`layer_locally_connected_2d.Rd`

`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.

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`