layer_locally_connected_2d {keras} | R Documentation

## Locally-connected layer for 2D inputs.

### Description

`layer_locally_connected_2d` works similarly to `layer_conv_2d()`, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

### Usage

    layer_locally_connected_2d(object, filters, kernel_size, strides = c(1L,
      1L), padding = "valid", data_format = NULL, activation = NULL,
      use_bias = TRUE, kernel_initializer = "glorot_uniform",
      bias_initializer = "zeros", kernel_regularizer = NULL,
      bias_regularizer = NULL, activity_regularizer = NULL,
      kernel_constraint = NULL, bias_constraint = NULL,
      batch_size = NULL, name = NULL, trainable = NULL, weights = NULL)

### Arguments

`object` | Model or layer object
`filters` | Integer, the dimensionality of the output space (i.e. the number output of filters in the convolution).
`kernel_size` | An integer or list of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
`strides` | An integer or list of 2 integers, specifying the strides of the convolution along the width and height. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any `dilation_rate` value != 1.
`padding` | Currently only supports `"valid"` (case-insensitive). `"same"` may be supported in the future.
`data_format` | A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch, width, height, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, width, height)`. 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".
`activation` | Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: `a(x) = x`).
`use_bias` | Boolean, whether the layer uses a bias vector.
`kernel_initializer` | Initializer for the `kernel` weights matrix.
`bias_initializer` | Initializer for the bias vector.
`kernel_regularizer` | Regularizer function applied to the `kernel` weights matrix.
`bias_regularizer` | Regularizer function applied to the bias vector.
`activity_regularizer` | Regularizer function applied to the output of the layer (its "activation")..
`kernel_constraint` | Constraint function applied to the kernel matrix.
`bias_constraint` | Constraint function applied to the bias vector.
`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

4D tensor with shape: `(samples, filters, new_rows, new_cols)` if data_format='channels_first' or 4D tensor with shape: `(samples, new_rows, new_cols, filters)` if data_format='channels_last'. `rows` and `cols` values might have changed due to padding.

### See Also

Other locally connected layers: `layer_locally_connected_1d`

[Package keras version 2.2.4 Index]