def profile_java_net():
"""
Note: These times are super unreliable for some reason.. A given run can vary
by 7s-14s for example. God knows why.
Version 'old', Best:
Scores at Epoch 0.0: Test: 8.200
Scores at Epoch 1.0: Test: 57.100
Scores at Epoch 2.0: Test: 71.200
Elapsed time is: 7.866s
Version 'arr', Best:
Scores at Epoch 0.0: Test: 8.200
Scores at Epoch 1.0: Test: 58.200
Scores at Epoch 2.0: Test: 71.500
Elapsed time is: 261.1s
Version 'new', Best:
Scores at Epoch 0.0: Test: 8.200
Scores at Epoch 1.0: Test: 58.200
Scores at Epoch 2.0: Test: 71.500
Elapsed time is: 8.825s
:return:
"""
mnist = get_mnist_dataset(flat=True).shorten(1000).to_onehot()
with JPypeConnection():
spiking_net = JavaSpikingNetWrapper.from_init(
fractional = True,
depth_first=False,
smooth_grads = False,
back_discretize = 'noreset-herding',
w_init=0.01,
hold_error=True,
rng = 1234,
n_steps = 10,
eta=0.01,
layer_sizes=[784]+[200]+[10],
dtype = 'float'
)
with EZProfiler(print_result=True):
result = assess_online_predictor(
predictor = spiking_net,
dataset=mnist,
evaluation_function='percent_argmax_correct',
test_epochs=[0, 1, 2],
minibatch_size=1,
test_on='test',
)
def profile_java_net():
"""
Note: These times are super unreliable for some reason.. A given run can vary
by 7s-14s for example. God knows why.
Version 'old', Best:
Scores at Epoch 0.0: Test: 8.200
Scores at Epoch 1.0: Test: 57.100
Scores at Epoch 2.0: Test: 71.200
Elapsed time is: 7.866s
Version 'arr', Best:
Scores at Epoch 0.0: Test: 8.200
Scores at Epoch 1.0: Test: 58.200
Scores at Epoch 2.0: Test: 71.500
Elapsed time is: 261.1s
Version 'new', Best:
Scores at Epoch 0.0: Test: 8.200
Scores at Epoch 1.0: Test: 58.200
Scores at Epoch 2.0: Test: 71.500
Elapsed time is: 8.825s
:return:
"""
mnist = get_mnist_dataset(flat=True).shorten(1000).to_onehot()
with JPypeConnection():
spiking_net = JavaSpikingNetWrapper.from_init(
fractional=True,
depth_first=False,
smooth_grads=False,
back_discretize='noreset-herding',
w_init=0.01,
hold_error=True,
rng=1234,
n_steps=10,
eta=0.01,
layer_sizes=[784] + [200] + [10],
dtype='float')
with EZProfiler(print_result=True):
result = assess_online_predictor(
predictor=spiking_net,
dataset=mnist,
evaluation_function='percent_argmax_correct',
test_epochs=[0, 1, 2],
minibatch_size=1,
test_on='test',
)
def test_java_spiking_net_wrapper():
with JPypeConnection():
assert_online_predictor_not_broken(
predictor_constructor=lambda n_dim_in, n_dim_out:
JavaSpikingNetWrapper.from_init(
layer_sizes=[n_dim_in, 100, n_dim_out],
n_steps=10,
w_init=0.01,
rng=1234,
eta=0.01,
),
initial_score_under=50,
categorical_target=False,
minibatch_size=1,
n_epochs=2)
def test_java_spiking_net_wrapper():
with JPypeConnection():
assert_online_predictor_not_broken(
predictor_constructor = lambda n_dim_in, n_dim_out:
JavaSpikingNetWrapper.from_init(
layer_sizes = [n_dim_in, 100, n_dim_out],
n_steps = 10,
w_init = 0.01,
rng = 1234,
eta = 0.01,
),
initial_score_under=50,
categorical_target=False,
minibatch_size=1,
n_epochs=2
)
