Spatial 2D version of Dropout.

    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, layer_spatial_dropout_2d will help promote independence between feature maps and should be used instead.

    layer_spatial_dropout_2d(
      object,
      rate,
      data_format = NULL,
      batch_size = NULL,
      name = NULL,
      trainable = NULL,
      weights = NULL
    )

    Arguments

    object

    Model or layer object

    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".

    batch_size

    Fixed batch size for layer

    name

    An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.

    trainable

    Whether the layer weights will be updated during training.

    weights

    Initial weights for layer.

    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

    See also