flax.linen.ConvTranspose
flax.linen.ConvTranspose#
- class flax.linen.ConvTranspose(features, kernel_size, strides=None, padding='SAME', kernel_dilation=None, use_bias=True, mask=None, dtype=None, param_dtype=<class 'jax.numpy.float32'>, precision=None, kernel_init=<function variance_scaling.<locals>.init>, bias_init=<function zeros>, parent=<flax.linen.module._Sentinel object>, name=None)[source]#
Convolution Module wrapping lax.conv_transpose.
- features#
number of convolution filters.
- Type
int
- kernel_size#
shape of the convolutional kernel. For 1D convolution, the kernel size can be passed as an integer. For all other cases, it must be a sequence of integers.
- Type
Union[int, Tuple[int, …]]
- strides#
a sequence of n integers, representing the inter-window strides.
- Type
Optional[Tuple[int, …]]
- padding#
either the string ‘SAME’, the string ‘VALID’, the string ‘CIRCULAR’ (periodic boundary conditions), or a sequence of n (low, high) integer pairs that give the padding to apply before and after each spatial dimension. A single int is interpeted as applying the same padding in all dims and passign a single int in a sequence causes the same padding to be used on both sides.
- Type
Union[str, int, Sequence[Union[int, Tuple[int, int]]]]
- kernel_dilation#
None, or a sequence of n integers, giving the dilation factor to apply in each spatial dimension of the convolution kernel. Convolution with kernel dilation is also known as ‘atrous convolution’.
- Type
Optional[Sequence[int]]
- use_bias#
whether to add a bias to the output (default: True).
- Type
bool
- mask#
Optional mask for the weights during masked convolution. The mask must be the same shape as the convolution weight matrix.
- Type
Optional[Any]
- dtype#
the dtype of the computation (default: infer from input and params).
- Type
Any
- param_dtype#
the dtype passed to parameter initializers (default: float32).
- Type
Any
- precision#
numerical precision of the computation see jax.lax.Precision for details.
- Type
Union[None, str, jax._src.lax.lax.Precision, Tuple[str, str], Tuple[jax._src.lax.lax.Precision, jax._src.lax.lax.Precision]]
- kernel_init#
initializer for the convolutional kernel.
- Type
Callable[[Any, Tuple[int, …], Any], Any]
- bias_init#
initializer for the bias.
- Type
Callable[[Any, Tuple[int, …], Any], Any]
- __call__(inputs)[source]#
Applies a transposed convolution to the inputs.
Behaviour mirrors of jax.lax.conv_transpose.
- Parameters
inputs – input data with dimensions (batch, spatial_dims…, features). This is the channels-last convention, i.e. NHWC for a 2d convolution and NDHWC for a 3D convolution. Note: this is different from the input convention used by lax.conv_general_dilated, which puts the spatial dimensions last.
- Returns
The convolved data.
Methods