What is GRU in TF learn Explain with example

This recipe explains what is GRU in TF learn with example

Recipe Objective

This recipe explains what is GRU in TFLayer.

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GRU

GRU stands for Gated Recurrent Unit Layer.
Its syntax is: tflearn.layers.recurrent.gru (incoming, n_units, activation='tanh', inner_activation='sigmoid', dropout=None, bias=True, weights_init=None, return_seq=False, return_state=False, initial_state=None, dynamic=False, trainable=True, restore=True, reuse=False, scope=None, name='GRU')

def GRU(incoming, n_units, activation='linear', inner_activation='softmax', dropout=None, bias=True, weights_init=None, return_seq=False, return_state=False, initial_state=None, dynamic=False, trainable=True, restore=True, reuse=False, scope=None, name="GRU"):

    c = GRUCell(n_units, activation=activation, inner_activation=inner_activation, bias=bias, weights_init=weights_init, trainable=trainable, restore=restore, reuse=reuse)
    y = _rnn_template(incoming, cell=cell, dropout=dropout, return_seq=return_seq, return_state=return_state, initial_state=initial_state, dynamic=dynamic, scope=scope, name=name)

return y

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