Reference Style Fix (#4972)
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@ -8,7 +8,7 @@ document vector is considered to preserve both the word-level and
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sentence-level structure of the context.
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# References
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- [A Hierarchical Neural Autoencoder for Paragraphs and Documents](https://web.stanford.edu/~jurafsky/pubs/P15-1107.pdf)
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- [A Hierarchical Neural Autoencoder for Paragraphs and Documents](https://arxiv.org/abs/1506.01057)
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Encodes paragraphs and documents with HRNN.
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Results have shown that HRNN outperforms standard
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RNNs and may play some role in more sophisticated generation tasks like
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@ -65,7 +65,7 @@ class PReLU(Layer):
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set `shared_axes=[1, 2]`.
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# References
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- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](http://arxiv.org/pdf/1502.01852v1.pdf)
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- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://arxiv.org/abs/1502.01852)
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'''
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def __init__(self, init='zero', weights=None, shared_axes=None, **kwargs):
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self.supports_masking = True
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@ -124,7 +124,7 @@ class ELU(Layer):
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alpha: scale for the negative factor.
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# References
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- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](http://arxiv.org/pdf/1511.07289v1.pdf)
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- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://arxiv.org/abs/1511.07289v1)
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'''
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def __init__(self, alpha=1.0, **kwargs):
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self.supports_masking = True
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@ -228,7 +228,7 @@ class ThresholdedReLU(Layer):
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theta: float >= 0. Threshold location of activation.
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# References
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- [Zero-Bias Autoencoders and the Benefits of Co-Adapting Features](http://arxiv.org/pdf/1402.3337.pdf)
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- [Zero-Bias Autoencoders and the Benefits of Co-Adapting Features](http://arxiv.org/abs/1402.3337)
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'''
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def __init__(self, theta=1.0, **kwargs):
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self.supports_masking = True
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@ -583,7 +583,7 @@ class Deconvolution2D(Convolution2D):
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`rows` and `cols` values might have changed due to padding.
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# References
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[1] [A guide to convolution arithmetic for deep learning](https://arxiv.org/abs/1603.07285 "arXiv:1603.07285v1 [stat.ML]")
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[1] [A guide to convolution arithmetic for deep learning](https://arxiv.org/abs/1603.07285v1)
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[2] [Transposed convolution arithmetic](http://deeplearning.net/software/theano_versions/dev/tutorial/conv_arithmetic.html#transposed-convolution-arithmetic)
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[3] [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf)
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'''
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@ -243,7 +243,7 @@ class ConvLSTM2D(ConvRecurrent2D):
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# References
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- [Convolutional LSTM Network: A Machine Learning Approach for
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Precipitation Nowcasting](http://arxiv.org/pdf/1506.04214v1.pdf)
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Precipitation Nowcasting](http://arxiv.org/abs/1506.04214v1)
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The current implementation does not include the feedback loop on the
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cells output
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'''
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@ -116,7 +116,7 @@ class SpatialDropout1D(Dropout):
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Same as input
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# References
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- [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/pdf/1411.4280.pdf)
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- [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280)
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'''
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def __init__(self, p, **kwargs):
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super(SpatialDropout1D, self).__init__(p, **kwargs)
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@ -154,7 +154,7 @@ class SpatialDropout2D(Dropout):
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Same as input
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# References
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- [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/pdf/1411.4280.pdf)
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- [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280)
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'''
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def __init__(self, p, dim_ordering='default', **kwargs):
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if dim_ordering == 'default':
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@ -202,7 +202,7 @@ class SpatialDropout3D(Dropout):
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Same as input
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# References
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- [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/pdf/1411.4280.pdf)
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- [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280)
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'''
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def __init__(self, p, dim_ordering='default', **kwargs):
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if dim_ordering == 'default':
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@ -875,7 +875,7 @@ class MaxoutDense(Layer):
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2D tensor with shape: `(nb_samples, output_dim)`.
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# References
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- [Maxout Networks](http://arxiv.org/pdf/1302.4389.pdf)
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- [Maxout Networks](http://arxiv.org/abs/1302.4389)
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'''
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def __init__(self, output_dim,
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nb_feature=4,
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@ -1001,7 +1001,7 @@ class Highway(Layer):
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2D tensor with shape: `(nb_samples, input_dim)`.
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# References
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- [Highway Networks](http://arxiv.org/pdf/1505.00387v2.pdf)
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- [Highway Networks](http://arxiv.org/abs/1505.00387v2)
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'''
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def __init__(self,
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init='glorot_uniform',
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@ -59,7 +59,7 @@ class BatchNormalization(Layer):
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Same shape as input.
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# References
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- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](http://jmlr.org/proceedings/papers/v37/ioffe15.pdf)
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- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)
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'''
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def __init__(self, epsilon=1e-3, mode=0, axis=-1, momentum=0.99,
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weights=None, beta_init='zero', gamma_init='one',
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@ -424,8 +424,8 @@ class GRU(Recurrent):
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dropout_U: float between 0 and 1. Fraction of the input units to drop for recurrent connections.
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# References
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- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](http://www.aclweb.org/anthology/W14-4012)
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- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](http://arxiv.org/pdf/1412.3555v1.pdf)
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- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://arxiv.org/abs/1409.1259)
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- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](http://arxiv.org/abs/1412.3555v1)
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- [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287)
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'''
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def __init__(self, output_dim,
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