blog 一名工匠的遗憾与骄傲
软件界的硬工 硬件界的码农 http://oarap.org/
人生没有原因,闯出一路精彩
软件界的硬工 硬件界的码农 http://oarap.org/
http://blog.csdn.net/robertsong2004
多机器人系统,机器人三维仿真 http://blog.csdn.net/zhangrelay ROS相关书籍: […]
机器人操作系统(ROS),SLAM,机器人视觉,自然语言处理,模式识别 https://www.cnblogs […]
图像学相关: http://blog.sina.com.cn/s/articlelist_1712413141 […]
很多ROS相关的内容: http://www.guyuehome.com/
有很多opencv相关: http://blog.csdn.net/abcd1992719g/article/ […]
http://www.cnblogs.com/benwu/p/4132026.html http://www. […]
安装插件:html-css-js prettify 快捷 :选中要格式化的代码, Ctl + Shift + […]
http://blog.csdn.net/lu_embedded
win7系统默认不支持USB3.0设备,需要安装额外驱动来支持。 旧一点的CPU: https://downl […]
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import numpy as np import tensorflow as tf # Model parameters W = tf.Variable([.3], dtype=tf.float32) b = tf.Variable([-.3], dtype=tf.float32) # Model input and output x = tf.placeholder(tf.float32) linear_model = W * x + b y = tf.placeholder(tf.float32) # loss loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares # optimizer optimizer = tf.train.GradientDescentOptimizer(0.01) train = optimizer.minimize(loss) # training data x_train = [1, 2, 3, 4] y_train = [0, -1, -2, -3] # training loop init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) # reset values to wrong for i in range(1000): sess.run(train, {x:x_train, y:y_train}) # evaluate training accuracy curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x:x_train, y:y_train}) print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss)) |
运行: [crayon-651d6463cd […]
https://www.tensorflow.org/get_started/get_started http […]
点积:在数学中,数量积(dot product; scalar product,也称为点积)是接受在实数R上的 […]