tf.keras.layers.LocallyConnected1D

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Locally-connected layer for 1D inputs.

Inherits From: Layer

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.LocallyConnected1D, `tf.compat.v2.keras.layers.LocallyConnected1D`

tf.keras.layers.LocallyConnected1D(
    filters, kernel_size, strides=1, padding='valid', data_format=None,
    activation=None, use_bias=True, kernel_initializer='glorot_uniform',
    bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None,
    activity_regularizer=None, kernel_constraint=None, bias_constraint=None,
    implementation=1, **kwargs
)

The LocallyConnected1D layer works similarly to the Conv1D layer, except that weights are unshared, that is, a different set of filters is applied at each different patch of the input.

Example:

# apply a unshared weight convolution 1d of length 3 to a sequence with
# 10 timesteps, with 64 output filters
model = Sequential()
model.add(LocallyConnected1D(64, 3, input_shape=(10, 32)))
# now model.output_shape == (None, 8, 64)
# add a new conv1d on top
model.add(LocallyConnected1D(32, 3))
# now model.output_shape == (None, 6, 32)

Arguments
filters	Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
kernel_size	An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.
strides	An integer or tuple/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, length, channels) while channels_first corresponds to inputs with shape (batch, channels, length). 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	implementation mode, 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.

Input shape:

3D tensor with shape: (batch_size, steps, input_dim)

Output shape:

3D tensor with shape: (batch_size, new_steps, filters) steps value might have changed due to padding or strides.