`R/layers-locally-connected.R`

`layer_locally_connected_1d.Rd`

`layer_locally_connected_1d()`

works similarly to `layer_conv_1d()`

, except
that weights are unshared, that is, a different set of filters is applied at
each different patch of the input.

layer_locally_connected_1d( object, filters, kernel_size, strides = 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, implementation = 1L, 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 a single integer, specifying the length of the 1D convolution window. |

strides | An integer or list of a single integer, specifying the stride
length of the convolution. 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. |

implementation | either 1, 2, or 3. 1 loops over input spatial locations
to perform the forward pass. It is memory-efficient but performs a lot of
(small) ops. 2 stores layer weights in a dense but sparsely-populated 2D
matrix and implements the forward pass as a single matrix-multiply. It uses
a lot of RAM but performs few (large) ops. 3 stores layer weights in a
sparse tensor and implements the forward pass as a single sparse
matrix-multiply. How to choose: 1: large, dense models, 2: small models, 3:
large, sparse models, where "large" stands for large input/output
activations (i.e. many |

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. |

3D tensor with shape: `(batch_size, steps, input_dim)`

3D tensor with shape: `(batch_size, new_steps, filters)`

`steps`

value might have changed due to padding or strides.

Other locally connected layers:
`layer_locally_connected_2d()`