yohokuno

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

siemanko

import argparse
import numpy as np
import tensorflow as tf

from tensorflow.python.framework.errors import FailedPreconditionError

"""Code for data dependent initialization in Weight Normalization paper:
https://arxiv.org/abs/1602.07868
"""

artsobolev

Note: I'm updating this gist as I encounter new reviews, so make sure you're reading the latest revision!

Just as the previous year I collected (and keep doing so) links to various summaries and takeaways from this year's NIPS.

• NIPS 2016 Symposium on People and machines: Public views on machine learning, and what this means for machine learning researchers. (Notes and panel discussion) by /u/gcr
• NIPS 2016 summary, wrap up, and links to slides by /u/beamsearch
• Post NIPS Reflections by Neil Lawrence
• [Some general take aways from #NIPS2016](https://medium.com/@IgorCarron/some-general-take-aways-from-ni

justheuristic

ajbrock

# Inception Score Calculator
#
# A Brock, 2017
#
# This snippet assumes you have two functions defined:
# 1. sample_net, which takes in a batch x num_latents random vector and returns batch samples,
# 2. eval_net, which takes in batch samples and returns a batch x #classes prediction vector. 

num_latents = 100

alrojo

from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops import rnn
from tensorflow.python.ops import nn
from tensorflow.python.ops import init_ops

kastnerkyle

# Stacked LSTMs
# Author: Kyle Kastner
# Based on script from /u/siblbombs
# License: BSD 3-Clause
import tensorflow as tf
from tensorflow.models.rnn import rnn
from tensorflow.models.rnn.rnn_cell import LSTMCell
import numpy as np
import time

junpenglao

import numpy
import theano
import theano.tensor as tt
from theano.gradient import disconnected_grad as stop_grad

x = tt.dscalar('x')
y = x ** 2
gy = tt.grad(y, x)

f = theano.function([x], gy)

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

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