developer-mayuan

"=============================================================================
" dark_powered.vim --- Dark powered mode of SpaceVim
" Copyright (c) 2016-2017 Wang Shidong & Contributors
" Author: Wang Shidong < wsdjeg at 163.com >
" URL: https://spacevim.org
" License: GPLv3
"=============================================================================


" SpaceVim Options: {{{

developer-mayuan

def dataset_input_fn(is_training, data_dir, batch_size, num_epochs=1):
    """Input function which provides batches for train or eval.
    """
    dataset = tf.data.Dataset.from_tensor_slices(_filenames(is_training,
                                                            data_dir))

    if is_training:
        dataset = dataset.shuffle(buffer_size=_FILE_SHUFFLE_BUFFER)

    dataset = dataset.flat_map(tf.data.TFRecordDataset)

developer-mayuan

git config --global core.editor "vim"

developer-mayuan

upstream websocket {
    server localhost:3000;
}

server {
    listen       80;
    server_name  localhost;

    access_log  /var/log/nginx/websocket.access.log  main;

developer-mayuan

# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

developer-mayuan

'atom-text-editor.vim-mode-plus.insert-mode':
  'j j': 'vim-mode-plus:activate-normal-mode'
  'j k': 'vim-mode-plus:activate-normal-mode'

'atom-text-editor[mini]':
  'j j': 'core:cancel'
  'j k': 'core:cancel'

developer-mayuan

# Working example for my blog post at:
# https://danijar.github.io/structuring-your-tensorflow-models
import functools
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data


def doublewrap(function):
    """
    A decorator decorator, allowing to use the decorator to be used without

developer-mayuan

    If this is your first install of dbus, automatically load on login with:
        mkdir -p ~/Library/LaunchAgents
        cp /Users/mayuan-mabuchi/Ordnance/anaconda3/envs/Hopenet/org.freedesktop.dbus-session.plist ~/Library/LaunchAgents/
        launchctl load -w ~/Library/LaunchAgents/org.freedesktop.dbus-session.plist

    If this is an upgrade and you already have the org.freedesktop.dbus-session.plist loaded:
        launchctl unload -w ~/Library/LaunchAgents/org.freedesktop.dbus-session.plist
        cp /Users/mayuan-mabuchi/Ordnance/anaconda3/envs/Hopenet/org.freedesktop.dbus-session.plist ~/Library/LaunchAgents/
        launchctl load -w ~/Library/LaunchAgents/org.freedesktop.dbus-session.plist

developer-mayuan

def bottleneck_block(inputs, filters, is_training, projection_shortcut, strides,
                     data_format):
    """
    Bottleneck block variant for residual networks with BN before convolutions.
    :param inputs: A tensor of size
    :param filters: The number of filers for the first two convolutions. Note that the third and final convolution will use 4 times as many filters.
    :param is_training: A Boolean for whether the model is in training or inference mode. Needed for batch normalization.
    :param projection_shortcut: The function to use for projection shortcuts (typcially a 1x1 convolution when downsampling in the input).
    :param strides: The block's stride. If greater than 1, this block will ultimately downsample the input.
    :param data_format: The input format ('channels_last' or 'channels_first').

developer-mayuan

def batch_norm_relu(inputs, is_training, data_format):
    """
    Performs a batch normalization followed by a ReLU.
    :param inputs:
    :param is_training:
    :param data_format:
    :return:
    """
    inputs = tf.layers.batch_normalization(
        inputs=inputs, axis=1 if data_format == 'channels_first' else 3,