11 Matching Annotations
  1. Feb 2019
  2. May 2018
  3. Oct 2017
    1. TensorFlow provides optimizers that slowly change each variable in order to minimize the loss function. The simplest optimizer is gradient descent. It modifies each variable according to the magnitude of the derivative of loss with respect to that variable

      Using Optimizer to auto estimate the parameters

      optimizer = tf.train.GradientDescentOptimizer(0.01)
      train = optimizer.minimize(loss)
      sess.run(init)  # reset values to incorrect defaults.
      for i in range(1000):
          sess.run(
              train, {x: [1, 2, 3, 4], y: [0, -1, -2, -3]})
      print("...", sess.run([W, b]))
      
  4. Sep 2017
  5. Jul 2017
    1. 这张图给出了谷歌在2015年提出的Inception-v3模型。这个模型在ImageNet数据集上可以达到95%的正确率。然而,这个模型中有2500万个参数,分类一张图片需要50亿次加法或者乘法运算。

      95%成功率,需要 25,000,000个参数!

  6. Apr 2017
    1. J(t)NEG=logQθ(D=1|the, quick)+log(Qθ(D=0|sheep, quick))

      Expression to learn theta and maximize cost and minimize the loss due to noisy words. Expression means -> probability of predicting quick(source of context) from the(target word) + non probability of sheep(noise) from word

    1. If we write that out as equations, we get:

      It would be easier to understand what are x and y and W here if the actual numbers were used, like 784, 10, 55000, etc. In this simple example there are 3 x and 3 y, which is misleading. In reality there are 784 x elements (for each pixel) and 55,000 such x arrays and only 10 y elements (for each digit) and then 55,000 of them.

  7. Mar 2017
    1. If you would like TensorFlow to automatically choose an existing and supported device to run the operations in case the specified one doesn't exist, you can set allow_soft_placement to True in the configuration option when creating the session.

      为了保证op按照自己的要求分配,必须设置为False

  8. Nov 2015
    1. Google is merely interested sharing the code. As Dean says, this will help the company improve this code. But at the same time, says Monga, it will also help improve machine learning as a whole, breeding all sorts of new ideas. And, well, these too will find their way back into Google. “Any advances in machine learning,” he says, “will be advances for us as well.”