Samuel Everett samueleverett01

## Simplex-Matrix.ipynb

      
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                samueleverett01
                / Simplex-Matrix.ipynb
            
            
              Created
              May 25, 2019 15:30
            
          
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## bnlstm.py
"""adapted from https://github.com/OlavHN/bnlstm to store separate population statistics per state"""
import tensorflow as tf, numpy as np
RNNCell = tf.nn.rnn_cell.RNNCell

class BNLSTMCell(RNNCell):
    '''Batch normalized LSTM as described in arxiv.org/abs/1603.09025'''
    def __init__(self, num_units, is_training_tensor, max_bn_steps, initial_scale=0.1, activation=tf.tanh, decay=0.95):
        """
        * max bn steps is the maximum number of steps for which to store separate population stats
        """

## pg-pong.py
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

## something2vec.md

      
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                nzw0301
                / something2vec.md
            
            
              Last active
              May 7, 2024 03:53
            
          
    *2vec papers


act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
apk2vec https://arxiv.org/abs/1809.05693
app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
ast2vec https://arxiv.org/abs/2103.11614
attribute2vec https://arxiv.org/abs/2004.01375
author2vec http://dl.acm.org/citation.cfm?id=2889382
baller2vec https://arxiv.org/abs/2102.03291
bb2vec https://arxiv.org/abs/1809.09621


## rbm_after_refactor.py
import tensorflow as tf
import numpy as np
import os

import zconfig
import utils


class RBM(object):

## min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)
	"""adapted from https://github.com/OlavHN/bnlstm to store separate population statistics per state"""
	import tensorflow as tf, numpy as np
	RNNCell = tf.nn.rnn_cell.RNNCell

	class BNLSTMCell(RNNCell):
	'''Batch normalized LSTM as described in arxiv.org/abs/1603.09025'''
	def __init__(self, num_units, is_training_tensor, max_bn_steps, initial_scale=0.1, activation=tf.tanh, decay=0.95):
	"""
	* max bn steps is the maximum number of steps for which to store separate population stats
	"""
	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward
	import tensorflow as tf
	import numpy as np
	import os

	import zconfig
	import utils


	class RBM(object):
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)