023331ec2a7b0086abfc81eca16c84a1692ee653,keras/layers/core.py,TimeDistributedDense,build,#TimeDistributedDense#Any#,1231

Before Change

        super(TimeDistributedDense, self).__init__(**kwargs)

    def build(self, input_shape):
        self.input_spec = [InputSpec(dtype=K.floatx(),
                                     shape=(None,) + input_shape[1:])]
        input_dim = input_shape[2]

        self.W = self.add_weight((input_dim, self.output_dim),
                                 initializer=self.init,
                                 name="{}_W".format(self.name),
                                 regularizer=self.W_regularizer,
                                 constraint=self.W_constraint)
        if self.bias:
            self.b = self.add_weight((self.output_dim,),
                                     initializer="zero",
                                     name="{}_b".format(self.name),
                                     regularizer=self.b_regularizer,
                                     constraint=self.b_constraint)
        else:
            self.b = None

        if self.initial_weights is not None:
            self.set_weights(self.initial_weights)
            del self.initial_weights
        self.built = True

    def get_output_shape_for(self, input_shape):
        return (input_shape[0], input_shape[1], self.output_dim)

After Change

    This version performs the same function as Dropout, however it drops
    entire 2D feature maps instead of individual elements. If adjacent pixels
    within feature maps are strongly correlated (as is normally the case in
    early convolution layers) then regular dropout will not regularize the
    activations and will otherwise just result in an effective learning rate
    decrease. In this case, SpatialDropout2D will help promote independence
    between feature maps and should be used instead.

    // Arguments
        rate: float between 0 and 1. Fraction of the input units to drop.
        data_format: "channels_first" or "channels_last".
            In "channels_first" mode, the channels dimension
            (the depth) is at index 1,
            in "channels_last" mode is it at index 3.
            It defaults to the `image_data_format` value found in your
            Keras config file at `~/.keras/keras.json`.
            If you never set it, then it will be "channels_last".

    // Input shape
        4D tensor with shape:
        `(samples, channels, rows, cols)` if data_format="channels_first"
        or 4D tensor with shape:
        `(samples, rows, cols, channels)` if data_format="channels_last".

    // Output shape
        Same as input

    // References
        - [Efficient Object Localization Using Convolutional Networks](https://arxiv.org/abs/1411.4280)
    

    def __init__(self, rate, data_format=None, **kwargs):

Instances