Description Usage Arguments Input shape Output shape See Also

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

`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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
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,
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. |

`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()`

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