iganichev

#!/usr/bin/env python
import sys

import tensorflow as tf
import numpy as np

from tensorflow.examples.tutorials.mnist import input_data

class Model(object):
  def __init__(self, x, with_new_layer=False):

iganichev

#!/usr/bin/env python

""" This example trains an LSTM to predict the next number
in a sequence 0, 0, 1, 1, 0, 0, 1, 1, ...
Training is done on very short sequences of length 3.
Loss is computed on all but the last predicted value for simplicity.

When REMEMBER_STATE is True, the previous LSTM state is transfered
to the next training step and the network reaches almost zero error.
When REMEMBER STATE is False, the network has no way to predict the

iganichev

#include <assert.h>

#include <algorithm>
#include <iostream>
#include <numeric>
#include <vector>

using namespace std;

bool next_perm(vector<int>& v) {