绍 在这个教程中我们将使用MyEclipse Enterprise Workb
2008-03-06 23:15:00
1006
RedisClient.zip
redisclient-win32.x86_64.2.0.jar
For 64 bit windows
Download the runable jar file redisclient-win32.x86_64.2.0.jar
Run the redisclient-win32.x86_64.2.0.jar
You can run it by double clicking it if your registry for jar file is configured correctly.
Or you can run it from command line, and input java
2020-09-10
MNIST_data
#http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html
import tensorflow as tf
import tensorflow.examples.tutorials.mnist.input_data as input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder("float", [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder("float", [None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
2018-01-03
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