def create_model():
from elektronn2 import neuromancer
in_sh = (20, 20 * 58)
inp = neuromancer.Input(in_sh, 'b,f', name='raw')
out = neuromancer.Perceptron(inp, 700, 'lin')
out = neuromancer.Perceptron(inp, 500, 'lin')
out = neuromancer.Perceptron(inp, 300, 'lin')
out = neuromancer.Perceptron(out, 2*58, 'lin')
out = neuromancer.Softmax(out, n_indep=58)
target = neuromancer.Input_like(out, override_f=1, name='target')
weights = neuromancer.ValueNode((116, ), 'f', value=[0.2, 1.8]*58)
loss = neuromancer.MultinoulliNLL(
out, target, target_is_sparse=True, class_weights=weights, name='nll'
)
# Objective
loss = neuromancer.AggregateLoss(loss)
# Monitoring / Debug outputs
errors = neuromancer.Errors(out, target, target_is_sparse=True)
model = neuromancer.model_manager.getmodel()
model.designate_nodes(
input_node=inp,
target_node=target,
loss_node=loss,
prediction_node=out,
prediction_ext=[loss, errors, out]
)
return model
def create_model():
from elektronn2 import neuromancer
act = 'relu'
in_sh = (batch_size, 1 if data_init_kwargs["raw_only"] else 4, nb_views,
int(x), int(y))
inp = neuromancer.Input(in_sh, 'b,f,z,y,x', name='raw')
out0 = neuromancer.Conv(inp,
13, (1, 5, 5), (1, 2, 2),
activation_func=act,
dropout_rate=dr)
out0 = neuromancer.Conv(out0,
19, (1, 5, 5), (1, 2, 2),
activation_func=act,
dropout_rate=dr)
out0 = neuromancer.Conv(out0,
25, (1, 4, 4), (1, 2, 2),
activation_func=act,
dropout_rate=dr)
out0 = neuromancer.Conv(out0,
25, (1, 4, 4), (1, 2, 2),
activation_func=act,
dropout_rate=dr)
out0 = neuromancer.Conv(out0,
30, (1, 2, 2), (1, 2, 2),
activation_func=act,
dropout_rate=dr)
out0 = neuromancer.Conv(out0,
30, (1, 1, 1), (1, 2, 2),
activation_func=act,
dropout_rate=dr)
out = neuromancer.Conv(out0,
31, (1, 1, 1), (1, 1, 1),
activation_func=act,
dropout_rate=dr)
out = neuromancer.Perceptron(out, 50, flatten=True, dropout_rate=dr)
out = neuromancer.Perceptron(out, 30, flatten=True, dropout_rate=dr)
out = neuromancer.Perceptron(out, 4, activation_func='lin')
out = neuromancer.Softmax(out)
target = neuromancer.Input_like(out, override_f=1, name='target')
weights = neuromancer.ValueNode((4, ), 'f', value=(1, 1, 2, 2))
loss = neuromancer.MultinoulliNLL(out,
target,
name='nll_',
target_is_sparse=True,
class_weights=weights)
# Objective
loss = neuromancer.AggregateLoss(loss)
# Monitoring / Debug outputs
errors = neuromancer.Errors(out, target, target_is_sparse=True)
model = neuromancer.model_manager.getmodel()
model.designate_nodes(input_node=inp,
target_node=target,
loss_node=loss,
prediction_node=out,
prediction_ext=[loss, errors, out])
return model
def create_model():
from elektronn2 import neuromancer
in_sh = (40, 40, 58)
inp = neuromancer.Input(in_sh, 'r,b,f', name='raw')
inp0, _ = neuromancer.split(inp, 'r', index=1, strip_singleton_dims=True)
inp_mem = neuromancer.InitialState_like(inp0,
override_f=750,
init_kwargs={
'mode': 'fix-uni',
'scale': 0.1
})
out = neuromancer.GRU(inp0, inp_mem, 750)
out = neuromancer.Scan(out,
inp_mem,
in_iterate=inp,
in_iterate_0=inp0,
n_steps=40,
last_only=True)
out = neuromancer.Perceptron(out, 2 * 58, 'lin')
out = neuromancer.Softmax(out, n_indep=58)
target = neuromancer.Input_like(out, override_f=1, name='target')
weights = neuromancer.ValueNode((116, ), 'f', value=(0.2, 1.8))
loss = neuromancer.MultinoulliNLL(out,
target,
name='nll',
target_is_sparse=True,
class_weights=weights)
# Objective
loss = neuromancer.AggregateLoss(loss)
# Monitoring / Debug outputs
errors = neuromancer.Errors(out, target, target_is_sparse=True)
model = neuromancer.model_manager.getmodel()
model.designate_nodes(input_node=inp,
target_node=target,
loss_node=loss,
prediction_node=out,
prediction_ext=[loss, errors, out])
return model