R/layerslocallyconnected.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


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()