flax.linen.ConvTranspose¶
- class flax.linen.ConvTranspose(features, kernel_size, strides=None, padding='SAME', kernel_dilation=None, use_bias=True, 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.
- Parameters
features (int) –
kernel_size (Union[int, Tuple[int, ...]]) –
strides (Optional[Tuple[int, ...]]) –
padding (Union[str, int, Sequence[Union[int, Tuple[int, int]]]]) –
kernel_dilation (Optional[Sequence[int]]) –
use_bias (bool) –
dtype (Any) –
param_dtype (Any) –
precision (Union[None, str, jax._src.lax.lax.Precision, Tuple[str, str], Tuple[jax._src.lax.lax.Precision, jax._src.lax.lax.Precision]]) –
kernel_init (Callable[[Any, Tuple[int, ...], Any], Any]) –
bias_init (Callable[[Any, Tuple[int, ...], Any], Any]) –
parent (Optional[Union[Type[flax.linen.module.Module], Type[flax.core.scope.Scope], Type[flax.linen.module._Sentinel]]]) –
name (str) –
- Return type
None
- 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
- 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 (Any) – 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.
- Return type
Any
Methods
bias_init(shape[, dtype])An initializer that returns a constant array full of zeros.
kernel_init(shape[, dtype])