qrnn.py 文件源码

def __init__(self, units, window_size=2, stride=1,
                 return_sequences=False, go_backwards=False, 
                 stateful=False, unroll=False, activation='tanh',
                 kernel_initializer='uniform', bias_initializer='zero',
                 kernel_regularizer=None, bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None, bias_constraint=None, 
                 dropout=0, use_bias=True, input_dim=None, input_length=None,
                 **kwargs):
        self.return_sequences = return_sequences
        self.go_backwards = go_backwards
        self.stateful = stateful
        self.unroll = unroll

        self.units = units 
        self.window_size = window_size
        self.strides = (stride, 1)

        self.use_bias = use_bias
        self.activation = activations.get(activation)
        self.kernel_initializer = initializers.get(kernel_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)
        self.kernel_constraint = constraints.get(kernel_constraint)
        self.bias_constraint = constraints.get(bias_constraint)

        self.dropout = dropout
        self.supports_masking = True
        self.input_spec = [InputSpec(ndim=3)]
        self.input_dim = input_dim
        self.input_length = input_length
        if self.input_dim:
            kwargs['input_shape'] = (self.input_length, self.input_dim)
        super(QRNN, self).__init__(**kwargs)