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 )
Model or layer object
Integer, the dimensionality of the output space (i.e. the number output of filters in the convolution).
An integer or list of a single integer, specifying the length of the 1D convolution window.
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
Currently only supports
A string, one of
Activation function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation:
Boolean, whether the layer uses a bias vector.
Initializer for the
Initializer for the bias vector.
Regularizer function applied to the
Regularizer function applied to the bias vector.
Regularizer function applied to the output of the layer (its "activation")..
Constraint function applied to the kernel matrix.
Constraint function applied to the bias vector.
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
Fixed batch size for layer
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.
Whether the layer weights will be updated during training.
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: