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 )
What to compose the new
Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by
The return value depends on
Layer instance is returned.
Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from
layer_instance(object) is returned.
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
dilation_rate value != 1.
Currently only supports
may be supported in the future.
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
found in your Keras config file at
~/.keras/keras.json. If you never set
it, then it will be "channels_last".
Activation function to use. If you don't specify anything,
no activation is applied (ie. "linear" activation:
a(x) = x).
Boolean, whether the layer uses a bias vector.
Initializer for the
kernel weights matrix.
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
filters, input_filters, large input_size, output_size),
and "sparse" stands for few connections between inputs and outputs, i.e.
filters * input_filters * kernel_size / (input_size * strides),
where inputs to and outputs of the layer are assumed to have shapes
(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
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: