Description Usage Arguments Input shape Output shape See Also

View source: R/layers-locally-connected.R

`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.

1 2 3 4 5 6 7 | ```
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)
``` |

`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 |

`padding` |
Currently only supports |

`data_format` |
A string, one of |

`activation` |
Activation function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation: |

`use_bias` |
Boolean, whether the layer uses a bias vector. |

`kernel_initializer` |
Initializer for the |

`bias_initializer` |
Initializer for the bias vector. |

`kernel_regularizer` |
Regularizer function applied to the |

`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. |

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'.

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.

Other locally connected layers: `layer_locally_connected_1d`

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