jfsantos

creating an lstm with 2 layers
setting forget gate biases to 1 in LSTM layer 1
setting forget gate biases to 1 in LSTM layer 2
number of parameters in the model: 240321
cloning rnn
cloning criterion
/Users/jfsantos/torch/install/bin/luajit: /Users/jfsantos/torch/install/share/lua/5.1/nn/Identity.lua:13: bad argument #1 to 'set' (expecting number or Tensor or Storage at /tmp/luarocks_torch-scm-1-8315/torch7/generic/Tensor.c:1089)
stack traceback:
	[C]: in function 'set'
	/Users/jfsantos/torch/install/share/lua/5.1/nn/Identity.lua:13: in function 'func'

jfsantos

signal = require "signal"
complex = require "signal.complex"

function istft(X, win, hop)
    local x = torch.zeros((X:size(1)-1)*hop + win)
    framesamp = X:size(2)
    hopsamp = hop
    for n=1,X:size(1) do
        i = 1 + (n-1)*hopsamp
        print(i, i + framesamp - 1)

jfsantos

model = nn.Sequential()

lstm = nn.Sequencer(
  nn.Sequential()
  :add(nn.LSTM(nFeatures,nHidden))
  :add(nn.Dropout())
  :add(nn.LSTM(nHidden,nHidden))
)

lstm:remember('neither') -- force model to call forget at each call to forward

jfsantos

from __future__ import print_function

from keras.models import Model, Sequential
from keras.layers import Input, Dense, TimeDistributed
from keras.layers.core import Reshape, Flatten, Dropout, TimeDistributedDense
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Convolution2D
from keras.layers.recurrent import LSTM
from keras.optimizers import Adam

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')

jfsantos

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

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

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

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

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