jfsantos

import glob
import logging
import os
import numpy as np
import re
import soundfile
from numpy.lib.stride_tricks import as_strided
from maracas.maracas import asl_meter
from audio_tools import iterate_invert_spectrogram

jfsantos

import torch
from torch.autograd import Variable

import numpy as np
import pickle

import os
from glob import glob
from tqdm import tqdm

jfsantos

-- Build files have been written to: /home/jfsantos/pytorch/torch/lib/build/libshm
[ 50%] Built target torch_shm_manager
[ 75%] Building CXX object CMakeFiles/shm.dir/core.cpp.o
/home/jfsantos/pytorch/torch/lib/libshm/core.cpp:149:1: error: invalid conversion from 'void* (*)(void*, long int)' to 'void* (*)(void*, ptrdiff_t) {aka void* (*)(void*, int)}' [-fpermissive]
 };
 ^
/home/jfsantos/pytorch/torch/lib/libshm/core.cpp:149:1: error: invalid conversion from 'void* (*)(void*, void*, long int)' to 'void* (*)(void*, void*, ptrdiff_t) {aka void* (*)(void*, void*, int)}' [-fpermissive]
CMakeFiles/shm.dir/build.make:62: recipe for target 'CMakeFiles/shm.dir/core.cpp.o' failed
make[2]: *** [CMakeFiles/shm.dir/core.cpp.o] Error 1
CMakeFiles/Makefile2:67: recipe for target 'CMakeFiles/shm.dir/all' failed

jfsantos

from __future__ import division

import multiprocessing
import scipy.spatial.distance
import numpy as np
import sklearn.datasets
from time import time
from multiprocessing import Pool
from itertools import combinations

jfsantos

def train_fn(model, optimizer, criterion, batch):
    x, y, lengths = batch

    x = Variable(x.cuda())
    y = Variable(y.cuda(), requires_grad=False)

    mask = Variable(torch.ByteTensor(x.size()).fill_(1).cuda(),
            requires_grad=False)
    for k, l in enumerate(lengths):
        mask[:l, k, :] = 0

jfsantos

from torch.utils.data import Dataset

class DummyDataset(Dataset):

    def __init__(self, items):
        super(DummyDataset, self).__init__()
        self.items = items

    def __getitem__(self, index):
        return self.items[index]

jfsantos

Agility

&{template:default} {{name=@{selected|character_name}}}{{Agility roll=[[1d20 + @{selected|agility_mod} + [[?{# Boons|0} - ?{# Banes|0}]]d6k1]]}}

Intellect

&{template:default} {{name=@{selected|character_name}}}{{Intellect roll=[[1d20 + @{selected|intellect_mod} + [[?{# Boons|0} - ?{# Banes|0}]]d6k1]]}}

Perception

jfsantos

'''
A logistic regression example using the meta-graph checkpointing
features of Tensorflow.

Author: João Felipe Santos, based on code by Aymeric Damien
(https://github.com/aymericdamien/TensorFlow-Examples/)
'''

from __future__ import print_function

jfsantos

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

jfsantos

from keras.models import Sequential
from keras.layers import Dense
from keras.utils.io_utils import HDF5Matrix
import numpy as np

def create_dataset():
    import h5py
    X = np.random.randn(200,10).astype('float32')
    y = np.random.randint(0, 2, size=(200,1))
    f = h5py.File('test.h5', 'w')