Fan Yang fanyangxyz

## subword_text_tokenizer.py
# coding=utf-8
# Copyright 2017 The Tensor2Tensor Authors.
#
# 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

## seq2seq.py
import logging

import numpy as np
import tensorflow as tf
from tensorflow.contrib import layers


GO_TOKEN = 0
END_TOKEN = 1
UNK_TOKEN = 2

## 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

## 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

## 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)

## tmux-cheatsheet.markdown

      
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              3266 forks
            
          
              181 comments
            
          
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                MohamedAlaa
                / tmux-cheatsheet.markdown
            
            
              Last active
              July 23, 2024 05:34
            
              
                tmux shortcuts & cheatsheet
              
          
    tmux shortcuts & cheatsheet

start new:
tmux

start new with session name:
tmux new -s myname


## lisp.cpp
Lisp interpreter in 90 lines of C++

I've enjoyed reading Peter Norvig's recent articles on Lisp. He implements a Scheme interpreter in 90 lines of Python in the first, and develops it further in the second.

Just for fun I wondered if I could write one in C++. My goals would be

1. A Lisp interpreter that would complete Peter's Lis.py test cases correctly...
2. ...in no more than 90 lines of C++.

Although I've been thinking about this for a few weeks, as I write this I have not written a line of the code. I'm pretty sure I will achieve 1, and 2 will be... a piece of cake!
	# coding=utf-8
	# Copyright 2017 The Tensor2Tensor Authors.
	#
	# 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
	import logging

	import numpy as np
	import tensorflow as tf
	from tensorflow.contrib import layers


	GO_TOKEN = 0
	END_TOKEN = 1
	UNK_TOKEN = 2
	""" 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 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
	"""
	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)
	Lisp interpreter in 90 lines of C++

	I've enjoyed reading Peter Norvig's recent articles on Lisp. He implements a Scheme interpreter in 90 lines of Python in the first, and develops it further in the second.

	Just for fun I wondered if I could write one in C++. My goals would be

	1. A Lisp interpreter that would complete Peter's Lis.py test cases correctly...
	2. ...in no more than 90 lines of C++.

	Although I've been thinking about this for a few weeks, as I write this I have not written a line of the code. I'm pretty sure I will achieve 1, and 2 will be... a piece of cake!