- 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
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"""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 | |
""" |
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""" 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 |
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import tensorflow as tf | |
import numpy as np | |
import os | |
import zconfig | |
import utils | |
class RBM(object): |
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""" | |
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) |