163 lines
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<div class="title">Gated Recurrent Unit Example</div> </div>
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<dl class="section user"><dt>Description:</dt><dd></dd></dl>
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<dl class="section user"><dt></dt><dd>Demonstrates a gated recurrent unit (GRU) example with the use of fully-connected, Tanh/Sigmoid activation functions.</dd></dl>
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<dl class="section user"><dt>Model definition:</dt><dd></dd></dl>
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<dl class="section user"><dt></dt><dd>GRU is a type of recurrent neural network (RNN). It contains two sigmoid gates and one hidden state. </dd></dl>
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<dl class="section user"><dt></dt><dd>The computation can be summarized as: <pre>z[t] = sigmoid( W_z ⋅ {h[t-1],x[t]} )
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r[t] = sigmoid( W_r ⋅ {h[t-1],x[t]} )
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n[t] = tanh( W_n ⋅ [r[t] × {h[t-1], x[t]} )
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h[t] = (1 - z[t]) × h[t-1] + z[t] × n[t] </pre> <div class="image">
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<img src="GRU.gif" alt="GRU.gif"/>
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Gate Recurrent Unit Diagram</div></div>
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</dd></dl>
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<dl class="section user"><dt>Variables Description:</dt><dd></dd></dl>
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<dl class="section user"><dt></dt><dd><ul>
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<li><code>update_gate_weights</code>, <code>reset_gate_weights</code>, <code>hidden_state_weights</code> are weights corresponding to update gate (W_z), reset gate (W_r), and hidden state (W_n). </li>
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<li><code>update_gate_bias</code>, <code>reset_gate_bias</code>, <code>hidden_state_bias</code> are layer bias arrays </li>
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<li><code>test_input1</code>, <code>test_input2</code>, <code>test_history</code> are the inputs and initial history</li>
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</ul>
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</dd></dl>
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<dl class="section user"><dt></dt><dd>The buffer is allocated as: </dd></dl>
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<dl class="section user"><dt></dt><dd>| reset | input | history | update | hidden_state | </dd></dl>
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<dl class="section user"><dt></dt><dd>In this way, the concatination is automatically done since (reset, input) and (input, history) are physically concatinated in memory. </dd></dl>
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<dl class="section user"><dt></dt><dd>The ordering of the weight matrix should be adjusted accordingly.</dd></dl>
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<dl class="section user"><dt>CMSIS DSP Software Library Functions Used:</dt><dd></dd></dl>
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<dl class="section user"><dt></dt><dd><ul>
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<li><a class="el" href="group__FC.html#gae3857bb6375692e81dde8cbd70adec08" title="Mixed Q15-Q7 opt fully-connected layer function. ">arm_fully_connected_mat_q7_vec_q15_opt()</a></li>
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<li><a class="el" href="group__Acti.html#ga8932b57c8d0ee757511af2d40dcc11e7" title="Q15 neural network activation function using direct table look-up. ">arm_nn_activations_direct_q15()</a></li>
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<li>arm_mult_q15()</li>
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<li>arm_offset_q15()</li>
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<li>arm_sub_q15()</li>
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<li>arm_copy_q15()</li>
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</ul>
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</dd></dl>
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<p><b> Refer </b> <a class="el" href="arm__nnexamples__gru_8cpp.html">arm_nnexamples_gru.cpp</a> </p>
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<li class="footer">Generated on Wed Aug 1 2018 17:12:32 for CMSIS-NN by Arm Ltd. All rights reserved.
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