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Locally-connected layer for 1D inputs.

Inherits From: `Layer`

tf.keras.layers.LocallyConnected1D( filters, kernel_size, strides=1, padding='valid', data_format=None, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, implementation=1, **kwargs )

The `LocallyConnected1D`

layer works similarly to the `Conv1D`

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

Note:layer attributes cannot be modified after the layer has been called once (except the`trainable`

attribute).

# apply a unshared weight convolution 1d of length 3 to a sequence with # 10 timesteps, with 64 output filters model = Sequential() model.add(LocallyConnected1D(64, 3, input_shape=(10, 32))) # now model.output_shape == (None, 8, 64) # add a new conv1d on top model.add(LocallyConnected1D(32, 3)) # now model.output_shape == (None, 6, 32)

Arguments | |
---|---|

`filters` | Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). |

`kernel_size` | An integer or tuple/list of a single integer, specifying the length of the 1D convolution window. |

`strides` | An integer or tuple/list of a single integer, specifying the stride length of the convolution. 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, length, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, length)` . 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. |

`implementation` | implementation mode, 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.
How to choose:
where "large" stands for large input/output activations (i.e. many It is recommended to benchmark each in the setting of interest to pick the most efficient one (in terms of speed and memory usage). Correct choice of implementation can lead to dramatic speed improvements (e.g. 50X), potentially at the expense of RAM. Also, only |

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.

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/layers/LocallyConnected1D