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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 >
< dl class = "section user" > < dt > Model definition:< / dt > < dd > < / dd > < / dl >
< 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 >
< dl class = "section user" > < dt > < / dt > < dd > The computation can be summarized as: < pre > z[t] = sigmoid( W_z ⋅ {h[t-1],x[t]} )
r[t] = sigmoid( W_r ⋅ {h[t-1],x[t]} )
n[t] = tanh( W_n ⋅ [r[t] × {h[t-1], x[t]} )
h[t] = (1 - z[t]) × h[t-1] + z[t] × n[t] < / pre > < div class = "image" >
< img src = "GRU.gif" alt = "GRU.gif" / >
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Gate Recurrent Unit Diagram< / div > < / div >
< / dd > < / dl >
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< dl class = "section user" > < dt > Variables Description:< / dt > < dd > < / dd > < / dl >
< 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 >
< li > < code > update_gate_bias< / code > , < code > reset_gate_bias< / code > , < code > hidden_state_bias< / code > are layer bias arrays < / li >
< 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 >
< / dd > < / dl >
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< dl class = "section user" > < dt > < / dt > < dd > The buffer is allocated as: < / dd > < / dl >
< dl class = "section user" > < dt > < / dt > < dd > | reset | input | history | update | hidden_state | < / dd > < / dl >
< 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 >
< 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 >
< 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 >
< 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 >
< li > arm_mult_q15()< / li >
< li > arm_offset_q15()< / li >
< li > arm_sub_q15()< / li >
< li > arm_copy_q15()< / li >
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< / ul >
< / 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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