| def mmul(*tensors): | |
| return tf.foldl(tf.matmul, tensors) | |
| def msym(X): | |
| return (X + tf.matrix_transpose(X)) / 2 | |
| def mdiag(X): | |
| return tf.matrix_diag(tf.matrix_diag_part(X)) | |
| @tf.RegisterGradient('Svd') |
| # -*- coding: utf-8 -*- | |
| """ | |
| | **@created on:** 11/05/17, | |
| | **@author:** Prathyush SP, | |
| | **@version:** v0.0.1 | |
| | | |
| | **Description:** | |
| | DL Module Tests | |
| | **Sphinx Documentation Status:** Complete | |
| | |
Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.
This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would
| import tensorflow as tf | |
| from tensorflow.python.framework import ops | |
| import numpy as np | |
| # Define custom py_func which takes also a grad op as argument: | |
| def py_func(func, inp, Tout, stateful=True, name=None, grad=None): | |
| # Need to generate a unique name to avoid duplicates: | |
| rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8)) | |