View source: R/layerslocallyconnected.R
layer_locally_connected_1d  R Documentation 
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

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. 
implementation 
either 1, 2, or 3. 1 loops over input spatial locations
to perform the forward pass. It is memoryefficient but performs a lot of
(small) ops. 2 stores layer weights in a dense but sparselypopulated 2D
matrix and implements the forward pass as a single matrixmultiply. 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
matrixmultiply. 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 
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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