lstm_update#

ivy.lstm_update(x, init_h, init_c, kernel, recurrent_kernel, /, *, bias=None, recurrent_bias=None)[source]#

Perform long-short term memory update by unrolling time dimension of input array.

Parameters:

  • x (Union[Array, NativeArray]) – input tensor of LSTM layer [batch_shape, t, in].

  • init_h (Union[Array, NativeArray]) – initial state tensor for the cell output [batch_shape, out].

  • init_c (Union[Array, NativeArray]) – initial state tensor for the cell hidden state [batch_shape, out].

  • kernel (Union[Array, NativeArray]) – weights for cell kernel [in, 4 x out].

  • recurrent_kernel (Union[Array, NativeArray]) – weights for cell recurrent kernel [out, 4 x out].

  • bias (Optional[Union[Array, NativeArray]]) – bias for cell kernel [4 x out]. (Default value = None) (default: None)

  • recurrent_bias (Optional[Union[Array, NativeArray]]) – bias for cell recurrent kernel [4 x out]. (Default value = None) (default: None)

Return type:

Tuple[Array, Array]

Returns:

ret – hidden state for all timesteps [batch_shape,t,out] and cell state for last timestep [batch_shape,out]

Array.lstm_update(self, init_h, init_c, kernel, recurrent_kernel, /, *, bias=None, recurrent_bias=None)#

ivy.Array instance method variant of ivy.lstm_update. This method simply wraps the function, and so the docstring for ivy.lstm_update also applies to this method with minimal changes.

Parameters:

  • init_h (Union[Array, NativeArray]) – initial state tensor for the cell output [batch_shape, out].

  • init_c (Union[Array, NativeArray]) – initial state tensor for the cell hidden state [batch_shape, out].

  • kernel (Union[Array, NativeArray]) – weights for cell kernel [in, 4 x out].

  • recurrent_kernel (Union[Array, NativeArray]) – weights for cell recurrent kernel [out, 4 x out].

  • bias (Optional[Union[Array, NativeArray]]) – bias for cell kernel [4 x out]. (Default value = None) (default: None)

  • recurrent_bias (Optional[Union[Array, NativeArray]]) – bias for cell recurrent kernel [4 x out]. (Default value = None) (default: None)

Return type:

Tuple[Array, Array]

Returns:

ret – hidden state for all timesteps [batch_shape,t,out] and cell state for last timestep [batch_shape,out]

Examples

>>> x = ivy.randint(0, 20, shape=(6, 20, 3))
>>> h_i = ivy.random_normal(shape=(6, 5))
>>> c_i = ivy.random_normal(shape=(6, 5))
>>> kernel = ivy.random_normal(shape=(3, 4 * 5))
>>> rc = ivy.random_normal(shape=(5, 4 * 5))
>>> result = x.lstm_update(h_i, c_i, kernel, rc)

>>> result[0].shape
(6, 20, 5)
>>> result[1].shape
(6, 5)
Container.lstm_update(self, init_h, init_c, kernel, recurrent_kernel, /, *, bias=None, recurrent_bias=None, key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)#

ivy.Container instance method variant of ivy.lstm_update. This method simply wraps the function, and so the docstring for ivy.lstm_update also applies to this method with minimal changes.

Parameters:

  • init_h (Union[Array, NativeArray, Container]) – initial state tensor for the cell output [batch_shape, out].

  • init_c (Union[Array, NativeArray, Container]) – initial state tensor for the cell hidden state [batch_shape, out].

  • kernel (Union[Array, NativeArray, Container]) – weights for cell kernel [in, 4 x out].

  • recurrent_kernel (Union[Array, NativeArray, Container]) – weights for cell recurrent kernel [out, 4 x out].

  • bias (Optional[Union[Array, NativeArray, Container]]) – bias for cell kernel [4 x out]. (Default value = None) (default: None)

  • recurrent_bias (Optional[Union[Array, NativeArray, Container]]) – bias for cell recurrent kernel [4 x out]. (Default value = None) (default: None)

Return type:

Tuple[Container, Container]

Returns:

ret – hidden state for all timesteps [batch_shape,t,out] and cell state for last timestep [batch_shape,out]

Examples

>>> x = ivy.Container(
...     a=ivy.random_normal(shape=(5, 20, 3)),
...     b=ivy.random_normal(shape=(5, 20, 3))
... )
>>> h_i = ivy.random_normal(shape=(5, 6))
>>> c_i = ivy.random_normal(shape=(5, 6))

>>> kernel = ivy.random_normal(shape=(3, 4 * 6))
>>> rc = ivy.random_normal(shape=(6, 4 * 6))
>>> x.lstm_update(h_i, c_i, kernel, rc)
{
    a: (tuple(2), <class ivy.array.array.Array>, shape=[5, 20, 6]),
    b: (tuple(2), <class ivy.array.array.Array>, shape=[5, 20, 6])
}