def build_lstsqclassifier(model, solver, conn, rng, transform):
from nengo.builder.connection import multiply
from nengo_ocl.builder.solvers import (get_solve_params, wrap_solver,
solve_for_decoders)
eval_points, neuron_type, gain, bias, X, Y, E = get_solve_params(
model, solver, conn, rng, transform)
# sort eval points by category
assert Y.ndim == 2
Yi = np.argmax(Y, axis=1)
i = np.argsort(Yi)
X[:] = X[i]
Y[:] = Y[i]
wrapped_solver = wrap_solver(model, conn, solve_for_decoders)
decoders, solver_info = wrapped_solver(solver,
neuron_type,
gain,
bias,
X,
Y,
rng,
E=E,
conn=conn,
queue=model.builder.queue)
weights = (decoders.T if solver.weights else multiply(
transform, decoders.T))
return eval_points, weights, solver_info
def build_decoders(model, conn, rng):
# Copied from older version of Nengo
encoders = model.params[conn.pre_obj].encoders
gain = model.params[conn.pre_obj].gain
bias = model.params[conn.pre_obj].bias
eval_points = connection_b.get_eval_points(model, conn, rng)
targets = connection_b.get_targets(model, conn, eval_points)
x = np.dot(eval_points, encoders.T / conn.pre_obj.radius)
E = None
if conn.solver.weights:
E = model.params[conn.post_obj].scaled_encoders.T[conn.post_slice]
# include transform in solved weights
targets = connection_b.multiply(targets, conn.transform.T)
try:
wrapped_solver = model.decoder_cache.wrap_solver(
connection_b.solve_for_decoders)
decoders, solver_info = wrapped_solver(conn.solver,
conn.pre_obj.neuron_type,
gain,
bias,
x,
targets,
rng=rng,
E=E)
except BuildError:
raise BuildError(
"Building %s: 'activities' matrix is all zero for %s. "
"This is because no evaluation points fall in the firing "
"ranges of any neurons." % (conn, conn.pre_obj))
return eval_points, decoders, solver_info
def build_decoders(model, conn, rng):
# Copied from older version of Nengo
encoders = model.params[conn.pre_obj].encoders
gain = model.params[conn.pre_obj].gain
bias = model.params[conn.pre_obj].bias
eval_points = connection_b.get_eval_points(model, conn, rng)
try:
targets = connection_b.get_targets(conn, eval_points)
except:
# nengo <= 2.3.0
targets = connection_b.get_targets(model, conn, eval_points)
x = np.dot(eval_points, encoders.T / conn.pre_obj.radius)
E = None
if conn.solver.weights:
E = model.params[conn.post_obj].scaled_encoders.T[conn.post_slice]
# include transform in solved weights
targets = connection_b.multiply(targets, conn.transform.T)
try:
wrapped_solver = model.decoder_cache.wrap_solver(
connection_b.solve_for_decoders
)
try:
decoders, solver_info = wrapped_solver(
conn, gain, bias, x, targets,
rng=rng, E=E)
except TypeError:
# fallback for older nengo versions
decoders, solver_info = wrapped_solver(
conn.solver, conn.pre_obj.neuron_type, gain, bias, x, targets,
rng=rng, E=E)
except BuildError:
raise BuildError(
"Building %s: 'activities' matrix is all zero for %s. "
"This is because no evaluation points fall in the firing "
"ranges of any neurons." % (conn, conn.pre_obj))
return eval_points, decoders, solver_info
def build_connection(model, conn):
if nengo_transforms is not None:
if isinstance(conn.transform, nengo_transforms.Convolution):
# TODO: integrate these into the same function
conv.build_conv2d_connection(model, conn)
return
elif not isinstance(conn.transform, nengo_transforms.Dense):
raise NotImplementedError(
"nengo-loihi does not yet support %s transforms"
% conn.transform)
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
pre_cx = model.objs[conn.pre_obj]['out']
post_cx = model.objs[conn.post_obj]['in']
assert isinstance(pre_cx, (LoihiBlock, LoihiInput))
assert isinstance(post_cx, (LoihiBlock, Probe))
weights = None
eval_points = None
solver_info = None
neuron_type = None
post_slice = conn.post_slice
# sample transform (if using a distribution)
transform = sample_transform(conn, rng=rng)
tau_s = 0.0 # `synapse is None` gets mapped to `tau_s = 0.0`
if isinstance(conn.synapse, nengo.synapses.Lowpass):
tau_s = conn.synapse.tau
elif conn.synapse is not None:
raise NotImplementedError("Cannot handle non-Lowpass synapses")
needs_decode_neurons = False
target_encoders = None
if isinstance(conn.pre_obj, Node):
assert conn.pre_slice == slice(None)
if np.array_equal(transform, np.array(1.)):
# TODO: this identity transform may be avoidable
transform = np.eye(conn.pre.size_out)
else:
assert transform.ndim == 2, "transform shape not handled yet"
assert transform.shape[1] == conn.pre.size_out
assert transform.shape[1] == conn.pre.size_out
if isinstance(conn.pre_obj, ChipReceiveNeurons):
weights = transform / model.dt
neuron_type = conn.pre_obj.neuron_type
else:
# input is on-off neuron encoded, so double/flip transform
weights = np.column_stack([transform, -transform])
target_encoders = 'node_encoders'
elif (isinstance(conn.pre_obj, Ensemble)
and isinstance(conn.pre_obj.neuron_type, nengo.Direct)):
raise NotImplementedError()
elif isinstance(conn.pre_obj, Ensemble): # Normal decoded connection
eval_points, decoders, solver_info = model.build(
conn.solver, conn, rng, transform)
if conn.solver.weights and not conn.solver.compositional:
weights = decoders
else:
weights = multiply(transform, decoders)
# the decoder solver assumes a spike height of 1/dt; that isn't the
# case on loihi, so we need to undo that scaling
weights = weights / model.dt
neuron_type = conn.pre_obj.neuron_type
if conn.solver.weights:
# weight solvers only allowed on ensemble->ensemble connections
assert isinstance(conn.post_obj, Ensemble)
if conn.solver.compositional:
encoders = model.params[conn.post_obj].scaled_encoders.T
encoders = encoders[post_slice]
weights = multiply(encoders.T, weights)
# post slice already applied to encoders (either here or in
# `build_decoders`), so don't apply later
post_slice = None
else:
needs_decode_neurons = True
elif isinstance(conn.pre_obj, Neurons):
assert conn.pre_slice == slice(None)
assert transform.ndim == 2, "transform shape not handled yet"
weights = transform / model.dt
neuron_type = conn.pre_obj.ensemble.neuron_type
else:
raise NotImplementedError("Connection from type %r" % (
type(conn.pre_obj),))
if neuron_type is not None and hasattr(neuron_type, 'amplitude'):
weights = weights * neuron_type.amplitude
mid_cx = pre_cx
mid_axon_inds = None
post_tau = tau_s
if needs_decode_neurons and not isinstance(conn.post_obj, Neurons):
# --- add decode neurons
assert weights.ndim == 2
d, n = weights.shape
if isinstance(post_cx, Probe):
# use non-spiking decode neurons for voltage probing
assert post_cx.target is None
assert post_slice == slice(None)
# use the same scaling as the ensemble does, to get good
# decodes. Note that this assumes that the decoded value
# is in the range -radius to radius, which is usually true.
weights = weights / conn.pre_obj.radius
gain = 1
dec_cx = LoihiBlock(2 * d, label='%s' % conn)
dec_cx.compartment.configure_nonspiking(
dt=model.dt, vth=model.vth_nonspiking)
dec_cx.compartment.bias[:] = 0
model.add_block(dec_cx)
model.objs[conn]['decoded'] = dec_cx
dec_syn = Synapse(n, label="probe_decoders")
weights2 = gain * np.vstack([weights, -weights]).T
dec_syn.set_full_weights(weights2)
dec_cx.add_synapse(dec_syn)
model.objs[conn]['decoders'] = dec_syn
else:
# use spiking decode neurons for on-chip connection
if isinstance(conn.post_obj, Ensemble):
# loihi encoders don't include radius, so handle scaling here
weights = weights / conn.post_obj.radius
post_d = conn.post_obj.size_in
post_inds = np.arange(post_d, dtype=np.int32)[post_slice]
assert weights.shape[0] == len(post_inds) == conn.size_out == d
mid_axon_inds = model.decode_neurons.get_post_inds(
post_inds, post_d)
target_encoders = 'decode_neuron_encoders'
dec_cx, dec_syn = model.decode_neurons.get_block(
weights, block_label="%s" % conn, syn_label="decoders")
model.add_block(dec_cx)
model.objs[conn]['decoded'] = dec_cx
model.objs[conn]['decoders'] = dec_syn
# use tau_s for filter into decode neurons, decode_tau for filter out
dec_cx.compartment.configure_filter(tau_s, dt=model.dt)
post_tau = model.decode_tau
dec_ax0 = Axon(n, label="decoders")
dec_ax0.target = dec_syn
pre_cx.add_axon(dec_ax0)
model.objs[conn]['decode_axon'] = dec_ax0
if conn.learning_rule_type is not None:
rule_type = conn.learning_rule_type
if isinstance(rule_type, nengo.PES):
if not isinstance(rule_type.pre_synapse,
nengo.synapses.Lowpass):
raise ValidationError(
"Loihi only supports `Lowpass` pre-synapses for "
"learning rules", attr='pre_synapse', obj=rule_type)
tracing_tau = rule_type.pre_synapse.tau / model.dt
# Nengo builder scales PES learning rate by `dt / n_neurons`
n_neurons = (conn.pre_obj.n_neurons
if isinstance(conn.pre_obj, Ensemble)
else conn.pre_obj.size_in)
learning_rate = rule_type.learning_rate * model.dt / n_neurons
# Account for scaling to put integer error in range [-127, 127]
learning_rate /= model.pes_error_scale
# Tracing mag set so that the magnitude of the pre trace
# is independent of the pre tau. `dt` factor accounts for
# Nengo's `dt` spike scaling. Where is the second `dt` from?
# Maybe the fact that post decode neurons have `vth = 1/dt`?
tracing_mag = -np.expm1(-1. / tracing_tau) / model.dt**2
# learning weight exponent controls the maximum weight
# magnitude/weight resolution
wgt_exp = model.pes_wgt_exp
dec_syn.set_learning(
learning_rate=learning_rate,
tracing_mag=tracing_mag,
tracing_tau=tracing_tau,
wgt_exp=wgt_exp,
)
else:
raise NotImplementedError()
mid_cx = dec_cx
if isinstance(post_cx, Probe):
assert post_cx.target is None
assert post_slice == slice(None)
post_cx.target = mid_cx
mid_cx.add_probe(post_cx)
elif isinstance(conn.post_obj, Neurons):
assert isinstance(post_cx, LoihiBlock)
assert post_slice == slice(None)
if weights is None:
raise NotImplementedError("Need weights for connection to neurons")
else:
assert weights.ndim == 2
n2, n1 = weights.shape
assert post_cx.n_neurons == n2
syn = Synapse(n1, label="neuron_weights")
gain = model.params[conn.post_obj.ensemble].gain
syn.set_full_weights(weights.T * gain)
post_cx.add_synapse(syn)
model.objs[conn]['weights'] = syn
ax = Axon(mid_cx.n_neurons, label="neuron_weights")
ax.target = syn
mid_cx.add_axon(ax)
post_cx.compartment.configure_filter(post_tau, dt=model.dt)
if conn.learning_rule_type is not None:
raise NotImplementedError()
elif isinstance(conn.post_obj, Ensemble) and conn.solver.weights:
assert isinstance(post_cx, LoihiBlock)
assert weights.ndim == 2
n2, n1 = weights.shape
assert post_cx.n_neurons == n2
# loihi encoders don't include radius, so handle scaling here
weights = weights / conn.post_obj.radius
syn = Synapse(n1, label="%s::decoder_weights" % conn)
syn.set_full_weights(weights.T)
post_cx.add_synapse(syn)
model.objs[conn]['weights'] = syn
ax = Axon(n1, label="decoder_weights")
ax.target = syn
mid_cx.add_axon(ax)
post_cx.compartment.configure_filter(post_tau, dt=model.dt)
if conn.learning_rule_type is not None:
raise NotImplementedError()
elif isinstance(conn.post_obj, Ensemble):
assert target_encoders is not None
if target_encoders not in post_cx.named_synapses:
build_decode_neuron_encoders(
model, conn.post_obj, kind=target_encoders)
mid_ax = Axon(mid_cx.n_neurons, label="encoders")
mid_ax.target = post_cx.named_synapses[target_encoders]
mid_ax.set_axon_map(mid_axon_inds)
mid_cx.add_axon(mid_ax)
model.objs[conn]['mid_axon'] = mid_ax
post_cx.compartment.configure_filter(post_tau, dt=model.dt)
else:
# This includes Node, since nodes can't be targets on-chip
raise NotImplementedError()
model.params[conn] = BuiltConnection(
eval_points=eval_points,
solver_info=solver_info,
transform=transform,
weights=weights)