def conv_info(in_shape,
out_chan,
filter_shape,
strides=None,
padding='VALID',
kernel_init=None,
bias_init=stax.randn(1e-6),
transpose=False):
"""Returns parameters and output shape information given input shapes."""
# Essentially the `stax` implementation
if len(in_shape) != 3:
raise ValueError('Need to `jax.vmap` in order to batch')
in_shape = (1, ) + in_shape
lhs_spec, rhs_spec, out_spec = DIMENSION_NUMBERS
one = (1, ) * len(filter_shape)
strides = strides or one
kernel_init = kernel_init or stax.glorot(rhs_spec.index('O'),
rhs_spec.index('I'))
filter_shape_iter = iter(filter_shape)
kernel_shape = tuple([
out_chan if c == 'O' else
in_shape[lhs_spec.index('C')] if c == 'I' else next(filter_shape_iter)
for c in rhs_spec
])
if transpose:
out_shape = lax.conv_transpose_shape_tuple(in_shape, kernel_shape,
strides, padding,
DIMENSION_NUMBERS)
else:
out_shape = lax.conv_general_shape_tuple(in_shape, kernel_shape,
strides, padding,
DIMENSION_NUMBERS)
bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]
bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))
out_shape = out_shape[1:]
shapes = (out_shape, kernel_shape, bias_shape)
inits = (kernel_init, bias_init)
return shapes, inits, (strides, padding, one)
def initialize(cls, rng, in_spec, dim_out,
kernel_init=stax.glorot(),
bias_init=stax.zeros):
"""Initializes Dense Layer.
Args:
rng: Random key.
in_spec: Input Spec.
dim_out: Output dimensions.
kernel_init: Kernel initialization function.
bias_init: Bias initialization function.
Returns:
Tuple with the output shape and the LayerParams.
"""
if rng is None:
raise ValueError('Need valid RNG to instantiate Dense layer.')
dim_in = in_spec.shape[-1]
k1, k2 = random.split(rng)
params = DenseParams(
base.create_parameter(k1, (dim_in, dim_out), init=kernel_init),
base.create_parameter(k2, (dim_out,), init=bias_init)
)
return base.LayerParams(params)
def testGlorotInitShape(self, shape):
key = random.PRNGKey(0)
out = stax.glorot()(key, shape)
self.assertEqual(out.shape, shape)
def create_parameter(rng, spec, init=stax.glorot()):
return init(rng, spec)