init

__init__(
    units,
    activation='tanh',
    recurrent_activation='sigmoid',
    use_bias=True,
    kernel_initializer='glorot_uniform',
    recurrent_initializer='orthogonal',
    bias_initializer='zeros',
    kernel_regularizer=None,
    recurrent_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    recurrent_constraint=None,
    bias_constraint=None,
    dropout=0.0,
    recurrent_dropout=0.0,
    implementation=2,
    return_sequences=False,
    return_state=False,
    go_backwards=False,
    stateful=False,
    unroll=False,
    time_major=False,
    reset_after=True,
    **kwargs
)

参数

理论

${\hat{c}}^{<t>}$是记忆状态,对应矩阵形状($units\ast features+units\ast units+bias$)

${\Gamma }_u$为更新门(update)，式中的δ为sigmoid函数，这让${\Gamma }_u$趋向于0或者1。当${\Gamma }_u$为0时${\hat{c}}^{<t>}$=${\hat{c}}^{<t-1>}$,不更新,记忆前一步,反之,更新
${\Gamma }_u$的矩阵形状是($units\ast features+units\ast units+bias$)

${\Gamma }_r$为记忆门(remember)控制前一时刻的状态被带入到当前状态中的程度,${\Gamma }_r$为1,带入信息大,重置门用于控制忽略前一时刻状态信息的程度,越小忽略越多
${\Gamma }_r$的矩阵形状是($units\ast features+units\ast units+bias$)

所以GRU层的总参数量为$(units\ast features+units\ast units+units)\ast 3$

注意:
tensorflow2.0中默认reset_after=True,所以separate biases for input and recurrent kernels因此总参数量为$(units\ast features+units\ast units+units+units)\ast 3$
将input和recurrent kernels的bias分开计算了

参考:
官网
https://www.imooc.com/article/36743
https://stackoverflow.com/questions/57318930/calculating-the-number-of-parameters-of-a-gru-layer-keras