sloria

# -*- coding: utf-8 -*-
'''recorder.py
Provides WAV recording functionality via two approaches:

Blocking mode (record for a set duration):
>>> rec = Recorder(channels=2)
>>> with rec.open('blocking.wav', 'wb') as recfile:
...     recfile.record(duration=5.0)

Non-blocking mode (start and stop recording):

rossant

"""
MIT License

Copyright (c) 2017 Cyrille Rossant

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

turnersr

#Non-mathematical Introductions

• http://gcn.com/articles/2014/01/09/topographical-data-analysis.aspx
• https://www.simonsfoundation.org/quanta/20131004-the-mathematical-shape-of-things-to-come/

#Videos

• http://www.youtube.com/channel/UCThuKLGcSXhBJ5GlwzHobJw
• http://www.youtube.com/watch?v=kctyag2Xi8o
• http://www.youtube.com/watch?v=x3Hl85OBuc0
• http://www.youtube.com/playlist?list=PL6763F57A61FE6FE8
• http://techtalks.tv/talks/topological-data-analysis-part-i/58123/

Qwlouse

karpathy

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

karpathy

""" 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

falcondai

import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_nn_ops

@ops.RegisterGradient("GuidedRelu")
def _GuidedReluGrad(op, grad):
    return tf.select(0. < grad, gen_nn_ops._relu_grad(grad, op.outputs[0]), tf.zeros(grad.get_shape()))

if __name__ == '__main__':
    with tf.Session() as sess:

mbollmann

from keras import backend as K
from keras.layers.recurrent import LSTM

class HiddenStateLSTM(LSTM):
    """LSTM with input/output capabilities for its hidden state.

    This layer behaves just like an LSTM, except that it accepts further inputs
    to be used as its initial states, and returns additional outputs,
    representing the layer's final states.

mbollmann

class AttentionLSTM(LSTM):
    """LSTM with attention mechanism

    This is an LSTM incorporating an attention mechanism into its hidden states.
    Currently, the context vector calculated from the attended vector is fed
    into the model's internal states, closely following the model by Xu et al.
    (2016, Sec. 3.1.2), using a soft attention model following
    Bahdanau et al. (2014).

    The layer expects two inputs instead of the usual one:

cbaziotis

def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':