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Locally-connected layer for 1D inputs.
Compat aliases for migration
See Migration guide for more details.
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 )
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.
Note: layer attributes cannot be modified after the layer has been called once (except the
# 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)
| ||Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).|
| ||An integer or tuple/list of a single integer, specifying the length of the 1D convolution window.|
| || 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 |
| || 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.|
| || implementation mode, either |
How to choose:
where "large" stands for large input/output activations (i.e. many
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.
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.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.