neural_tangents/stax.py
def Conv(out_chan, filter_shape, strides=None, padding=Padding.VALID.name,
         W_std=1.0, W_init=_randn(1.0), b_std=0.0, b_init=_randn(1.0),
         parameterization='ntk'):
  """Layer construction function for a convolution layer.

  Based on `jax.experimental.stax.Conv`. Has a similar API apart from:

  W_init and b_init only change the behavior of the finite width network, and
    are not used by kernel_fn. In most cases, W_std and b_std should be used
    instead

  Args:
    padding: in addition to `VALID` and `SAME' padding, supports `CIRCULAR`,
      not available in `jax.experimental.stax.GeneralConv`.
    parameterization: Either 'ntk' or 'standard'. These parameterizations are
      the direct analogues for convolution of the corresponding
      parameterizations for Dense layers.
  """
  return _GeneralConv(_CONV_DIMENSION_NUMBERS, out_chan, filter_shape, strides,
                      padding, W_std, W_init, b_std, b_init, parameterization)
Layer construction function for a convolution layer.

  Based on `jax.experimental.stax.Conv`. Has a similar API apart from:

  W_init and b_init only change the behavior of the finite width network, and
    are not used by kernel_fn. In most cases, W_std and b_std should be used
    instead

  Args:
    padding: in addition to `VALID` and `SAME' padding, supports `CIRCULAR`,
      not available in `jax.experimental.stax.GeneralConv`.
    parameterization: Either 'ntk' or 'standard'. These parameterizations are
      the direct analogues for convolution of the corresponding
      parameterizations for Dense layers.
  
Layer construction function for a convolution layer.

W_init and b_init only change the behavior of the finite width network, and
are not used by kernel_fn. In most cases, W_std and b_std should be used
instead
Layer construction function for a convolution layer . W_init and b_init only change the behavior of the finite width network and are not used by kernel_fn . In most cases W_std and b_std should be used instead
def Conv(out_chan, filter_shape, strides=None, padding=Padding.VALID.name,
         W_std=1.0, W_init=_randn(1.0), b_std=0.0, b_init=_randn(1.0),
         parameterization='ntk'):
  return _GeneralConv(_CONV_DIMENSION_NUMBERS, out_chan, filter_shape, strides,
                      padding, W_std, W_init, b_std, b_init, parameterization)
def Conv ( out_chan , filter_shape , strides = None , padding = Padding . VALID . name , W_std = 1.0 , W_init = _randn ( 1.0 ) , b_std = 0.0 , b_init = _randn ( 1.0 ) , parameterization = 'ntk' ) : return _GeneralConv ( _CONV_DIMENSION_NUMBERS , out_chan , filter_shape , strides , padding , W_std , W_init , b_std , b_init , parameterization )
Layer construction function for a convolution layer.
Layer construction function for a convolution layer .
"""Layer construction function for a convolution layer.

  Based on `jax.experimental.stax.Conv`. Has a similar API apart from:

  W_init and b_init only change the behavior of the finite width network, and
    are not used by kernel_fn. In most cases, W_std and b_std should be used
    instead

  Args:
    padding: in addition to `VALID` and `SAME' padding, supports `CIRCULAR`,
      not available in `jax.experimental.stax.GeneralConv`.
    parameterization: Either 'ntk' or 'standard'. These parameterizations are
      the direct analogues for convolution of the corresponding
      parameterizations for Dense layers.
  """
out_chan
filter_shape
strides
padding
in addition to `VALID` and `SAME' padding, supports `CIRCULAR`,
not available in `jax.experimental.stax.GeneralConv`.
in addition to ` VALID ` and ` SAME ' padding supports ` CIRCULAR ` not available in ` jax . experimental . stax . GeneralConv ` .
W_std
W_init
b_std
b_init
parameterization
Either 'ntk' or 'standard'. These parameterizations are
the direct analogues for convolution of the corresponding
parameterizations for Dense layers.
Either ' ntk ' or ' standard ' . These parameterizations are the direct analogues for convolution of the corresponding parameterizations for Dense layers .