leconteur

def cumsum(softmax):
  values = tf.split(1, softmax.get_shape()[1], softmax)
  out = []
  prev = tf.zeros_like(values[0])
  for val in values:
      s = prev + val
      out.append(s)
      prev = s
  cumsum = tf.concat(1, out)
  return cumsum

rjpower

import numpy as np

from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout, TimeDistributedDense
from keras.layers.recurrent import LSTM

text = open('/home/russellp/w/data/citation-graph/abstracts.txt', 'r').read()
char_to_idx = { ch: i for (i, ch) in enumerate(sorted(list(set(text)))) }
idx_to_char = { i: ch for (ch, i) in char_to_idx.items() }
vocab_size = len(char_to_idx)

taku-y

eamartin

Mahedi-61

#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

koshian2

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torchvision.utils import save_image
from torchvision.datasets import MNIST, FashionMNIST, CIFAR10, STL10
import os
import pickle