`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
What to compose the new

`Layer`

instance with. Typically a Sequential model or a Tensor (e.g., as returned by`layer_input()`

). The return value depends on`object`

. If`object`

is:missing or

`NULL`

, the`Layer`

instance is returned.a

`Sequential`

model, the model with an additional layer is returned.a Tensor, the output tensor from

`layer_instance(object)`

is returned.

- 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

`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, 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
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

`filters, input_filters, large input_size, output_size`

), and "sparse" stands for few connections between inputs and outputs, i.e. small ratio`filters * input_filters * kernel_size / (input_size * 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`padding="valid"`

is supported by`implementation=1`

.- 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()`