| int[][] result; | |
| float t, c; | |
| float ease(float p) { | |
| return 3*p*p - 2*p*p*p; | |
| } | |
| float ease(float p, float g) { | |
| if (p < 0.5) | |
| return 0.5 * pow(2*p, g); |
| // 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 |
| # -*- coding: utf-8 -*- | |
| # /var/runtime/awslambda/bootstrap.py | |
| """ | |
| aws_lambda.bootstrap.py | |
| Amazon Lambda | |
| Copyright (c) 2013 Amazon. All rights reserved. | |
| Lambda runtime implemention | |
| """ |
| """ | |
| Example TensorFlow script for finetuning a VGG model on your own data. | |
| Uses tf.contrib.data module which is in release v1.2 | |
| Based on PyTorch example from Justin Johnson | |
| (https://gist.github.com/jcjohnson/6e41e8512c17eae5da50aebef3378a4c) | |
| Required packages: tensorflow (v1.2) | |
| Download the weights trained on ImageNet for VGG: | |
| ``` | |
| wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz |
FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.
| def convex_hull_graham(points): | |
| ''' | |
| Returns points on convex hull in CCW order according to Graham's scan algorithm. | |
| By Tom Switzer <thomas.switzer@gmail.com>. | |
| ''' | |
| TURN_LEFT, TURN_RIGHT, TURN_NONE = (1, -1, 0) | |
| def cmp(a, b): | |
| return (a > b) - (a < b) |
| #include <cassert> | |
| #include <iostream> | |
| #include <vector> | |
| #include <type_traits> | |
| // http://www.embedded.com/electronics-blogs/programmer-s-toolbox/4219659/Integer-Square-Roots | |
| uint32_t isqrt0(uint32_t n) { | |
| uint32_t delta = 3; | |
| for (uint32_t square = 1; square <= n; delta += 2) |
| """ | |
| This is a batched LSTM forward and backward pass | |
| """ | |
| import numpy as np | |
| import code | |
| class LSTM: | |
| @staticmethod | |
| def init(input_size, hidden_size, fancy_forget_bias_init = 3): |
| def test_root_output(client): | |
| resp = client.fetch('/') | |
| assert resp.code == 200 | |
| assert resp.body == 'Hello, world' | |
| def test_async_output(client): | |
| resp = client.fetch('/async') | |
| assert resp.code == 200 |
