tf.keras.layers.MultiHeadAttention( num_heads, key_dim, value_dim=None, dropout=0.0, use_bias=True, output_shape=None, attention_axes=None, kernel_initializer="glorot_uniform", bias_initializer="zeros", kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs )
This is an implementation of multi-headed attention as described in the
paper "Attention is all you Need" (Vaswani et al., 2017).
value are the same, then
this is self-attention. Each timestep in
query attends to the
corresponding sequence in
key, and returns a fixed-width vector.
This layer first projects
value. These are
(effectively) a list of tensors of length
num_attention_heads, where the
corresponding shapes are
(batch_size, <query dimensions>, key_dim),
(batch_size, <key/value dimensions>, key_dim),
(batch_size, <key/value dimensions>, value_dim).
Then, the query and key tensors are dot-producted and scaled. These are softmaxed to obtain attention probabilities. The value tensors are then interpolated by these probabilities, then concatenated back to a single tensor.
Finally, the result tensor with the last dimension as value_dim can take an linear projection and return.
MultiHeadAttention inside a custom layer, the custom layer must
implement its own
build() method and call
This enables weights to be restored correctly when the model is loaded.
Performs 1D cross-attention over two sequence inputs with an attention mask. Returns the additional attention weights over heads.
>>> layer = MultiHeadAttention(num_heads=2, key_dim=2) >>> target = tf.keras.Input(shape=[8, 16]) >>> source = tf.keras.Input(shape=[4, 16]) >>> output_tensor, weights = layer(target, source, ... return_attention_scores=True) >>> print(output_tensor.shape) (None, 8, 16) >>> print(weights.shape) (None, 2, 8, 4)
Performs 2D self-attention over a 5D input tensor on axes 2 and 3.
>>> layer = MultiHeadAttention( ... num_heads=2, key_dim=2, attention_axes=(2, 3)) >>> input_tensor = tf.keras.Input(shape=[5, 3, 4, 16]) >>> output_tensor = layer(input_tensor, input_tensor) >>> print(output_tensor.shape) (None, 5, 3, 4, 16)
Nonemeans attention over all axes, but batch, heads, and features.
(B, T, dim).
(B, S, dim).
(B, S, dim). If not given, will use
value, which is the most common case.
(B, T, S), that prevents attention to certain positions. The boolean mask specifies which query elements can attend to which key elements, 1 indicates attention and 0 indicates no attention. Broadcasting can happen for the missing batch dimensions and the head dimension.
False. Defaults to
(B, T, E), where
Tis for target sequence shapes and
Eis the query input last dimension if
None. Otherwise, the multi-head outputs are projected to the shape specified by