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@ -9,12 +9,33 @@ from ..param_attr import ParamAttr
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from tensor import concat
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__all__ = [
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'fc', 'embedding', 'dynamic_lstm', 'gru_unit', 'linear_chain_crf',
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'crf_decoding', 'cos_sim', 'cross_entropy', 'square_error_cost', 'accuracy',
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'chunk_eval', 'sequence_conv', 'conv2d', 'sequence_pool', 'pool2d',
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'batch_norm', 'beam_search_decode', 'conv2d_transpose', 'sequence_expand',
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'lstm_unit', 'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min',
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'sequence_first_step', 'sequence_last_step', 'dropout'
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'fc',
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'embedding',
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'dynamic_lstm',
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'gru_unit',
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'linear_chain_crf',
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'crf_decoding',
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'cos_sim',
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'cross_entropy',
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'square_error_cost',
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'accuracy',
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'chunk_eval',
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'sequence_conv',
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'conv2d',
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'sequence_pool',
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'pool2d',
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'batch_norm',
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'beam_search_decode',
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'conv2d_transpose',
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'sequence_expand',
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'lstm_unit',
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'reduce_sum',
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'reduce_mean',
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'reduce_max',
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'reduce_min',
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'sequence_first_step',
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'sequence_last_step',
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'dropout',
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]
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@ -248,13 +269,13 @@ def gru_unit(input,
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h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})
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The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms
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of the equation above, the :math:`z_t` is split into 3 parts -
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:math:`xu_t`, :math:`xr_t` and :math:`xm_t`. This means that in order to
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implement a full GRU unit operator for an input, a fully
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of the equation above, the :math:`z_t` is split into 3 parts -
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:math:`xu_t`, :math:`xr_t` and :math:`xm_t`. This means that in order to
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implement a full GRU unit operator for an input, a fully
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connected layer has to be applied, such that :math:`z_t = W_{fc}x_t`.
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The terms :math:`u_t` and :math:`r_t` represent the update and reset gates
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of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is
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The terms :math:`u_t` and :math:`r_t` represent the update and reset gates
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of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is
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an intermediate candidate hidden output, which is denoted by :math:`m_t`.
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This layer has three outputs :math:`h_t`, :math:`dot(r_t, h_{t-1})`
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and concatenation of :math:`u_t`, :math:`r_t` and :math:`m_t`.
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@ -276,7 +297,7 @@ def gru_unit(input,
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.. code-block:: python
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# assuming we have x_t_data and prev_hidden of size=10
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x_t = fluid.layers.fc(input=x_t_data, size=30)
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x_t = fluid.layers.fc(input=x_t_data, size=30)
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hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
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hidden = prev_hidden)
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Cao Ying
7 years ago
committed by
GitHub