Function dot

tensorflow/python/keras/backend.py:1630–1698

Multiplies 2 tensors (and/or variables) and returns a *tensor*. When attempting to multiply a nD tensor with a nD tensor, it reproduces the Theano behavior. (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`) Arguments: x: Tensor or variable. y: Tensor or variable. Returns: A ten

(x, y)

@keras_export('keras.backend.dot')
def dot(x, y):
  """Multiplies 2 tensors (and/or variables) and returns a *tensor*.

  When attempting to multiply a nD tensor
  with a nD tensor, it reproduces the Theano behavior.
  (e.g. `(2, 3) * (4, 3, 5) -> (2, 4, 5)`)

  Arguments:
      x: Tensor or variable.
      y: Tensor or variable.

  Returns:
      A tensor, dot product of `x` and `y`.

  Examples:
  ```python
  # dot product between tensors
  >>> x = K.placeholder(shape=(2, 3))
  >>> y = K.placeholder(shape=(3, 4))
  >>> xy = K.dot(x, y)
  >>> xy
  <tf.Tensor 'MatMul_9:0' shape=(2, 4) dtype=float32>
  ```

  ```python
  # dot product between tensors
  >>> x = K.placeholder(shape=(32, 28, 3))
  >>> y = K.placeholder(shape=(3, 4))
  >>> xy = K.dot(x, y)
  >>> xy
  <tf.Tensor 'MatMul_9:0' shape=(32, 28, 4) dtype=float32>
  ```

  ```python
  # Theano-like behavior example
  >>> x = K.random_uniform_variable(shape=(2, 3), low=0, high=1)
  >>> y = K.ones((4, 3, 5))
  >>> xy = K.dot(x, y)
  >>> K.int_shape(xy)
  (2, 4, 5)
  ```
  """
  if ndim(x) is not None and (ndim(x) > 2 or ndim(y) > 2):
    x_shape = []
    for i, s in zip(int_shape(x), array_ops.unstack(array_ops.shape(x))):
      if i is not None:
        x_shape.append(i)
      else:
        x_shape.append(s)
    x_shape = tuple(x_shape)
    y_shape = []
    for i, s in zip(int_shape(y), array_ops.unstack(array_ops.shape(y))):
      if i is not None:
        y_shape.append(i)
      else:
        y_shape.append(s)
    y_shape = tuple(y_shape)
    y_permute_dim = list(range(ndim(y)))