def residual(*layers, **kwargs):
"""Constructs a residual version of layers, summing input to layers output."""
res = kwargs.get('res', stax.Identity)
if len(layers) > 1:
return stax.serial(stax.FanOut(2),
stax.parallel(stax.serial(*layers), res),
stax.FanInSum)
elif len(layers) == 1:
return stax.serial(stax.FanOut(2), stax.parallel(layers[0], res),
stax.FanInSum)
else:
raise ValueError('Empty residual combinator.')
def Lambda(fn): # pylint: disable=invalid-name
"""Turn a normal function into a bound, callable Stax layer.
Args:
fn: a python function with _named_ args (i.e. no *args) and no kwargs.
Returns:
A callable, 'bound' staxlayer that can be assigned to a python variable and
called like a function with other staxlayers as arguments. Like Bind,
wherever this value is placed in the stax tree, it will always output the
same cached value.
"""
# fn's args are just symbolic names that we fill with Vars.
num_args = len(inspect.getargspec(fn).args)
if num_args > 1:
bound_args = Vars(num_args)
return LambdaBind(stax.serial(
stax.parallel(*bound_args), # capture inputs
_PlaceholderInputs, # placeholders for input combinators inside fn
fn(*bound_args) # feed captured inputs into fn's args
))
elif num_args == 1:
bound_arg = Var()
return LambdaBind(stax.serial(
bound_arg, # capture input
_PlaceholderInputs, # placeholders for input combinators inside fn
fn(bound_arg) # feed captured inputs into fn's args
))
# LambdaBind when no args are given:
else:
return LambdaBind(fn())
def __call__(self, *args):
if len(args) > 1:
return stax.serial(stax.parallel(*args), self)
elif len(args) == 1:
return stax.serial(args[0], self)
else:
return self
def MultiHeadedAttention( # pylint: disable=invalid-name
feature_depth,
num_heads=8,
dropout=1.0,
mode='train'):
"""Transformer-style multi-headed attention.
Args:
feature_depth: int: depth of embedding
num_heads: int: number of attention heads
dropout: float: dropout rate - keep probability
mode: str: 'train' or 'eval'
Returns:
Multi-headed self-attention layer.
"""
return stax.serial(
stax.parallel(stax.Dense(feature_depth, W_init=xavier_uniform()),
stax.Dense(feature_depth, W_init=xavier_uniform()),
stax.Dense(feature_depth, W_init=xavier_uniform()),
stax.Identity),
PureMultiHeadedAttention(feature_depth,
num_heads=num_heads,
dropout=dropout,
mode=mode),
stax.Dense(feature_depth, W_init=xavier_uniform()),
)
def policy_and_value_net(rng_key,
batch_observations_shape,
num_actions,
bottom_layers=None):
"""A policy and value net function."""
# Layers.
layers = []
if bottom_layers is not None:
layers.extend(bottom_layers)
# Now, with the current logits, one head computes action probabilities and the
# other computes the value function.
layers.extend([stax.FanOut(2), stax.parallel(
stax.serial(stax.Dense(num_actions), stax.Softmax),
stax.Dense(1)
)])
net_init, net_apply = stax.serial(*layers)
_, net_params = net_init(rng_key, batch_observations_shape)
return net_params, net_apply
