DarinM223

My explanation of the main concepts in Rust

There are three main concepts with Rust:

1. Ownership (only one variable "owns" the data at one time, and the owner is in charge of deallocating)
2. Borrowing (you can borrow a reference to an owned variable)
3. Lifetimes (all data keeps track of when it will be destroyed)

These are fairly simple concepts, but they are often counter-intuitive to concepts in other languages, so I wanted to give a shot at

et2010

(setq url-gateway-method 'socks)
(setq socks-server '("Default server" "127.0.0.1" 1080 5))

gabrieleangeletti

import tensorflow as tf
import numpy as np
import os

import zconfig
import utils

class DenoisingAutoencoder(object):

    """ Implementation of Denoising Autoencoders using TensorFlow.

evanthebouncy

import tensorflow as tf    
import numpy as np

if __name__ == '__main__':
  np.random.seed(1)  
  # the size of the hidden state for the lstm (notice the lstm uses 2x of this amount so actually lstm will have state of size 2)
  size = 1
  # 2 different sequences total
  batch_size= 2
  # the maximum steps for both sequences is 10

myme5261314

import tensorflow as tf
import numpy as np
import input_data
import Image
from util import tile_raster_images


def sample_prob(probs):
    return tf.nn.relu(
        tf.sign(

alexgorban

"""Illustration for various types of namespace scopes in TensorFlow.

> python tf_scopes.py
foo_name_scoped :
	v.name= v:0
	v2.name= foo_name_scoped/v2:0
	a.name= Variable:0
	b.name= Variable_1:0
	result_op.name= foo_name_scoped/Add:0
foo_op_scoped :

frnhr

####
#### pyenv-virtualenv bash prompt customization
####


# pyenv
eval "$(pyenv init -)"

# pyenv-virtualenv:

udibr

# variation to https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py

def keras_rnn_predict(samples, empty=empty, rnn_model=model, maxlen=maxlen):
    """for every sample, calculate probability for every possible label
    you need to supply your RNN model and maxlen - the length of sequences it can handle
    """
    data = sequence.pad_sequences(samples, maxlen=maxlen, value=empty)
    return rnn_model.predict(data, verbose=0)

def beamsearch(predict=keras_rnn_predict,

bmhatfield

# In order for gpg to find gpg-agent, gpg-agent must be running, and there must be an env
# variable pointing GPG to the gpg-agent socket. This little script, which must be sourced
# in your shell's init script (ie, .bash_profile, .zshrc, whatever), will either start
# gpg-agent or set up the GPG_AGENT_INFO variable if it's already running.

# Add the following to your shell init to set up gpg-agent automatically for every shell
if [ -f ~/.gnupg/.gpg-agent-info ] && [ -n "$(pgrep gpg-agent)" ]; then
    source ~/.gnupg/.gpg-agent-info
    export GPG_AGENT_INFO
else

danijar

# Example for my blog post at:
# https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
import functools
import sets
import tensorflow as tf


def lazy_property(function):
    attribute = '_' + function.__name__