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Locally-connected layer for 2D inputs.

Inherits From: `Layer`

tf.keras.layers.LocallyConnected2D( filters, kernel_size, strides=(1, 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 `LocallyConnected2D`

layer works similarly to the `Conv2D`

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

# apply a 3x3 unshared weights convolution with 64 output filters on a 32x32 image # with `data_format="channels_last"`: model = Sequential() model.add(LocallyConnected2D(64, (3, 3), input_shape=(32, 32, 3))) # now model.output_shape == (None, 30, 30, 64) # notice that this layer will consume (30*30)*(3*3*3*64) + (30*30)*64 parameters # add a 3x3 unshared weights convolution on top, with 32 output filters: model.add(LocallyConnected2D(32, (3, 3))) # now model.output_shape == (None, 28, 28, 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 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 tuple/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. |

`padding` | Currently only support `"valid"` (case-insensitive). `"same"` will be supported in 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, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch, channels, height, width)` . 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 - np.prod(strides))
`, where inputs to and outputs of the layer are assumed to have shapes` input_size + (input_filters,)`,` output_size + (filters,)` respectively.
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 |

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.

© 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/r1.15/api_docs/python/tf/keras/layers/LocallyConnected2D