tf.keras.backend.batch_dot( x, y, axes=None )
Defined in tensorflow/python/keras/_impl/keras/backend.py
.
Batchwise dot product.
batch_dot
is used to compute dot product of x
and y
when x
and y
are data in batch, i.e. in a shape of (batch_size, :)
. batch_dot
results in a tensor or variable with less dimensions than the input. If the number of dimensions is reduced to 1, we use expand_dims
to make sure that ndim is at least 2.
x
: Keras tensor or variable with ndim >= 2
.y
: Keras tensor or variable with ndim >= 2
.axes
: list of (or single) int with target dimensions. The lengths of axes[0]
and axes[1]
should be the same.A tensor with shape equal to the concatenation of `x`'s shape (less the dimension that was summed over) and `y`'s shape (less the batch dimension and the dimension that was summed over). If the final rank is 1, we reshape it to `(batch_size, 1)`.
Examples: Assume x = [[1, 2], [3, 4]]
and y = [[5, 6], [7, 8]]
batch_dot(x, y, axes=1) = [[17, 53]]
which is the main diagonal of x.dot(y.T)
, although we never have to calculate the off-diagonal elements.
Shape inference: Let `x`'s shape be `(100, 20)` and `y`'s shape be `(100, 30, 20)`. If `axes` is (1, 2), to find the output shape of resultant tensor, loop through each dimension in `x`'s shape and `y`'s shape: * `x.shape[0]` : 100 : append to output shape * `x.shape[1]` : 20 : do not append to output shape, dimension 1 of `x` has been summed over. (`dot_axes[0]` = 1) * `y.shape[0]` : 100 : do not append to output shape, always ignore first dimension of `y` * `y.shape[1]` : 30 : append to output shape * `y.shape[2]` : 20 : do not append to output shape, dimension 2 of `y` has been summed over. (`dot_axes[1]` = 2) `output_shape` = `(100, 30)`
>>> x_batch = K.ones(shape=(32, 20, 1)) >>> y_batch = K.ones(shape=(32, 30, 20)) >>> xy_batch_dot = K.batch_dot(x_batch, y_batch, axes=[1, 2]) >>> K.int_shape(xy_batch_dot) (32, 1, 30)
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Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/backend/batch_dot