michaelklachko

import numpy as np
import tensorflow as tf
import os

os.environ['TF_CPP_MIN_LOG_LEVEL']='2'


def build_mask(fs, dim, debug=False):

    mask = np.zeros((fs*fs, dim, dim))   #this mask will be applied to each group in conv_out

michaelklachko

# -*- coding: utf-8 -*-
"""
Created on Sun Jun 11 15:22:27 2017

@author: Modification of https://github.com/spro/practical-pytorch/blob/master/char-rnn-generation/char-rnn-generation.ipynb by Michael Klachko

Changes:
    - added batch support
    - added multi-GPU support
    - minor changes to train code

michaelklachko

"""
Created on Sun Jun 11 15:22:27 2017

@author: Modification of https://github.com/spro/practical-pytorch/blob/master/char-rnn-generation/char-rnn-generation.ipynb by Michael Klachko

Changes:
    - added batch support
    - added multi-GPU support
    - minor changes to train code
    - removed Unicode support (assume input.txt is ASCII)

michaelklachko

# -*- coding: utf-8 -*-
"""
Created on Sun Jun 11 15:22:27 2017

@author: Modification of https://github.com/spro/practical-pytorch/blob/master/char-rnn-generation/char-rnn-generation.ipynb by Michael Klachko

Changes:
    - added batch support
    - added multi-GPU support
    - minor changes to train code

michaelklachko

"""
Created on Sun Jun 11 15:22:27 2017

@author: Modification of https://github.com/spro/practical-pytorch/blob/master/char-rnn-generation/char-rnn-generation.ipynb by Michael Klachko

Changes:
    - added batch support
    - added multi-GPU support
    - minor changes to train code
    - removed Unicode support (assume input.txt is ASCII)

michaelklachko

import random
import torch
import torch.nn as nn
from torch.autograd import Variable

text = open('input.txt', 'r').read()
file_len = len(text)
all_chars = ''.join(set(text))
n_chars = len(all_chars)