nikitakit

"""
Beam decoder for tensorflow

Sample usage:

```
from tf_beam_decoder import  beam_decoder

decoded_sparse, decoded_logprobs = beam_decoder(
    cell=cell,

machinaut

#!/usr/bin/env python
from itertools import combinations, chain
import numpy as np
from pyhull.convex_hull import ConvexHull
from stl import Mode
from stl.mesh import Mesh


def subdivide(shape):
    ''' Take a triangulated sphere and subdivide each face. '''

poolio

rtqichen

## Weight norm is now added to pytorch as a pre-hook, so use that instead :)

import torch
import torch.nn as nn
from torch.nn import Parameter
from functools import wraps

class WeightNorm(nn.Module):
    append_g = '_g'
    append_v = '_v'

killeent

A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

1. How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
2. How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
3. How does PyTorch cwrap work to generate code for Tensor methods?
4. How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.

alexmlamb

import numpy as np
import torch
import random
from torch.autograd import Variable, grad
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt

gngdb

Newmu

import os
import numpy as np
from matplotlib import pyplot as plt
from time import time

from foxhound import activations
from foxhound import updates
from foxhound import inits
from foxhound.theano_utils import floatX, sharedX

mrdrozdov

from tf_logger import TFLogger


""" Example of using TFLogger to save train & dev statistics. To visualize
    in tensorboard simply do:
    
        tensorboard --logdir /path/to/summaries
        
    This code does depend on Tensorflow, but does not require that your model
    is built using Tensorflow. For instance, could build a model in Chainer, then

yaroslavvb

import numpy as np
import tensorflow as tf
import scipy
from tensorflow.contrib.eager.python import tfe
tfe.enable_eager_execution()

# manual numpy example
# X = np.array(([[0., 1], [2, 3]]))
# W0 = X
# W1 = np.array(([[0., 1], [2, 3]]))/10