| #!/usr/bin/env bash | |
| # gdrive_download | |
| # | |
| # script to download Google Drive files from command line | |
| # not guaranteed to work indefinitely | |
| # taken from Stack Overflow answer: | |
| # http://stackoverflow.com/a/38937732/7002068 | |
| gURL=$1 |
| # use the latest ubuntu environment (18.04) available on travis | |
| dist: bionic | |
| language: go | |
| # You don't need to test on very old versions of the Go compiler. It's the user's | |
| # responsibility to keep their compiler up to date. | |
| go: | |
| - 1.16.x |
| // Tensorflow Serving Go client for the inception model | |
| // go get github.com/golang/protobuf/ptypes/wrappers google.golang.org/grpc | |
| // | |
| // Compile the proto files: | |
| // | |
| // git clone https://github.com/tensorflow/serving.git | |
| // git clone https://github.com/tensorflow/tensorflow.git | |
| // | |
| // mkdir -p vendor |
| import logging | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow.contrib import layers | |
| GO_TOKEN = 0 | |
| END_TOKEN = 1 | |
| UNK_TOKEN = 2 |
After a weekend of research, stress and pain I finally figure out how to install manjaro 17 and configure the nvidia/bumblebee drivers on my avell laptop
Here's my notebook specs:
$ inxi -MGCNA
Machine: Device: laptop System: Avell High Performance product: 1513
Mobo: N/A model: N/A v: 0.1 UEFI: American Megatrends v: N.1.02 date: 09/28/2016
Battery BAT0: charge: 44.0 Wh 100.0% condition: 44.0/44.0 Wh (100%)
| """ | |
| A bare bones examples of optimizing a black-box function (f) using | |
| Natural Evolution Strategies (NES), where the parameter distribution is a | |
| gaussian of fixed standard deviation. | |
| """ | |
| import numpy as np | |
| np.random.seed(0) | |
| # the function we want to optimize |
Problem 4.9. BST Sequences: A binary search tree was created by traversing through an array from left to right and inserting each element. Given a binary search tree with distinct elements, print all possible arrays that could have led to this tree.
Solution.
Let's start with an example.
4
/ \
2 5 | #!/usr/bin/env python | |
| """ | |
| A quick, partial implementation of ENet (https://arxiv.org/abs/1606.02147) using PyTorch. | |
| The original Torch ENet implementation can process a 480x360 image in ~12 ms (on a P2 AWS | |
| instance). TensorFlow takes ~35 ms. The PyTorch implementation takes ~25 ms, an improvement | |
| over TensorFlow, but worse than the original Torch. | |
| """ | |
| from __future__ import absolute_import |
| """Simple example on how to log scalars and images to tensorboard without tensor ops. | |
| License: BSD License 2.0 | |
| """ | |
| __author__ = "Michael Gygli" | |
| import tensorflow as tf | |
| from StringIO import StringIO | |
| import matplotlib.pyplot as plt | |
| import numpy as np |
