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<span style="float:right;">[[source]](https://github.com/keras-team/keras/blob/master/keras/layers/local.py#L19)</span>
### LocallyConnected1D
```python
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)
```
Locally-connected layer for 1D inputs.
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.
__Example__
```python
# 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__: String, one of `channels_first`, `channels_last`.
- __activation__: Activation function to use
(see [activations](../activations.md)).
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
(see [initializers](../initializers.md)).
- __bias_initializer__: Initializer for the bias vector
(see [initializers](../initializers.md)).
- __kernel_regularizer__: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
- __bias_regularizer__: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
- __activity_regularizer__: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
- __kernel_constraint__: Constraint function applied to the kernel matrix
(see [constraints](../constraints.md)).
- __bias_constraint__: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
__Input shape__
3D tensor with shape: `(batch_size, steps, input_dim)`
__Output shape__
3D tensor with shape: `(batch_size, new_steps, filters)`
`steps` value might have changed due to padding or strides.
----
<span style="float:right;">[[source]](https://github.com/keras-team/keras/blob/master/keras/layers/local.py#L183)</span>
### LocallyConnected2D
```python
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)
```
Locally-connected layer for 2D inputs.
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.
__Examples__
```python
# 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
(see [activations](../activations.md)).
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
(see [initializers](../initializers.md)).
- __bias_initializer__: Initializer for the bias vector
(see [initializers](../initializers.md)).
- __kernel_regularizer__: Regularizer function applied to
the `kernel` weights matrix
(see [regularizer](../regularizers.md)).
- __bias_regularizer__: Regularizer function applied to the bias vector
(see [regularizer](../regularizers.md)).
- __activity_regularizer__: Regularizer function applied to
the output of the layer (its "activation").
(see [regularizer](../regularizers.md)).
- __kernel_constraint__: Constraint function applied to the kernel matrix
(see [constraints](../constraints.md)).
- __bias_constraint__: Constraint function applied to the bias vector
(see [constraints](../constraints.md)).
__Input shape__
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'.
__Output shape__
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.
|
