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| Latency Comparison Numbers | |
| -------------------------- | |
| L1 cache reference 0.5 ns | |
| Branch mispredict 5 ns | |
| L2 cache reference 7 ns 14x L1 cache | |
| Mutex lock/unlock 25 ns | |
| Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
| Compress 1K bytes with Zippy 3,000 ns 3 us | |
| Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
| Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
| 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 |
| 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 |
| 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 |
| """ | |
| The MIT License (MIT) | |
| Copyright (c) 2015 Alec Radford | |
| Permission is hereby granted, free of charge, to any person obtaining a copy | |
| of this software and associated documentation files (the "Software"), to deal | |
| in the Software without restriction, including without limitation the rights | |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| copies of the Software, and to permit persons to whom the Software is |
| def adam(loss, all_params, learning_rate=0.001, b1=0.9, b2=0.999, e=1e-8, | |
| gamma=1-1e-8): | |
| """ | |
| ADAM update rules | |
| Default values are taken from [Kingma2014] | |
| References: | |
| [Kingma2014] Kingma, Diederik, and Jimmy Ba. | |
| "Adam: A Method for Stochastic Optimization." | |
| arXiv preprint arXiv:1412.6980 (2014). |
| def adam(loss, all_params, learning_rate=0.0002, beta1=0.1, beta2=0.001, | |
| epsilon=1e-8, gamma=1-1e-7): | |
| """ | |
| ADAM update rules | |
| Default values are taken from [Kingma2014] | |
| References: | |
| [Kingma2014] Kingma, Diederik, and Jimmy Ba. | |
| "Adam: A Method for Stochastic Optimization." | |
| arXiv preprint arXiv:1412.6980 (2014). |
| """ | |
| 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) |