oarriaga

"""
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

oarriaga

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':

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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:

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from keras.layers.core import Layer
import keras.backend as K

if K.backend() == 'tensorflow':
    import tensorflow as tf
    def K_arange(start, stop=None, step=1, dtype='int32'):
        result = tf.range(start, limit=stop, delta=step, name='arange')
        if dtype != 'int32':
            result = K.cast(result, dtype)
        return result

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"""
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):

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import pickle
import numpy as np
import pdb

img_width, img_height = 300, 300
box_configs = [
    {'layer_width': 38, 'layer_height': 38, 'num_prior': 3, 'min_size':  30.0,
     'max_size': None, 'aspect_ratios': [1.0, 2.0, 1/2.0]},
    {'layer_width': 19, 'layer_height': 19, 'num_prior': 6, 'min_size':  60.0,
     'max_size': 114.0, 'aspect_ratios': [1.0, 1.0, 2.0, 1/2.0, 3.0, 1/3.0]},

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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:

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#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/