Programing with Deep Neural Networks dneuraln

## TheNumericsOfGANs.ipynb

      
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                dneuraln
                / TheNumericsOfGANs.ipynb
            
            
              Created
              October 8, 2017 16:41
                — forked from LMescheder/TheNumericsOfGANs.ipynb
            
              
                This notebook contains the code for the toy experiment in the paper The Numerics of GANs.
              
          
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## tf_lstm.py
"""Short and sweet LSTM implementation in Tensorflow.

Motivation:
When Tensorflow was released, adding RNNs was a bit of a hack - it required
building separate graphs for every number of timesteps and was a bit obscure
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
Currently the APIs are decent, but all the tutorials that I am aware of are not
making the best use of the new APIs.

Advantages of this implementation:

## Amortized_Explaining_Away_Demo.ipynb

      
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                dneuraln
                / Amortized_Explaining_Away_Demo.ipynb
            
            
              Created
              February 3, 2017 08:49
                — forked from fhuszar/Amortized_Explaining_Away_Demo.ipynb
            
          
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## bayes_by_backprop.py
import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from theano.sandbox.rng_mrg import MRG_RandomStreams
from lasagne.updates import adam
from lasagne.utils import collect_shared_vars

from sklearn.datasets import fetch_mldata
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing

## blog_tensorflow_variable_sequence_labelling.py
# Working example for my blog post at:
# http://danijar.com/variable-sequence-lengths-in-tensorflow/
import functools
import sets
import tensorflow as tf
from tensorflow.models.rnn import rnn_cell
from tensorflow.models.rnn import rnn


def lazy_property(function):

## train_imagenet_32x32_gan.py
import sys
sys.path.append('..')

import os
import json
from time import time
import numpy as np
from tqdm import tqdm
from matplotlib import pyplot as plt
from sklearn.externals import joblib

## mdn_demo_theano.py
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 22 00:36:48 2016

@author: park
"""

#%% train

import theano

## conv_deconv_vae.py
# Alec Radford, Indico, Kyle Kastner
# License: MIT
"""
Convolutional VAE in a single file.
Bringing in code from IndicoDataSolutions and Alec Radford (NewMu)
Additionally converted to use default conv2d interface instead of explicit cuDNN
"""
import theano
import theano.tensor as T
from theano.compat.python2x import OrderedDict

## conv_deconv_variational_autoencoder.py
import theano
import theano.tensor as T
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
from theano.tensor.signal.downsample import max_pool_2d
from theano.tensor.extra_ops import repeat
from theano.sandbox.cuda.dnn import dnn_conv

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

## gputest.lua
-- Max Jaderberg 4/9/13
-- GPU Effectiveness test

require 'torch'
require 'sys'
require 'nn'
require 'xlua'

cmd = torch.CmdLine()
cmd:text()
	"""Short and sweet LSTM implementation in Tensorflow.

	Motivation:
	When Tensorflow was released, adding RNNs was a bit of a hack - it required
	building separate graphs for every number of timesteps and was a bit obscure
	to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
	Currently the APIs are decent, but all the tutorials that I am aware of are not
	making the best use of the new APIs.

	Advantages of this implementation:
	import theano
	import theano.tensor as T
	from theano.tensor.shared_randomstreams import RandomStreams
	from theano.sandbox.rng_mrg import MRG_RandomStreams
	from lasagne.updates import adam
	from lasagne.utils import collect_shared_vars

	from sklearn.datasets import fetch_mldata
	from sklearn.cross_validation import train_test_split
	from sklearn import preprocessing
	# Working example for my blog post at:
	# http://danijar.com/variable-sequence-lengths-in-tensorflow/
	import functools
	import sets
	import tensorflow as tf
	from tensorflow.models.rnn import rnn_cell
	from tensorflow.models.rnn import rnn


	def lazy_property(function):
	import sys
	sys.path.append('..')

	import os
	import json
	from time import time
	import numpy as np
	from tqdm import tqdm
	from matplotlib import pyplot as plt
	from sklearn.externals import joblib
	# -- coding: utf-8 --
	"""
	Created on Mon Aug 22 00:36:48 2016

	@author: park
	"""

	#%% train

	import theano
	# Alec Radford, Indico, Kyle Kastner
	# License: MIT
	"""
	Convolutional VAE in a single file.
	Bringing in code from IndicoDataSolutions and Alec Radford (NewMu)
	Additionally converted to use default conv2d interface instead of explicit cuDNN
	"""
	import theano
	import theano.tensor as T
	from theano.compat.python2x import OrderedDict
	-- Max Jaderberg 4/9/13
	-- GPU Effectiveness test

	require 'torch'
	require 'sys'
	require 'nn'
	require 'xlua'

	cmd = torch.CmdLine()
	cmd:text()