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tfnlp.layers.DenseEinsum

A densely connected layer that uses tf.einsum as the backing computation.

This layer can perform einsum calculations of arbitrary dimensionality.

output_shape Positive integer or tuple, dimensionality of the output space.
num_summed_dimensions The number of dimensions to sum over. Standard 2D matmul should use 1, 3D matmul should use 2, and so forth.
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 weights matrix.
bias_constraint Constraint function applied to the bias vector.

Input shape:

N-D tensor with shape: (batch_size, ..., input_dim). The most common situation would be a 2D input with shape (batch_size, input_dim).

Output shape:

N-D tensor with shape: (batch_size, ..., units). For instance, for a 2D input with shape (batch_size, input_dim), the output would have shape (batch_size, units).

Methods

call

View source

This is where the layer's logic lives.

Note here that call() method in tf.keras is little bit different from keras API. In keras API, you can pass support masking for layers as additional arguments. Whereas tf.keras has compute_mask() method to support masking.

Args
inputs Input tensor, or list/tuple of input tensors.
*args Additional positional arguments. Currently unused.
**kwargs Additional keyword arguments. Currently unused.

Returns
A tensor or list/tuple of tensors.