I hereby claim:
- I am mirceamironenco on github.
- I am mirceamironenco (https://keybase.io/mirceamironenco) on keybase.
- I have a public key whose fingerprint is AE6D E27B 8985 EA4B 1082 A35B EDFF DD1F 075B 2BAB
To claim this, I am signing this object:
| include(FetchContent) | |
| set(CMAKE_CXX_STANDARD_REQUIRED ON) | |
| set(CMAKE_CXX_EXTENSIONS ON) | |
| set(CMAKE_EXPORT_COMPILE_COMMANDS ON) | |
| if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin") | |
| set(MACOSX_FOUND TRUE) | |
| endif() |
| from __future__ import annotations | |
| import dataclasses | |
| import pathlib | |
| import sys | |
| import warnings | |
| from collections import deque | |
| from collections.abc import Sequence | |
| from typing import ( | |
| Annotated, |
| gcloud compute instances create mircea \ | |
| --min-cpu-platform "Intel Broadwell" \ | |
| --machine-type n1-standard-1 --zone europe-west1-b \ | |
| --boot-disk-size 500GB --boot-disk-type=pd-ssd\ | |
| --accelerator type=nvidia-tesla-k80,count=1 \ | |
| --image-family ubuntu-1604-lts --image-project ubuntu-os-cloud \ | |
| --maintenance-policy TERMINATE --restart-on-failure \ | |
| --metadata startup-script='#!/bin/bash | |
| echo "Checking for CUDA and installing." | |
| # Check for CUDA and try to install. |
I hereby claim:
To claim this, I am signing this object:
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import inspect | |
| import time | |
| import numpy as np | |
| import tensorflow as tf |
| class Layer(object): | |
| def __init__(self, scope="dense_layer"): | |
| self.scope = scope | |
| def __call__(self, x, **kwargs): | |
| with tf.name_scope(self.scope): | |
| return self.output(x, **kwargs) | |
| def output(self, x, **kwargs): | |
| raise NotImplementedError() |
| def loss(self, mc_logits, y_true): | |
| """ | |
| mc_logits is batch_size x num_k_samples x num_classes | |
| y_true is batch_size x num_classes | |
| self.k_mc is the number of samples used. | |
| self.alpha is the alpha-divergence parameter | |
| (0-VI,0.5-Hellinger,1.0-EP) | |
| """ | |
| mc_log_softmax = mc_logits - tf.reduce_max(mc_logits, axis=2, | |
| keep_dims=True) |